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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 8 of 8 1 A Comprehensive Understanding of Code-mixed Language Semantics using Hierarchical Transformer ... Sengupta, Ayan; Suresh, Tharun; Akhtar, Md Shad. - : arXiv, 2022 BASE Show details 2 HIT - A Hierarchically Fused Deep Attention Network for Robust Code-mixed Language Representation ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Akhtar, Md Shad; Bhattacharjee, Sourabh. - : Underline Science Inc., 2021 BASE Show details 3 HIT: A Hierarchically Fused Deep Attention Network for Robust Code-mixed Language Representation ... Sengupta, Ayan; Bhattacharjee, Sourabh Kumar; Chakraborty, Tanmoy. - : arXiv, 2021 BASE Show details 4 Nice perfume. How long did you marinate in it? Multimodal Sarcasm Explanation ... Desai, Poorav; Chakraborty, Tanmoy; Akhtar, Md Shad. - : arXiv, 2021 BASE Show details 5 Detecting Harmful Memes and Their Targets ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Akhtar, Md Shad; Charaborty, Tanmoy. - : Underline Science Inc., 2021 BASE Show details 6 LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content ... Gupta, Shreya; Singh, Parantak; Sundriyal, Megha; Akhtar, Md Shad; Chakraborty, Tanmoy. - : arXiv, 2021 Abstract: The conceptualization of a claim lies at the core of argument mining. The segregation of claims is complex, owing to the divergence in textual syntax and context across different distributions. Another pressing issue is the unavailability of labeled unstructured text for experimentation. In this paper, we propose LESA, a framework which aims at advancing headfirst into expunging the former issue by assembling a source-independent generalized model that captures syntactic features through part-of-speech and dependency embeddings, as well as contextual features through a fine-tuned language model. We resolve the latter issue by annotating a Twitter dataset which aims at providing a testing ground on a large unstructured dataset. Experimental results show that LESA improves upon the state-of-the-art performance across six benchmark claim datasets by an average of 3 claim-F1 points for in-domain experiments and by 2 claim-F1 points for general-domain experiments. On our dataset too, LESA outperforms existing ... : 9 pages (plus 2 pages of references), 1 figure, 9 tables, accepted at EACL 2021 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2101.11891 https://arxiv.org/abs/2101.11891 BASE Hide details 7 DESYR: Definition and Syntactic Representation Based Claim Detection on the Web ... Sundriyal, Megha; Singh, Parantak; Akhtar, Md Shad. - : arXiv, 2021 BASE Show details 8 Related Tasks can Share! A Multi-task Framework for Affective language ... Deep, Kumar Shikhar; Akhtar, Md Shad; Ekbal, Asif. - : arXiv, 2020 BASE Show details

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