Interpreting Verbal Irony: Linguistic Strategies and the Connection to theType of Semantic Incongruity

Debanjan Ghosh⇤1, Elena Musi2, Kartikeya Upasani3, Smaranda Muresan4

1McGovern Institute for Brain Research, MIT, Cambridge, MA2University of Liverpool, Liverpool, UK

3Facebook Conversational AI, CA4 Data Science Institute, Columbia University, New York, NYdg513@mit.edu, elena.musi@liverpool.ac.uk,

kart@fb.com, smara@columbia.edu

AbstractHuman communication often involves the useof verbal irony or sarcasm, where the speakersusually mean the opposite of what they say.To better understand how verbal irony is ex-pressed by the speaker and interpreted by thehearer we conduct a crowdsourcing task: givenan utterance expressing verbal irony, users areasked to verbalize their interpretation of thespeaker’s ironic message. We propose a ty-pology of linguistic strategies for verbal ironyinterpretation and link it to various theoreti-cal linguistic frameworks. We design com-putational models to capture these strategiesand present empirical studies aimed to answerthree questions: (1) what is the distributionof linguistic strategies used by hearers to in-terpret ironic messages?; (2) do hearers adoptsimilar strategies for interpreting the speaker’sironic intent?; and (3) does the type of seman-tic incongruity in the ironic message (explicitvs. implicit) influence the choice of interpreta-tion strategies by the hearers?

1 Introduction

It is well understood that recognizing whether aspeaker is ironic or sarcastic is essential to under-standing their actual sentiments and beliefs. Forinstance, the utterance “pictures of holding ani-mal carcasses are so flattering” is an expressionof verbal irony, where the speaker has a nega-tive sentiment towards “pictures of holding animalcarcasses”, but uses the positive sentiment word“flattering”. This inherent characteristic of ver-bal irony is called semantic incongruity — incon-gruity between the literal evaluation and the con-text (e.g., between the positive sentiment wordsand the negative situation in this example). MostNLP research on verbal irony or sarcasm has fo-cused on the task of sarcasm detection treating

⇤Part of the research was carried out while Debanjan wasa Ph.D. candidate at Rutgers University.

it as a binary classification task using either theutterance in isolation or adding contextual infor-mation such as conversation context, author con-text, visual context, or cognitive features (Davi-dov et al., 2010; Maynard and Greenwood, 2014;Wallace et al., 2014; Joshi et al., 2015; Bammanand Smith, 2015; Muresan et al., 2016; Amir et al.,2016; Mishra et al., 2016; Ghosh and Veale, 2017;Felbo et al., 2017; Ghosh et al., 2017; Hazarikaet al., 2018; Tay et al., 2018; Ghosh et al., 2018;Oprea and Magdy, 2019). Such approaches havefocused their analysis on the speakers’ beliefs andintentions for using irony (Attardo, 2000). How-ever, sarcasm and verbal irony are types of inter-actional phenomena with specific perlocutionaryeffects on the hearer (Haverkate, 1990). Thus, weargue that, besides recognizing the speaker’s sar-castic/ironic intent, it is equally important to un-derstand how the hearer interprets the speaker’ssarcastic/ironic message. For the above utterance,the strength of negative sentiment perceived bythe hearer depends on whether they interpret thespeaker’s actual meaning as “picture . . . are notflattering” vs. “pictures . . . are so gross” (Table1). The intensity of negative sentiment is higher inthe latter interpretation than in the former. Kreuz(2000) noted that most studies in linguistics andpsychology have conducted experiments analyz-ing reaction times (Gibbs, 1986; Katz et al., 2004)or situational context (Ivanko and Pexman, 2003),featuring a setup with in vitro data aimed at testingthe validity of specific theories of irony. Instead,our study adopts a naturalistic approach to under-stand hearers’ reception of irony looking at whatlinguistic strategies are recurrently used by hear-ers to interpret the non-literal meaning underlyingironic utterances.

We leverage the crowdsourcing task introducedby Ghosh et al. (2015) for their work on detect-ing whether a word has a literal or sarcastic in-

terpretation, later adopted by Peled and Reichart(2017). The task is framed as follows: givena speaker’s ironic message, five annotators (e.g.,Turkers on Amazon Mechanical Turk (MTurk))are asked to verbalize their interpretation of thespeaker’s ironic message (i.e., their understand-ing of the speaker’s intended meaning) (see Ta-ble 1; Sim denotes the speaker’s ironic message,while Hint denotes the hearer’s interpretation ofthat ironic message). The crowdsourcing experi-ments are reported in Section 2.

The paper makes three contributions. First, wepropose a data-driven typology of linguistic strate-gies that hearers use to interpret ironic messagesand discuss its relevance in verifying theoreticalframeworks of irony (Section 4). Second, we pro-pose computational models to capture these strate-gies (Section 5). Third, we present two studiesthat aim to answer two questions: (1) does thetype of semantic incongruity in the ironic mes-sage (explicit vs. implicit; see Section 3) influ-ence the choice of interpretation strategies by thehearers? (Section 6.2); (2) do interpretation strate-gies of verbal irony vary by hearers? We make alldatasets and code available.1

2 Datasets of Speakers’ Ironic Messagesand Hearers’ Interpretations

To generate a parallel dataset of speakers’ ironicmessages and hearers’ interpretations we conducta crowdsourcing experiment. Given a speaker’sironic message (Sim), five Turkers (hearers) onMTurk are asked to verbalize their interpretationof the speaker’s ironic message (i.e., their un-derstanding of the speaker’s intended meaning)(Hint). The design of the MTurk task was first in-troduced by Ghosh et al. (2015), who use the re-sulting dataset to identify words that can have botha literal and a sarcastic sense. Peled and Reichart(2017) employed similar design to generate a par-allel dataset to use for generating interpretations ofsarcastic messages using machine translation ap-proaches. They use skilled annotators in comedywriting and literature paraphrasing and give themthe option not to rephrase (we refer to Peled andReichart (2017)’s dataset as SIGN ). We performthis new crowdsourcing task and do not rely en-tirely on the above two datasets for two reasons:(1) we focus on verbal irony, and (2) we alwaysrequire an interpretation from the Turkers. Un-

1https://github.com/debanjanghosh/interpreting verbal irony

like the above two studies, the main goal of ourresearch is to analyze the linguistics strategies em-ployed by hearers in interpreting verbal irony.

