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        <title>Survey on Computational Approaches to Implicature</title>
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        <namePart type="given">Kaveri</namePart>
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        <namePart type="given">Srihitha</namePart>
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        <namePart type="given">Sriharshitha</namePart>
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    <name type="personal">
        <namePart type="given">Amit</namePart>
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    <name type="personal">
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            <namePart type="given">Karunesh</namePart>
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    <abstract>This paper explores the concept of solving implicature in Natural Language Processing (NLP), highlighting its significance in understanding indirect communication. Drawing on foundational theories by Austin, Searle, and Grice, we discuss how implicature extends beyond literal language to convey nuanced meanings. We review existing datasets, including the Pragmatic Understanding Benchmark (PUB), that assess models’ capabilities in recognizing and interpreting implicatures. Despite recent advances in large language models (LLMs), challenges remain in effectively processing implicature due to limitations in training data and the complexities of contextual interpretation. We propose future directions for research, including the enhancement of datasets and the integration of pragmatic reasoning tasks, to improve LLMs’ understanding of implicature and facilitate better human-computer interaction.</abstract>
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%0 Conference Proceedings
%T Survey on Computational Approaches to Implicature
%A Anuranjana, Kaveri
%A Mallepally, Srihitha
%A Mareddy, Sriharshitha
%A Shukla, Amit
%A Mamidi, Radhika
%Y Lalitha Devi, Sobha
%Y Arora, Karunesh
%S Proceedings of the 21st International Conference on Natural Language Processing (ICON)
%D 2024
%8 December
%I NLP Association of India (NLPAI)
%C AU-KBC Research Centre, Chennai, India
%F anuranjana-etal-2024-survey
%X This paper explores the concept of solving implicature in Natural Language Processing (NLP), highlighting its significance in understanding indirect communication. Drawing on foundational theories by Austin, Searle, and Grice, we discuss how implicature extends beyond literal language to convey nuanced meanings. We review existing datasets, including the Pragmatic Understanding Benchmark (PUB), that assess models’ capabilities in recognizing and interpreting implicatures. Despite recent advances in large language models (LLMs), challenges remain in effectively processing implicature due to limitations in training data and the complexities of contextual interpretation. We propose future directions for research, including the enhancement of datasets and the integration of pragmatic reasoning tasks, to improve LLMs’ understanding of implicature and facilitate better human-computer interaction.
%U https://aclanthology.org/2024.icon-1.25/
%P 224-229

Markdown (Informal)

[Survey on Computational Approaches to Implicature](https://aclanthology.org/2024.icon-1.25/) (Anuranjana et al., ICON 2024)

• Survey on Computational Approaches to Implicature (Anuranjana et al., ICON 2024)

ACL

• Kaveri Anuranjana, Srihitha Mallepally, Sriharshitha Mareddy, Amit Shukla, and Radhika Mamidi. 2024. Survey on Computational Approaches to Implicature. In Proceedings of the 21st International Conference on Natural Language Processing (ICON), pages 224–229, AU-KBC Research Centre, Chennai, India. NLP Association of India (NLPAI).