{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/when-did-you-become-so-smart-oh-wise-one-1","title":"When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues","arxiv_id":"2203.06419","date":"2022-03-12","proceeding":"ACL 2022 5","authors":["Shivani Kumar","Atharva Kulkarni","Md Shad Akhtar","Tanmoy Chakraborty"],"abstract":"Indirect speech such as sarcasm achieves a constellation of discourse goals in human communication. While the indirectness of figurative language warrants speakers to achieve certain pragmatic goals, it is challenging for AI agents to comprehend such idiosyncrasies of human communication. Though sarcasm identification has been a well-explored topic in dialogue analysis, for conversational systems to truly grasp a conversation's innate meaning and generate appropriate responses, simply detecting sarcasm is not enough; it is vital to explain its underlying sarcastic connotation to capture its true essence. In this work, we study the discourse structure of sarcastic conversations and propose a novel task - Sarcasm Explanation in Dialogue (SED). Set in a multimodal and code-mixed setting, the task aims to generate natural language explanations of satirical conversations. To this end, we curate WITS, a new dataset to support our task. We propose MAF (Modality Aware Fusion), a multimodal context-aware attention and global information fusion module to capture multimodality and use it to benchmark WITS. The proposed attention module surpasses the traditional multimodal fusion baselines and reports the best performance on almost all metrics. Lastly, we carry out detailed analyses both quantitatively and qualitatively.","url_abs":"https://arxiv.org/abs/2203.06419v1","url_pdf":"https://arxiv.org/pdf/2203.06419v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"when-did-you-become-so-smart-oh-wise-one-1","repo_url":"https://github.com/lcs2-iiitd/maf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"sarcasm-detection","task_name":"Sarcasm Detection"}],"methods":[{"method_slug":"aware","method_name":"AWARE"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/sarcasm-detection-on-wits","task":"Sarcasm Detection","dataset":"WITS","model":"BART","rank_in_archive_order":1,"of":1,"metrics":{"R1":"36.88"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2203.06419","atlas_url":"https://app.syntology.ai/?focus=2203.06419","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.06419"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/LCS2-IIITD/MAF","reach":null}],"summary":{"ran":1,"unverified":1},"by_repo_kind":{"official":{"samples":2,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":2,"samples":[{"code_sha256_prefix":"2df60328125fffa3","entry":"ContextAwareAttention","repo":"LCS2-IIITD/MAF","repo_kind":"official","path":"Code/Trimodal-BART-driver.py","file_url":"https://github.com/LCS2-IIITD/MAF/blob/HEAD/Code/Trimodal-BART-driver.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"2df60328125fffa3"}},{"code_sha256_prefix":"cfce1d7883f67fe5","entry":"MAF","repo":"LCS2-IIITD/MAF","repo_kind":"official","path":"Code/Trimodal-BART-driver.py","file_url":"https://github.com/LCS2-IIITD/MAF/blob/HEAD/Code/Trimodal-BART-driver.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"cfce1d7883f67fe5"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}