We collected messages that express verbal ironyfrom Twitter using the hashtags #irony, #sarcas-tic, and #sarcasm. We chose Twitter as a sourcesince the presence of the hashtags allows us to se-lect sentences where the speaker’s intention wasto be ironic. Furthermore, even though Twitterusers cannot be considered representative of theentire population, they are unlikely to be skewedwith respect to topics or gender. We manuallychecked and kept 1,000 tweets that express verbalirony. We do not draw any theoretical distinctionbetween sarcasm and irony since we cannot as-sume that Twitter users also differentiate between#irony and #sarcasm, blurred even in scholarly lit-erature. The Turkers were provided with detailedinstructions and examples of the task including thestandard definition of verbal irony taken from theMerriam-Webster dictionary (“use of words to ex-press something other than and especially the op-posite of the literal meaning”). We decided tosuggest them a guiding definition for two reasons.First, hearers do not usually focus on literal vs.non literal meaning, as shown by studies measur-ing processing times for both types of statements(Inhoff et al., 1984). Therefore, when asked torephrase the speakers’ intended meaning, hearerswould have probably come up with sentences ex-pressing the speaker’s imagined discursive goals,rather than disclosing their perceived literal mean-ing. Second, it is reasonable to assume that Turk-ers would have looked up the standard meaning ofironic utterance given by an online dictionary toease up their task, possibly coming up with biaseddefinitions.

The Turkers were instructed to consider the en-tire message in their verbalization to avoid asym-metry in length between the Sim and Hint. Weobtained a dataset of 5,000 Sim-Hint pairs wherefive Turkers rephrase each Sim. A total of 184Turkers participated in the rephrasing task. Ta-ble 1 shows examples of speaker’s ironic messages(Sim) and their corresponding hearers’ interpreta-tions (Hi

int). Next, we ran a second MTurk task

to verify whether the generated Hint messages areplausible interpretations of the ironic messages.This time we employ three Turkers per task andonly Turkers who were not involved in the con-tent generation task were allowed to perform this

Sim H1int H2

int H3int

1. Ed Davey is such a pas-sionate, inspiring speaker

Ed Davey is a boring, unin-spiring speaker

Ed Davey is such a dull,monotonous speaker

Ed Davey is not a passion-ate, inspiring speaker

2. can’t believe how muchcaptain America looks likeme

I wish I looked like Cap-tain America. I need to loseweights

can’t believe how muchcaptain America looks dif-ferent from me

I don’t, but I wish I lookedlike Captain America

3. Pictures of you holdingdead animal carcasses areso flattering

Hate hunting season andthe pictures of you holdingdead animal are so gross

Pictures of you holdingdead animal carcasses is anunflattering look

Pictures of you holdingdead animal carcasses arenot flattering

Table 1: Examples of speaker’s ironic messages (Sim) and interpretations given by 3 Turkers (Hiint).

task. We observe that Turkers labeled 5% (i.e., 238verbalizations) of Hints as invalid and low qual-ity (e.g., wrong interpretation). For both tasks, weallowed only qualified Turkers (i.e., at least 95%approval rate and 5,000 approved HITs), paid 7cents/task and gave sixty minutes to complete eachtask. The final dataset contains 4,762 pairs Sim-Hint.

3 Semantic Incongruity in IronicMessages: Explicit vs. Implicit

Attardo (2000) and later Burgers (2010) distin-guish between two theoretical aspects of irony:irony markers and irony factors. Irony markers aremeta-communicative signals, such as interjectionsor emoticons that alert the reader that an utterancemight be ironic. In contrast, irony factors cannotbe removed without destroying the irony, such asthe incongruity between the literal evaluation andits context (“semantic incongruity”). Incongruityexpresses the contrast between the conveyed senti-ment (usually, positive) and the targeted situation(usually, negative). This contrast can be explicitlyor implicitly expressed in the ironic message.

Following Karoui et al. (2017), we considerthat semantic incongruity is explicit, when itis lexicalized in the utterance itself (e.g., boththe positive sentiment word(s) and the negativesituation are available to the reader explicitly).On Twitter, beside sentiment words, users oftenmake use of hashtags (e.g., “Studying 5 subjects. . . #worstsaturdaynight”) or an image (e.g., “En-couraging how Police feel they’re above the law.URL”; the URL shows a police car not payingparking) to express their sentiment. We considerthese cases as explicit, since the incongruity ispresent in the utterance even if via hashtags orother media. For implicit incongruity, we con-sider cases where one of the two incongruent terms(“propositions” in Karoui et al. (2017)) is not lex-icalized and has to be reconstructed from the con-

text (either outside word knowledge or a largerconversational context). For example “You aresuch a nice friend!!!”, or “Driving in Detroit is fun;)” are cases of ironic messages where the seman-tic incongruity is implicit. Based on these def-initions of explicit and implicit incongruity, twoexpert annotators annotated the Sim-Hint dataset(1000 ironic messages) as containing explicit orimplicit semantic incongruity. The inter-annotatoragreement was =0.7, which denotes good agree-ment similar to Karoui et al. (2017). The annota-tion showed that 38.7% of the ironic messages areexplicit, while 61.3% are implicit. In the followingsection we propose a typology of linguistic strate-gies used in hearers’ interpretations of speakers’ironic messages and in section 6.2 we discuss thecorrelation of linguistic strategies with the type ofsemantic incongruity.

4 Interpreting Verbal Irony: A Typologyof Linguistic Strategies

Given the definition of verbal irony, we wouldexpect that Turkers’ interpretation of speaker’sironic message will contain some degree of op-posite meaning with respect to what the speakerhas said. However, it is unclear what linguisticstrategies the Turkers will use to express that. Tobuild our typology, from the total set of Sim-Hint

pairs obtained through crowdsourcing (i.e., 4,762pairs; see Section 2) we selected a dev set of 500Sim-Hint pairs. Our approach does not assume anyspecific theory or irony, but it is data-driven: a lin-guist expert in semantics and pragmatics analyzedthe dev set to formulate the lexical and pragmaticphenomena attested in the data. The assembledtypology is, thus, the result of a bottom-up proce-dure. A Sim-Hint pair can be annotated with morethan one strategy. The core linguistic strategies areexplained below and synthesized in Table 2.

