Papers by Silviu Oprea
Exploring Author Context for Detecting Intended vs Perceived Sarcasm (P19-1)
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| Challenge: | Existing studies on textual sarcasm detection use manual labelling and tag-based distant supervision to detect sarcasm. |
| Approach: | They define author context as the embedded representation of their historical tweets and suggest neural models that extract these representations. |
| Outcome: | The proposed models achieve state-of-the-art on two datasets labelled manually and via tag-based distant supervision indicating a difference between intended and perceived sarcasm . |
iSarcasm: A Dataset of Intended Sarcasm (2020.acl-main)
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| Challenge: | Existing methods for detecting intended sarcasm have shown low performance compared to previous studies. |
| Approach: | They propose a dataset of tweets labeled for intended sarcasm by their authors . they aim to encourage future NLP research to develop methods for detecting sarkasmus in text as intended by the authors of the text . |
| Outcome: | The proposed model shows that existing methods are biased or obvious and sarcasm could be understudied. |
Sarcasm Detection is Way Too Easy! An Empirical Comparison of Human and Machine Sarcasm Detection (2022.findings-emnlp)
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| Challenge: | sarcasm detection datasets focus on intended, rather than perceived sarcasm, but there is no comparison between human and machine performance. |
| Approach: | They collect author-annotated sarcasm datasets that focus on intended, rather than perceived sarcasticism . they compare human-level benchmarks to that of state-of-the-art sarkasmatic detection systems . |
| Outcome: | The proposed datasets compare human and machine performance on sarcastic tasks in English and Arabic. |
Chandler: An Explainable Sarcastic Response Generator (2021.emnlp-demo)
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| Challenge: | sarcasm generators assume intended meaning is opposite of literal meaning . sarcastically generated responses are more specific and coherent to input . |
| Approach: | They propose a system that generates sarcastic responses to a given utterance . they ground their generation process on a formal theory that unambiguously differentiates . |
| Outcome: | The proposed system generates sarcastic responses to a given utterance. |