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Resolving Word Vagueness with Scenario-guided Adapter for Natural Language Inference

Yonghao Liu, Mengyu Li, Di Liang, Ximing Li, Fausto Giunchiglia, Lan Huang, Xiaoyue Feng, Renchu Guan

TL;DR

ScenaFuse addresses word vagueness in natural language inference by integrating scenario visuals into pre-trained language models via a scenario-guided adapter. It introduces an image-sentence interaction module to enrich sentence semantics and an image-sentence fusion module with gating to adaptively fuse visual and textual features. Experiments on SNLI variants show consistent gains, with ablations confirming the crucial roles of interaction and fusion components and compatibility with large language models. The work demonstrates that explicit visual context can disambiguate textual meaning in NLI, enabling more robust multimodal reasoning in NLP.

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.

Resolving Word Vagueness with Scenario-guided Adapter for Natural Language Inference

TL;DR

ScenaFuse addresses word vagueness in natural language inference by integrating scenario visuals into pre-trained language models via a scenario-guided adapter. It introduces an image-sentence interaction module to enrich sentence semantics and an image-sentence fusion module with gating to adaptively fuse visual and textual features. Experiments on SNLI variants show consistent gains, with ablations confirming the crucial roles of interaction and fusion components and compatibility with large language models. The work demonstrates that explicit visual context can disambiguate textual meaning in NLI, enabling more robust multimodal reasoning in NLP.

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.
Paper Structure (20 sections, 8 equations, 2 figures, 4 tables)

This paper contains 20 sections, 8 equations, 2 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Natural Language Inference
  4. Multimodal Learning
  5. Problem Definition
  6. Method
  7. Image-sentence Interaction
  8. Visual-enhanced Sentence Representation.
  9. Sentence-rectified Visual Representation.
  10. Image-sentence Fusion
  11. Model Training
  12. Experiment
  13. Dataset.
  14. Baselines.
  15. Implementation Details.
  16. ...and 5 more sections

Figures (2)

  • Figure 1: The two sentences have an entailment relationship when viewed in the context of image (A), but a contradiction relationship when viewed in the context of image (B).
  • Figure 2: (Left) Overall architecture of our framework. (Right) The architecture of the fusion module. (Best viewed in color)