Typology Distribution (%)Antonyms- lexical antonyms (42.2)- antonym phrases (6.0)Negation- simple negation (28.4)Antonyms OR Negation- weakening sentiment (23.2)- interrogative ! declarative (5.2)- desiderative constructions (2.8)Pragmatic inference (3.2)

Table 2: Typology of linguistic strategies and theirdistribution (in %) over the dev set

4.1 Linguistic StrategiesLexical and phrasal antonyms: This categorycontains lexical antonyms (e.g., “love” $ “hate”,“great” $ “terrible”) as well as indirect antonyms(Fellbaum, 1998), where the opposite meaning canonly be interpreted in context (e.g., “passionatespeaker” ! “boring speaker”; Table 1). Althoughthe typical antonym of “passionate” is “unpassion-ate”, “boring” works in this context as a lexicalopposite since a speaker who is passionate entailsthat he is not boring. Besides lexical antonyms,Turkers sometimes use antonym phrases (e.g., “Ican’t wait” ! “not looking forward”, “I like (tovisit ER)” ! “I am upset (to visit ER) ”).

Negation: Here, Turkers negate the main pred-icate. This strategy is used in the presence ofcopulative constructions where the predicative ex-pression is an adjective/noun expressing sentiment(e.g., “is great” ! “is not great”) and of verbs ex-pressing sentiment (e.g., “love” ! “do not love”)or propositional attitudes (e.g., “I wonder” ! “Idon’t wonder”).

Weakening the intensity of sentiment: The useof negation and antonyms is sometimes accom-panied by two strategies that reflect a weakeningof sentiment intensity. First, when Sim containswords expressing a high degree of positive sen-timent, the hearer’s interpretation replaces themwith more neutral ones (e.g., “I love it” ! “I don’tlike it”). Second, when Sim contains an intensi-fier, it is eliminated in the Turkers’ interpretation.Intensifiers specify the degree of value/quality ex-pressed by the words they modify (Mendez-Naya,2008) (e.g., “cake for breakfast. so healthy” !“cake for breakfast. not healthy”).

Interrogative to Declarative Transformation(+ Antonym/Negation): This strategy, used

mostly in conjunction with the negation orantonym strategies, consists in replacing the inter-rogative form with a declarative form, when Sim isa rhetorical question (for brevity, RQ) (e.g., “don’tyou love fighting?” ! “I hate fighting”).

Counterfactual Desiderative Constructions:When the ironic utterance expresses a posi-tive/negative sentiment towards a past event (e.g.,“glad you relayed this news”) or an expressivespeech act (e.g., “thanks X that picture neededmore copy”) the hearer’s interpretation of intendedmeaning is expressed through the counterfactualdesiderative constructions I wish (that) p (“I wishyou hadn’t relayed . . . ”, “I wish X didn’t copy. . . ”). Differently from antonymic phrases, thisstrategy stresses on the failure of the speaker’sexpectation more than on their commitment to theopposite meaning.

Pragmatic Inference: In addition to the abovestrategies, there are cases where the interpretationcalls for an inferential process to be recognized.For instance, “made 174 this month . . . I’m gonnabuy a yacht!” ! “made 174 this month . . . I amso poor”. The distribution of the strategies on thedev set is represented in Table 2.

4.2 Links to Theoretical FrameworksIn linguistic literature many different approachesto irony have been provided. Here we focus onthe three accounts (w.r.t. examples from Sim-Hint

corpus) that bear a different views on pragmaticfactors. According to Grice (1975), ironic mes-sages are uttered to convey a meaning oppositeto that literally expressed, flouting the conversa-tional maxim of quality “do not say what you be-lieve to be false”. In verbal irony, the violationof the maxim is frequently signaled by “the oppo-site” of what is said literally (e.g., intended mean-ing of “carcasses are flattering” is they are gross;Table 1). The linguistic strategies of antonyms(e.g. “worst day of my life”) and simple nega-tion (“yeap we totally dont drink alcohol everysingle day”[...]) cover the majority of the Sim-Hint corpus and seem to fit the Gricean (Grice,1975) account of irony, since the hearer seemsto have primarily recognized the presence of se-mantic incongruity. However, as touched uponby Giora (1995), antonyms and direct negationare not always semantically equivalent strategies,since the second sometimes allows a graded inter-pretation: if “x is not encouraging”, it is not nec-

essarily bad, but simply “x < encouraging”. Suchan implicature is available exclusively with itemsallowing mediated contraries, such as sentimentwords (Horn, 1989). Direct negation with senti-ment words implies that just one value in a set isnegated, while the others are potentially affirmed.The spectrum of interpretations allowed by nega-tion as a rephrasing strategy indicates that hearersrecognize that the relevance of the ironic utterancein itself plays a role next to what the utterancesrefers to (if the rephrased utterance is intendedas “x is not encouraging at all”, the perceived ir-relevance of the corresponding ironic utterance ismore prominent than in “x is not very encourag-ing”). The fact that the interpretation of irony has apropositional scope is even clearer when the ironicsentence in interrogative form (“and they all livedhappily ever after ?”) is rephrased as a declara-tive (e.g. “I doubt they all lived happily ever af-ter”): the hearers recognizes that the question hasa rhetoric value since otherwise contextually irrel-evant. The intentional falsehood of Gricean anal-ysis is also not deemed by Sperber and Wilson(1986); Wilson and Sperber (2012) as a necessaryand sufficient condition for irony. According totheir theory of echoic mentioning, irony presup-poses the mention to the inappropriateness of theentire sentence: in asserting “awesome weather inScotland today” the speaker does not simply wantto express that the weather was horrible but hesignals that assuming that the weather would benice was irrelevant and, thus, ridiculous. Kreuzand Glucksberg (1989) expand the Relevance The-ory approach talking about echoic reminding toaccount for cases such as “could you be just alittle louder, please? My baby isn’t trying tosleep” where the extreme politeness reminds thehearer that the question is indeed a request andthat the mother bears a certain stance and has cer-tain expectations towards the addressee. Simi-larly, the use of the pragmatic inference strategycannot be fully explained in Gricean terms: therephrase “made 174 this month . . . I am so poor”for “made 174 this month . . . I am gonna buy ayatch” more than pointing to the presence of lex-ical incongruity, show that the hearers knows forbackground knowledge that the assertion of “buy-ing a yatch” is completely irrelevant in the con-text of a low salary situation. Rephrasing strate-gies using counterfactual desiderative construc-tions (e.g. “I really wish my friends and fam-

ily would check up on my after yesterday’s neardeath experience”) show, instead, that the interpre-tation of irony involves an echoic reminding to thespeaker’s (social) expectations which failed to befulfilled. Overall, using the results of our crowd-sourcing experiment with main existing theoriesof irony, it turns out that the theories have a com-plementary explanatory power. In Section 6.2 weinvestigate weather this situation might relate tothe presence of explicit/implicit irony.

