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1
Semi-automatic WordNet Linking using Word Embeddings ...
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Indian Language Wordnets and their Linkages with Princeton WordNet ...
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Tapping BERT for Preposition Sense Disambiguation ...
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4
Techniques for Jointly Extracting Entities and Relations: A Survey ...
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5
Knowledge-based Extraction of Cause-Effect Relations from Biomedical Text ...
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How low is too low? A monolingual take on lemmatisation in Indian languages ...
NAACL 2021 2021; Bhattacharyya, Pushpak; Saunack, Kumar. - : Underline Science Inc., 2021
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Role of Language Relatedness in Multilingual Fine-tuning of Language Models: A Case Study in Indo-Aryan Languages ...
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M2H2: A Multimodal Multiparty Hindi Dataset For Humor Recognition in Conversations ...
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"So You Think You're Funny?": Rating the Humour Quotient in Standup Comedy ...
Abstract: Computational Humour (CH) has attracted the interest of Natural Language Processing and Computational Linguistics communities. Creating datasets for automatic measurement of humour quotient is difficult due to multiple possible interpretations of the content. In this work, we create a multi-modal humour-annotated dataset ($\sim$40 hours) using stand-up comedy clips. We devise a novel scoring mechanism to annotate the training data with a humour quotient score using the audience's laughter. The normalized duration (laughter duration divided by the clip duration) of laughter in each clip is used to compute this humour coefficient score on a five-point scale (0-4). This method of scoring is validated by comparing with manually annotated scores, wherein a quadratic weighted kappa of 0.6 is obtained. We use this dataset to train a model that provides a "funniness" score, on a five-point scale, given the audio and its corresponding text. We compare various neural language models for the task of humour-rating and ... : Accepted at EMNLP 2021 Main Conference (short papers); 4 pages, 1 figure, 3 tables ...
Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/2110.12765
https://dx.doi.org/10.48550/arxiv.2110.12765
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10
Cognition-aware Cognate Detection ...
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11
Role of Language Relatedness in Multilingual Fine­-tuning of Language Models: A Case Study in Indo-­Aryan Languages ...
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12
Crosslingual Embeddings are Essential in UNMT for Distant Languages: An English to IndoAryan Case Study ...
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13
Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages ...
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14
Modelling source- and target-language syntactic Information as conditional context in interactive neural machine translation
In: Gupta, Kamal Kumar, Haque, Rejwanul orcid:0000-0003-1680-0099 , Ekbal, Asif, Bhattacharyya, Pushpak and Way, Andy orcid:0000-0001-5736-5930 (2020) Modelling source- and target-language syntactic Information as conditional context in interactive neural machine translation. In: Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, 2-6 Nov 2020, Lisboa, Portugal. (2020)
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15
Syntax-informed interactive neural machine translation
In: Gupta, Kamal Kumar, Haque, Rejwanul orcid:0000-0003-1680-0099 , Ekbal, Asif, Bhattacharyya, Pushpak and Way, Andy orcid:0000-0001-5736-5930 (2020) Syntax-informed interactive neural machine translation. In: The International Joint Conference on Neural Networks (IJCNN), 19-24 July 2020, Glasgow, UK (Online). (2020)
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16
Extracting N-ary Cross-sentence Relations using Constrained Subsequence Kernel ...
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17
Related Tasks can Share! A Multi-task Framework for Affective language ...
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18
Reinforced Multi-task Approach for Multi-hop Question Generation ...
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19
Utilizing Language Relatedness to improve Machine Translation: A Case Study on Languages of the Indian Subcontinent ...
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20
Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages ...
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