{"type":"ScholarlyArticle","title":"Resolving Word Vagueness with Scenario-guided Adapter for Natural Language Inference","authors":[{"name":"Yonghao Liu","slug":"yonghao-liu","given":"Yonghao","family":"Liu","affiliation":"Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education , College of Computer Science and Technology , Jilin University"},{"name":"Mengyu Li","slug":"mengyu-li","given":"Mengyu","family":"Li","affiliation":"Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education , College of Computer Science and Technology , Jilin University"},{"name":"Di Liang","slug":"di-liang","given":"Di","family":"Liang","affiliation":"Fudan University"},{"name":"Ximing Li","slug":"ximing-li","given":"Ximing","family":"Li","affiliation":"Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education , College of Computer Science and Technology , Jilin University"},{"name":"Fausto Giunchiglia","slug":"fausto-giunchiglia","given":"Fausto","family":"Giunchiglia","affiliation":"University of Trento"},{"name":"Lan Huang","slug":"lan-huang","given":"Lan","family":"Huang","affiliation":"Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education , College of Computer Science and Technology , Jilin University"},{"name":"Xiaoyue Feng","slug":"xiaoyue-feng","given":"Xiaoyue","family":"Feng","affiliation":"Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education , College of Computer Science and Technology , Jilin University"},{"name":"Renchu Guan","slug":"renchu-guan","given":"Renchu","family":"Guan","affiliation":"Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education , College of Computer Science and Technology , Jilin University"}],"abstract":"Natural Language Inference (NLI) is a crucial task in natural language processing that involves determining the relationship between two sentences, typically referred to as the premise and the hypothesis. However, traditional NLI models solely rely on the semantic information inherent in independent sentences and lack relevant situational visual information, which can hinder a complete understanding of the intended meaning of the sentences due to the ambiguity and vagueness of language. To address this challenge, we propose an innovative ScenaFuse adapter that simultaneously integrates large-scale pre-trained linguistic knowledge and relevant visual information for NLI tasks. Specifically, we first design an image-sentence interaction module to incorporate visuals into the attention mechanism of the pre-trained model, allowing the two modalities to interact comprehensively. Furthermore, we introduce an image-sentence fusion module that can adaptively integrate visual information from images and semantic information from sentences. By incorporating relevant visual information and leveraging linguistic knowledge, our approach bridges the gap between language and vision, leading to improved understanding and inference capabilities in NLI tasks. Extensive benchmark experiments demonstrate that our proposed ScenaFuse, a scenario-guided approach, consistently boosts NLI performance.","doi":"10.24963/ijcai.2024/509","url":"https://ijcai.org/proceedings/2024/509","pdf":"https://ijcai.org/proceedings/2024/0509.pdf","markdown":"https://ijcai.org/proceedings/2024/509.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/509","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":509,"pages":{"first":4605,"last":4613},"keywords":["Data Mining: DM: Mining text, web, social media","Knowledge Representation and Reasoning: KRR: Learning and reasoning","Machine Learning: ML: Multi-modal learning"],"videos":[],"references":[{"position":1,"raw":"[Antol et al., 2015] Stanislaw Antol, Aishwarya Agrawal, Ji- asen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zit- nick, and Devi Parikh. 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