5 Empirical Analysis of InterpretationStrategies

Here our goal is to perform a comparative em-pirical analysis to understand how hearers inter-pret verbal irony. To accomplish this, we pro-pose computational models to automatically de-tect these linguistic strategies in two datasets: (1)Sim -Hint dataset and (2) the SIGN dataset. Asstated in Section 2, albeit for a different purpose,the task designed in Peled and Reichart (2017) isidentical to ours: they used a set of 3,000 sarcas-tic tweets and collected five interpretation verbal-ization, including an option to just copy the orig-inal message if it was not deemed ironic. Theyused workers skilled in comedy writing and liter-ature paraphrasing. SIGN contains 14,970 pairs.To evaluate our models, we asked two annotatorsto annotate two test sets of 500 pairs each fromthe Sim -Hint and the SIGN dataset (i.e., denotedby SIGNtest), respectively. Note, the test set forthe Sim -Hint has no overlap with the dev set of500 Sim-Hint pairs used to identify the strategies(Section 4). Agreement between the annotators forboth sets is high with > 0.9. In SIGNtest, 79instances were just copies of the original message,which we eliminated, thus the SIGNtest containsonly 421 instances.

5.1 Computational Methods

Lexical Antonyms. To detect whether an Sim-Hint pair uses the lexical antonyms strategy, wefirst need to build a resource of lexical antonyms.We use the MPQA sentiment Lexicon (Wilsonet al., 2005), Hu and Liu (2004)’s opinion lexi-con, antonym pairs from Mohammad et al. (2013),antonyms from WordNet, and pairs of oppo-site verbs from Verbocean (Chklovski and Pantel,2004).

Given this lexicon of lexical antonyms, the taskis now to detect whether a given Sim-Hint pair

dev test SIGNtest

Strategies P R F1 P R F1 P R F1Lex ant 89.0 95.7 92.2 97.2 89.9 93.4 89.4 97.9 93.5Simple neg 92.0 89.4 90.7 88.3 88.3 88.3 93.3 91.2 92.2AN weaksent 93.6 87.9 90.7 95.0 91.9 93.4 93.3 87.5 90.3ANI!D 53.1 65.4 58.6 80.0 0.44 57.2 85.7 70.6 77.4AN desiderative 100.0 92.9 96.3 100.0 100.0 100.0 100.0 66.7 80.0AntPhrase+PragInf 86.2 53.2 65.8 70.7 85.3 77.4 89.5 68.0 77.3

Table 3: Evaluation of Computational Methods on dev, test and SIGNtest set (in %)

uses the lexical antonyms strategy. We use aheuristic approach based on word-alignment anddependency parsing (similar to contradiction de-tection (De Marneffe et al., 2008)). Word-to-wordalignments between Sim-Hint are extracted usinga statistical machine translation (SMT) alignmentmethod - IBM Model 4 with HMM alignmentfrom Giza++ (Och and Ney, 2004). We considera lexical antonym strategy if: 1) antonym wordsare aligned; 2) they are the roots of the respec-tive dependency trees or if the nodes modifiedby the lexical antonyms are the same in their re-spective trees (e.g., ‘can you show any more ofsteelers” ! “show less of steelers”, the candi-date lexical antonyms are more and less and theyare the objects of the same predicate in Sim-Hint:show). Out of 211 Sim-Hint pairs that are markedas having lexical antonym strategy (dev set), 12instances are identified by only the dependencyparses, 67 instances by the word-alignments, and100 instances by both (P/R/F1 scores are 92.1%,77.7% and 84.3%), respectively on dev dataset.However, sometimes both dependency and word-alignment methods fail. In “circling down thebowl. Yay” ! “circling down the bowl. aw-ful”, although the lexical antonyms yay and aw-ful exist, neither the alignment nor the dependencytrees can detect it (25 such instances in the devset). To account for this, after having run thedependency and alignment methods, we also justsearch whether a Sim-Hint pair contains a lexicalantonym pair. This improves the final recall and onthe dev set we achieve 89.0% precision, 95.7% re-call, and 92.2% F1 on dev dataset (Lex ant Strat-egy; Table 3 show results both on dev and thetest sets). Note, just searching whether a lexicalantonym pair is present in a Sim-Hint pair resultsin low precision (58.6%) but high recall (80%).

Simple negation. This strategy (denoted asSimple neg in Table 3 and Table 4) involves iden-tifying the presence of negation and its scope.Here, however, the scope of negation is con-

strained since generally Turkers negated only asingle word (i.e., “love” ! “not love”). Thus ourproblem is easier than the general problem of find-ing the scope of negation (Li and Lu, 2018; Qianet al., 2016; Fancellu et al., 2016). We use 30negation markers from Reitan et al. (2015) to findnegation scope in tweets. We first detect whether anegation marker appears in either Hint or Sim, butnot in both (negation can appear in Sim for ironicblame) If the marker is used, we extract its parentnode from the dependency tree, and if this node isalso present in the other utterance, then Negationstrategy is selected. For instance, in “looks justlike me” ! “does not look like me”, the negationnot is modifying the main predicate looks in Hint,which is also the main predicate in Sim (words arelemmatized). In the next section, we discuss if theparent nodes are not the same but similar and withdifferent sentiment strength.

Weakening the intensity of sentiment. Thefirst strategy — replacing words expressing a highdegree of positive/negative sentiment with moreneutral ones (‘I love being sick” ! “I don’t likebeing sick)—, is applied only in conjunction withthe negation strategy. We measure the differ-ence in strength using the Dictionary of Affect(Whissell et al., 1986). Out of 31 Sim-Hint pairsin the dev set, we automatically identify 28 inter-pretations that use this approach. For the secondstrategy — removing the intensifier (I am reallyhappy” ! “I am disappointed’) —, we first deter-mine whether the intensifier exists in Sim and iseliminated from Hint. We use only adjective andadverb intensifiers from Taboada et al. (2011), pri-marily to discard conjunctions such as “so” (“nowater so I can’t wash . . . ”). This strategy is usedtogether with both lexical antonyms and Simplenegation strategies. For a candidate Sim-Hint pair,if the lexical antonym strategy is selected and aSand aH are the lexical antonyms, we determinewhether any intensifier modifies aS and no inten-sifier modifies aH . If the Negation strategy is se-

lected, we identify the negated term in the Hint

and then search its aligned node from the Sim us-ing the word-word alignment. Next, we search inthe Sim if any intensifier is intensifying the alignedterm. The strategies are denoted as AN weaksentin Table 3 and Table 4.

