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        <title>Identifying Statements Crucial for Awareness of Interpretive Nonsense to Prevent Communication Breakdowns</title>
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    <abstract>During remote conversations, communication breakdowns often occur when a listener misses certain statements. Our objective is to prevent such breakdowns by identifying Statements Crucial for Awareness of Interpretive Nonsense (SCAINs). If a listener misses a SCAIN, s/he may interpret subsequent statements differently from the speaker’s intended meaning. To identify SCAINs, we adopt a unique approach where we create a dialogue by omitting two consecutive statements from the original dialogue and then generate text to make the following statement more specific. The novelty of the proposed method lies in simulating missing information by processing text with omissions. We validate the effectiveness of SCAINs through evaluation using a dialogue dataset. Furthermore, we demonstrate that SCAINs cannot be identified as merely important statements, highlighting the uniqueness of our proposed method.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.773</identifier>
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%0 Conference Proceedings
%T Identifying Statements Crucial for Awareness of Interpretive Nonsense to Prevent Communication Breakdowns
%A Maekawa, Tomoyuki
%A Imai, Michita
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F maekawa-imai-2023-identifying
%X During remote conversations, communication breakdowns often occur when a listener misses certain statements. Our objective is to prevent such breakdowns by identifying Statements Crucial for Awareness of Interpretive Nonsense (SCAINs). If a listener misses a SCAIN, s/he may interpret subsequent statements differently from the speaker’s intended meaning. To identify SCAINs, we adopt a unique approach where we create a dialogue by omitting two consecutive statements from the original dialogue and then generate text to make the following statement more specific. The novelty of the proposed method lies in simulating missing information by processing text with omissions. We validate the effectiveness of SCAINs through evaluation using a dialogue dataset. Furthermore, we demonstrate that SCAINs cannot be identified as merely important statements, highlighting the uniqueness of our proposed method.
%R 10.18653/v1/2023.emnlp-main.773
%U https://aclanthology.org/2023.emnlp-main.773
%U https://doi.org/10.18653/v1/2023.emnlp-main.773
%P 12550-12566

Markdown (Informal)

[Identifying Statements Crucial for Awareness of Interpretive Nonsense to Prevent Communication Breakdowns](https://aclanthology.org/2023.emnlp-main.773) (Maekawa & Imai, EMNLP 2023)

• Identifying Statements Crucial for Awareness of Interpretive Nonsense to Prevent Communication Breakdowns (Maekawa & Imai, EMNLP 2023)

ACL

• Tomoyuki Maekawa and Michita Imai. 2023. Identifying Statements Crucial for Awareness of Interpretive Nonsense to Prevent Communication Breakdowns. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 12550–12566, Singapore. Association for Computational Linguistics.