Linguistic Features of Sarcasm and Metaphor Production Quality

Skalicky, Stephen; Crossley, Scott A.

doi:10.18653/v1/w18-0902

Cited by 10 publications

(4 citation statements)

References 24 publications

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“…"), (3) Humour ("How funny are the sentences ?") (Skalicky and Crossley, 2018), and (4) Grammaticality ("How grammatical are the sentences ?"). We design a MTurk task where Turkers were asked to rate outputs from all the six systems.…”

Section: Evaluation Criteriamentioning

confidence: 99%

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Rˆ3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge

Chakrabarty

Ghosh

Muresan

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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We propose an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence. Our method employs a retrieve-andedit framework to instantiate two major characteristics of sarcasm: reversal of valence and semantic incongruity with the context, which could include shared commonsense or world knowledge between the speaker and the listener. While prior works on sarcasm generation predominantly focus on context incongruity, we show that combining valence reversal and semantic incongruity based on commonsense knowledge generates sarcastic messages of higher quality based on several criteria. Human evaluation shows that our system generates sarcasm better than human judges 34% of the time, and better than a reinforced hybrid baseline 90% of the time.

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Section: Evaluation Criteriamentioning

confidence: 99%

“…For instance, for the first and the second example in Table 4, all of FM, RV and NSI are better than human generated sarcasm. In general, the generations from the FM model are more humorous, which is also an useful criterion to evaluate sarcasm besides sarcasticness (Skalicky and Crossley, 2018).…”

Section: Qualitative Analysismentioning

confidence: 99%

Rˆ3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge

Chakrabarty

Ghosh

Muresan

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

View full text Add to dashboard Cite

show abstract

“…With the explosion of internet usage, sarcasm detection in online communications from social networking platforms, discussion forums, and e-commerce websites has become crucial for opinion mining, sentiment analysis, and identifying cyberbullies—online trolls. The topic of sarcasm received great interest from Neuropsychology [ 1 ] to Linguistics [ 2 ], but developing computational models for automatic detection of sarcasm is still at its nascent phase. Earlier works on sarcasm detection on texts use lexical (content) and pragmatic (context) cues [ 3 ] such as interjections, punctuation, and sentimental shifts, which are major indicators of sarcasm [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Interpretable Multi-Head Self-Attention Architecture for Sarcasm Detection in Social Media

Akula

Garibay

2021

Entropy

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With the online presence of more than half the world population, social media plays a very important role in the lives of individuals as well as businesses alike. Social media enables businesses to advertise their products, build brand value, and reach out to their customers. To leverage these social media platforms, it is important for businesses to process customer feedback in the form of posts and tweets. Sentiment analysis is the process of identifying the emotion, either positive, negative or neutral, associated with these social media texts. The presence of sarcasm in texts is the main hindrance in the performance of sentiment analysis. Sarcasm is a linguistic expression often used to communicate the opposite of what is said, usually something that is very unpleasant, with an intention to insult or ridicule. Inherent ambiguity in sarcastic expressions make sarcasm detection very difficult. In this work, we focus on detecting sarcasm in textual conversations from various social networking platforms and online media. To this end, we develop an interpretable deep learning model using multi-head self-attention and gated recurrent units. The multi-head self-attention module aids in identifying crucial sarcastic cue-words from the input, and the recurrent units learn long-range dependencies between these cue-words to better classify the input text. We show the effectiveness of our approach by achieving state-of-the-art results on multiple datasets from social networking platforms and online media. Models trained using our proposed approach are easily interpretable and enable identifying sarcastic cues in the input text which contribute to the final classification score. We visualize the learned attention weights on a few sample input texts to showcase the effectiveness and interpretability of our model.

show abstract

“…With the explosion of internet usage, sarcasm detection in online communications from social networking platforms [1,2], discussion forums [3,4], and e-commerce websites has become crucial for opinion mining, sentiment analysis, and in identifying cyberbullies, online trolls. The topic of sarcasm received great interest from Neuropsychology [5] to Linguistics [6], but developing computational models for automatic detection of sarcasm is still at its nascent phase. Earlier works on sarcasm detection on texts use lexical (content) and pragmatic (context) cues [7] such as interjections, punctuation, and sentimental shifts, that are major indicators of sarcasm [8].…”

Section: Introductionmentioning

confidence: 99%

Interpretable Multi-Head Self-Attention Architecture for Sarcasm Detection in Social Media

Akula¹,

Garibay²

2021

Preprint

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Sarcasm is a linguistic expression often used to communicate the opposite of what is said, usually something that is very unpleasant with an intention to insult or ridicule. Inherent ambiguity in sarcastic expressions, make sarcasm detection very difficult. In this work, we focus on detecting sarcasm in textual conversations from various social networking platforms and online media. To this end, we develop an interpretable deep learning model using multi-head self-attention and gated recurrent units. Multi-head self-attention module aids in identifying crucial sarcastic cue-words from the input, and the recurrent units learn long-range dependencies between these cue-words to better classify the input text. We show the effectiveness of our approach by achieving state-of-the-art results on multiple datasets from social networking platforms and online media. Models trained using our proposed approach are easily interpretable and enable identifying sarcastic cues in the input text which contribute to the final classification score. We visualize the learned attention weights on few sample input texts to showcase the effectiveness and interpretability of our model.

show abstract

Linguistic Features of Sarcasm and Metaphor Production Quality

Cited by 10 publications

References 24 publications

Rˆ3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge

Rˆ3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge

Interpretable Multi-Head Self-Attention Architecture for Sarcasm Detection in Social Media

Interpretable Multi-Head Self-Attention Architecture for Sarcasm Detection in Social Media

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