Interrogative to Declarative Transformation(+ Antonym/Neg). To capture this strategy weneed to determine first if the verbal irony was ex-pressed as a rhetorical question. To build a clas-sifier to detect RQ, we collect two categories oftweets (4K each) (1) tweets labeled with #sarcasmor #irony that also contain “?”, and (2) informationseeking tweets containing “?”. We train a binaryclassifier using SVM RBF Kernel with default pa-rameters. The features are Twitter-trained wordembeddings (Ghosh et al., 2015), modal verbs,pronouns, interrogative words, negations, and po-sition of “?” in a tweet. We evaluate the trainingmodel on the dev data and the P/R/F1 are 53.2%,65.4%, and 58.6%, respectively (in future workwe plan to develop more accurate models for RQdetection). Once we detect the ironic messagewas expressed as a RQ, we identify the specificinterpretation strategy accompanying the trans-formation from interrogative to declarative form:antonym or negation. These combined strategiesare denoted as ANI!D in Table 3 and Table 4.

Desiderative Constructions: Currently, we usea simple regular expression “I [w]⇤ wish” to cap-ture counterfactual cases (AN desiderative in Ta-bles 3 and Table 4).

Note, when the Simple negation and lexicalantonyms strategies are combined with other strat-egy (e.g., removing of intensifier), we considerthis combined strategy for the interpretation ofverbal irony and not the simple negation or lexicalantonym strategy (i.e., we do not double count).

Phrasal antonyms and pragmatic inference:Identifying phrasal antonyms and pragmatic in-ference is a complex task, and thus we proposea method of phrase matching based on phraseextraction via unsupervised alignment techniquein SMT. We use IBM Model 4 with HMM(Giza++; (Och and Ney, 2000)), phrase extractionvia Moses (Koehn et al., 2007) and the IRST toolto build the required language models. As post-processing, we first remove phrase pairs obtainedfrom the Sim-Hint bitext that are also present inthe set of extracted phrases from the Hint-Hint

Strategies Sim-Hint SIGNLex ant 2,198 (40.0) 9,691 (51.8)Simple neg 1,596 (29.1) 3,827 (20.5)AN weaksent 895 (16.3) 2,160 (11.6)ANI!D 329 (6.0) 933 (5.0)AN desiderative 92 (1.7) 86 (0.5)AntPhrase+PragInf 357 (6.5) 1912 (10.1)

Table 4: Distribution of interpretation strategies ontwo datasets (in %)

bitext. This increases the likelihood of retainingsemantically opposite phrases, since phrases ex-tracted from the Hint-Hint bitext are more likelyto be paraphrastic. Second, based on the transla-tion probability scores �, for phrase e if we havea set of aligned phrases fset we reject phrasesthat have � scores less than 1

size(fset). Finally,

11,200 phrases are extracted from the Sim-Hint

bitext. The low recall for this strategy is ex-pected since there are too many ways that userscan employ pragmatic inference or rephrase theutterance without directly using any antonym ornegation. In future, we will explore neural MT(Cho et al., 2014) and use external data to gen-erate more phrases. Since we have not manuallyevaluated these phrase pairs, we only use this strat-egy after we have tried all the remaining strategies(AntPhrase+PragInf in Table 3 and Table 4).

5.2 Results and Distribution of LinguisticStrategies

The performance of the models is similar on bothtest and SIGNtest sets, showing consistentlygood performance (Table 3; 90% F1 for all strate-gies, except the AntPhrase+PragInf and ANI!D).Given these results, we can now apply these mod-els to study the distribution of these strategies inthe entire datasets (Table 4). The strategy distri-bution between our dataset Sim-Hint and SIGNdataset is similar and matches the distribution onthe manual annotations on the dev dataset in Ta-ble 2. The sum of the strategies can exceed thetotal number of the pairs since a tweet can con-tain several ironic sentences that are interpretedby Turkers. For instance, in “Dave too nice . . . anice fella” ! “Dave not nice . . . a mean fella” weobserve the application of two strategies, lexicalantonyms (e.g., nice ! mean) and negation (e.g.,nice ! not nice).

6 Discussion

6.1 Hearer-dependent InterpretationStrategies

We investigate how hearers adopt strategies for in-terpreting the speaker’s ironic intent. To imple-ment this study, we selected three Turkers (e.g.,H1, H2, and H3; In Table 1, Hi

intare generated

by the correspondent Turker Hi), from our crowd-sourced data, who were able to rephrase at leastfive hundred identical Sim messages. Note, wecannot carry this experiment on the SIGN dataset(Peled and Reichart, 2017) because the annotators’information is absent there.

Although the three Turkers choose lexicalantonym and simple negation as two top choices,there is some variation among them. H1 andH2 choose antonyms more frequently than nega-tion while in contrary Turker H3 choose negationmore than antonyms, sometime combined with theweakening of sentiment strategy. As we mentionedin Section 4.2, antonyms and direct negation arenot semantically equivalent strategies since the lat-ter, allows a graded interpretation: if “x is not in-spiring”, it is not necessarily bad, but simply “x <inspiring” (Giora, 1995). In Table 1, the Sim-Hint

pair “passionate” ! “boring” and “flattering” !“gross” (interpretation of H1) have more contrastthan the pair “passionate” ! “not passionate” and“so flattering” ! “not flattering” (interpretation ofH3). This suggests that H1 perceive the intensityof negative sentiment towards the target of irony(“Ed Davey” and “picture of dead animals”, re-spectively) higher than Turker H3. All three Turk-ers have chosen the remaining strategies with sim-ilar frequencies.

6.2 Message-dependent InterpretationStrategies

Interpretation Strategies and the Type of Se-mantic Incongruity: We investigate whetherthe type of semantic incongruity in the ironic mes-sage (explicit vs. implicit; see Section 3) influ-ences the choice of interpretation strategies by thehearers. To do this, we looked at Sim-level distri-bution of interpretation strategies used by the hear-ers for the same ironic message Sim. Table 5 rep-resents the correlation of linguistic strategies withthe type of semantic incongruity (explicit vs. im-plicit) as well as the presence and absence of ironymarkers.

We notice that Turkers use lexical antonyms

Strategies incongruity markerExp. Imp. + �

Lex ant 48.5 34.8 35.7 42.2Simple neg 24.9 32.3 28.9 30.0AN weaksent 14.3 17.6 15.7 16.8ANI!D 5.9 6.1 12.3 3.1AN desiderative 1.3 1.9 0.9 2.0AntPhrase+PragInf 5.2 7.1 6.2 6.6

Table 5: Rephrasing Strategies against Incongru-ency and Irony Markers on Sim-Hint dataset (in%)

Figure 1: Strategies selected per message (in %)

as interpretation strategy more when the seman-tic incongruity is explicit than implicit (48.5% vs.34.8%): the presence of explicit sentiment trig-gered the use of the antonym strategy. In contrarythey use simple negation more when the semanticincongruity is implicit than explicit.

We also analyze the interpretation strategiesw.r.t. to the presence (+) or absence (�) of ironymarkers. We implement various morpho-syntacticas well as typographic markers (similar to (Ghoshand Muresan, 2018)) to identify the presence ofmarkers. We observe that Lex ant strategy isused more in cases where the markers are ab-sent. In Sim-Hint, markers are present twice asmuch in the case of implicit (21%) than explicitincongruity (10%). This finding validates (Burg-ers et al., 2012) who argued speakers will likelyuse markers to signal their ironic intent in implicitincongruity.

Message interpreted the same by all hearers:In Figure 1, the vertical columns (purple: Sim-Hint and grey: SIGN ) depict the distribution (in%) of tweets strategy-wise. In Sim-Hint dataset,for 17% of messages (124 Sims) all five Turkersuse the same strategy to interpret the Sims (labeledas 5 on the X-axis), whereas for 26% (188 Sims),4 Turkers used same strategy (labeled as 4,1 on X-axis) and so on.

We observe when the Sims are marked by strongsubjective words e.g., “great”, “best”, etc., they

have been replaced in 90% of cases as lexicalantonyms (e.g., “great” ! “terrible”). In addition,the majority of adjectives are used in attributiveposition (i.e., “lovely neighbor is vacuuming atnight”), thus blocking paraphrases involving pred-icate negation. However, not all strong subjec-tive words guarantee the use of direct oppositesin the Hints (e.g., “flattering” ! “not flattering”;See Table 1). The choice of strategies may alsodepend upon the target of ironic situation (Ivankoand Pexman, 2003). We implement the bootstrap-ping algorithm from Riloff et al. (2013) to iden-tify ironic situations in Sims that are rephrased byLexical antonym strategy. We find utterances con-taining stereotypical negative situations regardinghealth issues (e.g., “having migraines”, “gettingkilled by chemicals”) and other undesirable nega-tive states such as “oversleeping”, “luggage lost”,“stress in life” are almost always interpreted vialexical antonym strategy.

Utterances where all five Turkers used simplenegation, if negative particles are positioned inthe ironic message with a sentential scope (e.g.,“not a biggie”, “not awkward”) then they are sim-ply omitted in the interpretations. This trend canbe explained according to the inter-subjective ac-count of negation types (Verhagen, 2005). Sen-tential negation leads the addressee to open up analternative mental space where an opposite predi-cation is at stake.

7 Related Work

Most NLP research on verbal irony or sarcasm hasfocused on the task of sarcasm detection treatingit as a binary classification task using either the ut-terance in isolation or adding contextual informa-tion such as conversation context, author context,visual context, or cognitive features (Gonzalez-Ibanez et al., 2011; Liebrecht et al., 2013; Wallaceet al., 2014; Zhang et al., 2016; Ghosh and Veale,2016; Schifanella et al., 2016; Xiong et al., 2019;Castro et al., 2019). Unlike this line of work,our research focuses on how the hearer interpretsan ironic message. The findings from our studycould have multiple impacts on the sarcasm de-tection task. First, interpretation strategies openup a scope of “graded interpretation” of irony in-stead of only a binary decision (i.e., predicting thestrength of irony). Second, nature of semantic in-congruence and stereotype irony situations can beuseful features in irony detection.

Recently, Peled and Reichart (2017) proposeda computational model based on SMT to gen-erate interpretations of sarcastic messages. Weaim to deepen our understanding of such inter-pretations by introducing a typology of linguis-tic strategies. We study the distribution of thesestrategies via both hearer-dependent and message-dependent interpretations. Psycholinguistics stud-ies that have dealt with the hearers’ perception,have mainly focused on how ironic messages areprocessed: through the analysis of reaction times(Gibbs, 1986; Katz et al., 2004), the role of situa-tional context (Ivanko and Pexman, 2003) and intackling speaker-hearer social relations by anno-tating ironic texts from different genres (Burgers,2010). However, no attention has been paid to cor-relations between how ironic message is expressedand how it is interpreted by the hearer, includingwhat linguistic strategies the hearers employ.

8 Conclusions

We leveraged a crowdsourcing task to obtain adataset of ironic utterances paired with the hearer’sverbalization of their interpretation. We proposeda typology of linguistic strategies for verbal ironyinterpretation and designed computational mod-els to capture these strategies with good perfor-mance. Our study shows (1) Turkers mostly adoptlexical antonym and negation strategies to inter-pret speaker’s irony, (2) interpretations are corre-lated to stereotype ironic situations, and (3) ironyexpression (explicit vs. implicit incongruity andabsence or presence of markers) influences thechoice of interpretation strategies and match withdifferent explanatory theories (the Gricean ap-proach links up better with explicit incongruity,while Relevance Theory with the implicit one).The latter can have an impact on irony detectionby bringing out more discriminative semantic andpragmatic features.

Acknowledgements

We thank Rituparna Mukherjee, Daniel Chaparro,Pedro Perez Sanchez, and Renato Augusto VieiraNishimori who helped us in annotating as well asin running experiments. This paper partially basedon the work supported by the DARPA-DEFT pro-gram. The views expressed are those of the au-thors and do not reflect the official policy or po-sition of the Department of Defense or the U.S.Government.

ReferencesSilvio Amir, Byron C Wallace, Hao Lyu, and Paula

Carvalho Mario J Silva. 2016. Modelling contextwith user embeddings for sarcasm detection in so-cial media. arXiv preprint arXiv:1607.00976 .

Salvatore Attardo. 2000. Irony markers and functions:Towards a goal-oriented theory of irony and its pro-cessing. Rask 12(1):3–20.

David Bamman and Noah A Smith. 2015. Contextual-ized sarcasm detection on twitter. In Ninth Interna-tional AAAI Conference on Web and Social Media.

Christian Burgers, Margot Van Mulken, and PeterSchellens. 2012. Verbal irony differences in usageacross written genres. Journal of Language and So-cial Psychology 31(3):290–310.

Christian Frederik Burgers. 2010. Verbal irony: Useand effects in written discourse. [Sl: sn].

Santiago Castro, Devamanyu Hazarika, VeronicaPerez-Rosas, Roger Zimmermann, Rada Mihalcea,and Soujanya Poria. 2019. Towards multimodal sar-casm detection (an obviously perfect paper). arXivpreprint arXiv:1906.01815 .

Timothy Chklovski and Patrick Pantel. 2004. Verbo-cean: Mining the web for fine-grained semantic verbrelations. In EMNLP. volume 4, pages 33–40.

Kyunghyun Cho, Bart Van Merrienboer, Caglar Gul-cehre, Dzmitry Bahdanau, Fethi Bougares, HolgerSchwenk, and Yoshua Bengio. 2014. Learningphrase representations using rnn encoder-decoderfor statistical machine translation. arXiv preprintarXiv:1406.1078 .

Dmitry Davidov, Oren Tsur, and Ari Rappoport. 2010.Semi-supervised recognition of sarcastic sentencesin twitter and amazon. In Proceedings of the Four-teenth Conference on Computational Natural Lan-guage Learning. CoNLL ’10.

Marie-Catherine De Marneffe, Anna N Rafferty, andChristopher D Manning. 2008. Finding contradic-tions in text. In ACL. volume 8, pages 1039–1047.

Federico Fancellu, Adam Lopez, and Bonnie Webber.2016. Neural networks for negation scope detection.In Proceedings of the 54th Annual Meeting of the As-sociation for Computational Linguistics. volume 1,pages 495–504.

Bjarke Felbo, Alan Mislove, Anders Søgaard, IyadRahwan, and Sune Lehmann. 2017. Using mil-lions of emoji occurrences to learn any-domain rep-resentations for detecting sentiment, emotion andsarcasm. In Proceedings of the 2017 Confer-ence on Empirical Methods in Natural LanguageProcessing. Association for Computational Linguis-tics, pages 1615–1625. https://doi.org/10.18653/v1/D17-1169.

Christiane Fellbaum. 1998. WordNet. Wiley OnlineLibrary.

Aniruddha Ghosh and Tony Veale. 2016. Frackingsarcasm using neural network. In Proceedings ofNAACL-HLT . pages 161–169.

Aniruddha Ghosh and Tony Veale. 2017. Magnetsfor sarcasm: Making sarcasm detection timely, con-textual and very personal. In Proceedings of the2017 Conference on Empirical Methods in Natu-ral Language Processing. Association for Compu-tational Linguistics, pages 482–491. https://doi.org/10.18653/v1/D17-1050.

Debanjan Ghosh, Alexander R. Fabbri, and SmarandaMuresan. 2018. Sarcasm analysis using conversa-tion context. Computational Linguistics 44(4):755–792. https://doi.org/10.1162/coli_a_00336.

Debanjan Ghosh, Weiwei Guo, and Smaranda Mure-san. 2015. Sarcastic or not: Word embeddings topredict the literal or sarcastic meaning of words. InProceedings of the 2015 Conference on EmpiricalMethods in Natural Language Processing. Associ-ation for Computational Linguistics, Lisbon, Portu-gal, pages 1003–1012. http://aclweb.org/anthology/D15-1116.

Debanjan Ghosh and Smaranda Muresan. 2018. ” with1 follower i must be awesome: P.” exploring the roleof irony markers in irony recognition. In TwelfthInternational AAAI Conference on Web and SocialMedia.

Debanjan Ghosh, Alexander Richard Fabbri, andSmaranda Muresan. 2017. The role of conversa-tion context for sarcasm detection in online inter-actions. In Proceedings of the 18th Annual SIG-dial Meeting on Discourse and Dialogue. Asso-ciation for Computational Linguistics, Saarbrcken,Germany, pages 186–196. http://aclweb.org/anthology/W17-5523.

Raymond W Gibbs. 1986. On the psycholinguisticsof sarcasm. Journal of Experimental Psychology:General 115(1):3.

Rachel Giora. 1995. On irony and negation. Discourseprocesses 19(2):239–264.

Roberto Gonzalez-Ibanez, Smaranda Muresan, andNina Wacholder. 2011. Identifying sarcasm in twit-ter: A closer look. In ACL (Short Papers). Associa-tion for Computational Linguistics, pages 581–586.

H Paul Grice. 1975. ” logic and conversation” in cole,p., and morgan, j.(eds.). Syntax & Semantics 3.

Henk Haverkate. 1990. A speech act analysis of irony.Journal of Pragmatics 14(1):77–109.

Devamanyu Hazarika, Soujanya Poria, Sruthi Gorantla,Erik Cambria, Roger Zimmermann, and Rada Mi-halcea. 2018. Cascade: Contextual sarcasm detec-tion in online discussion forums. In Proceedings of

the 27th International Conference on ComputationalLinguistics. Association for Computational Linguis-tics, pages 1837–1848. http://aclweb.org/anthology/C18-1156.

Laurence Horn. 1989. A natural history of negation.The University of Chicago Press.

Minqing Hu and Bing Liu. 2004. Mining and summa-rizing customer reviews. In Proceedings of the tenthACM SIGKDD international conference on Knowl-edge discovery and data mining. ACM, pages 168–177.

Albrecht Werner Inhoff, Susan D Lima, and Patrick JCarroll. 1984. Contextual effects on metaphorcomprehension in reading. Memory & Cognition12(6):558–567.

Stacey L Ivanko and Penny M Pexman. 2003. Contextincongruity and irony processing. Discourse Pro-cesses 35(3):241–279.

Aditya Joshi, Vinita Sharma, and Pushpak Bhat-tacharyya. 2015. Harnessing context incongruity forsarcasm detection. In ACL (2). pages 757–762.

Jihen Karoui, Farah Benamara, Veronique Moriceau,Viviana Patti, Cristina Bosco, and NathalieAussenac-Gilles. 2017. Exploring the impact ofpragmatic phenomena on irony detection in tweets:A multilingual corpus study. Association forComputational Linguistics (ACL).

Albert N Katz, Dawn G Blasko, and Victoria AKazmerski. 2004. Saying what you don’t mean:Social influences on sarcastic language process-ing. Current Directions in Psychological Science13(5):186–189.

Philipp Koehn, Hieu Hoang, Alexandra Birch, ChrisCallison-Burch, Marcello Federico, Nicola Bertoldi,Brooke Cowan, Wade Shen, Christine Moran,Richard Zens, et al. 2007. Moses: Open sourcetoolkit for statistical machine translation. In Pro-ceedings of the 45th Annual Meeting of the ACL onInteractive Poster and Demonstration Sessions. As-sociation for Computational Linguistics, pages 177–180.

Roger J Kreuz. 2000. The production and processingof verbal irony. Metaphor and Symbol 15(1-2):99–107.

Roger J Kreuz and Sam Glucksberg. 1989. How tobe sarcastic: The echoic reminder theory of verbalirony. Journal of experimental psychology: General118(4):374.

Hao Li and Wei Lu. 2018. Learning with structuredrepresentations for negation scope extraction. InProceedings of the 56th Annual Meeting of the As-sociation for Computational Linguistics (Volume 2:Short Papers). volume 2, pages 533–539.

CC Liebrecht, FA Kunneman, and APJ van den Bosch.2013. The perfect solution for detecting sarcasm intweets# not. In Proceedings of the 4th Workshopon Computational Approaches to Subjectivity, Senti-ment and Social Media Analysis.

Diana Maynard and Mark A Greenwood. 2014. Whocares about sarcastic tweets? investigating the im-pact of sarcasm on sentiment analysis. In Proceed-ings of LREC.

Belen Mendez-Naya. 2008. Special issue on en-glish intensifiers. English Language and Linguistics12(02):213–219.

Abhijit Mishra, Diptesh Kanojia, Seema Nagar, KuntalDey, and Pushpak Bhattacharyya. 2016. Harness-ing cognitive features for sarcasm detection. In Pro-ceedings of the 54th Annual Meeting of the Associa-tion for Computational Linguistics (Volume 1: LongPapers). Association for Computational Linguistics,Berlin, Germany, pages 1095–1104. https://doi.org/10.18653/v1/P16-1104.

Saif M. Mohammad, Bonnie J. Dorr, Graeme Hirst, andPeter D. Turney. 2013. Computing lexical contrast.Computational Linguistics 39(3):555–590.

Smaranda Muresan, Roberto Gonzalez-Ibanez, Deban-jan Ghosh, and Nina Wacholder. 2016. Identifica-tion of nonliteral language in social media: A casestudy on sarcasm. Journal of the Association forInformation Science and Technology http://dx.doi.org/10.1002/asi.23624.

Franz Josef Och and Hermann Ney. 2000. Giza++:Training of statistical translation models.

Franz Josef Och and Hermann Ney. 2004. The align-ment template approach to statistical machine trans-lation. Computational linguistics 30(4):417–449.

Silviu Oprea and Walid Magdy. 2019. Exploring au-thor context for detecting intended vs perceived sar-casm. In Proceedings of the 57th Annual Meetingof the Association for Computational Linguistics.pages 2854–2859.

Lotem Peled and Roi Reichart. 2017. Sarcasmsign: Interpreting sarcasm with sentiment basedmonolingual machine translation. arXiv preprintarXiv:1704.06836 .

Zhong Qian, Peifeng Li, Qiaoming Zhu, GuodongZhou, Zhunchen Luo, and Wei Luo. 2016. Specula-tion and negation scope detection via convolutionalneural networks. In Proceedings of the 2016 Con-ference on Empirical Methods in Natural LanguageProcessing. pages 815–825.

Johan Reitan, Jørgen Faret, Bjorn Gamback, and LarsBungum. 2015. Negation scope detection for twittersentiment analysis. In Proceedings of the 6th Work-shop on Computational Approaches to Subjectivity,Sentiment and Social Media Analysis. pages 99–108.

Ellen Riloff, Ashequl Qadir, Prafulla Surve, Lalin-dra De Silva, Nathan Gilbert, and Ruihong Huang.2013. Sarcasm as contrast between a positive senti-ment and negative situation. In Proceedings of theConference on Empirical Methods in Natural Lan-guage Processing. Association for ComputationalLinguistics, pages 704–714.

Rossano Schifanella, Paloma de Juan, Joel Tetreault,and Liangliang Cao. 2016. Detecting sarcasm inmultimodal social platforms. In Proceedings of the2016 ACM on Multimedia Conference. ACM, pages1136–1145.

Dan Sperber and Deirdre Wilson. 1986. Relevance:Communication and cognition, volume 142. Har-vard University Press Cambridge, MA.

Maite Taboada, Julian Brooke, Milan Tofiloski, Kim-berly Voll, and Manfred Stede. 2011. Lexicon-basedmethods for sentiment analysis. Computational lin-guistics 37(2):267–307.

Yi Tay, Anh Tuan Luu, Siu Cheung Hui, and JianSu. 2018. Reasoning with sarcasm by reading in-between. In Proceedings of the 56th Annual Meet-ing of the Association for Computational Linguis-tics (Volume 1: Long Papers). Association for Com-putational Linguistics, pages 1010–1020. http://aclweb.org/anthology/P18-1093.

Arie Verhagen. 2005. Constructions of intersubjectiv-ity: Discourse, syntax, and cognition. Oxford Uni-versity Press on Demand.

Byron C Wallace, Do Kook Choe, Laura Kertz, andEugene Charniak. 2014. Humans require context toinfer ironic intent (so computers probably do, too).In ACL (2). pages 512–516.

Cynthia Whissell, Michael Fournier, Rene Pelland,Deborah Weir, and Katherine Makarec. 1986. Adictionary of affect in language: Iv. reliability, va-lidity, and applications. Perceptual and Motor Skills62(3):875–888.

Deirdre Wilson and Dan Sperber. 2012. Explainingirony. Meaning and relevance pages 123–145.

Theresa Wilson, Janyce Wiebe, and Paul Hoffmann.2005. Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the con-ference on human language technology and empiri-cal methods in natural language processing. Associ-ation for Computational Linguistics, pages 347–354.

Tao Xiong, Peiran Zhang, Hongbo Zhu, and YihuiYang. 2019. Sarcasm detection with self-matchingnetworks and low-rank bilinear pooling. In TheWorld Wide Web Conference. ACM, pages 2115–2124.

Meishan Zhang, Yue Zhang, and Guohong Fu. 2016.Tweet sarcasm detection using deep neural network.In COLING.