Papers with text-based approaches

5 papers
Dialogue Act-based Breakdown Detection in Negotiation Dialogues (2021.eacl-main)

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Challenge: Recent studies have succeeded in modeling a negotiating agent in natural language that can control both text generation and reasoning in goal-oriented dialogue systems.
Approach: They propose a human-human negotiation dialogue dataset that features increased complexities in terms of the number of possible solutions and a utility function.
Outcome: The proposed method performs comparable to text-based approaches in existing corpora and better results in the proposed dataset.
Multimodal Conversation Modelling for Topic Derailment Detection (2022.findings-emnlp)

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Challenge: Existing work on analysing textual dialogues that derailed into toxic content ignores visual information, such as images and videos.
Approach: They propose a new multimodal conversational architecture that utilises visual and conversational contexts to classify comments for derailment.
Outcome: The proposed approach outperforms existing methods and is more robust to textual noise.
Beyond Transcripts: A Renewed Perspective on Audio Chaptering (2026.acl-long)

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Challenge: despite its relevance, research on audio chaptering remains limited and predominantly textbased . authors: audio chapterers can't be used linearly because they skim, scrub timelines, jump to relevant moments . acoustic features and learning representations are not used for audio chapterer evaluation .
Approach: They propose to use audio-only architecture to automatically segment audio into coherent sections . they compare audio-based models with acoustic features and a novel audio-oriented architecture .
Outcome: The proposed audio-only architecture outperforms text-based approaches on acoustic features and LLMs.
Incorporating Object-Level Visual Context for Multimodal Fine-Grained Entity Typing (2023.findings-emnlp)

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Challenge: Experimental results show that fine-grained entity typing is superior to text-based methods.
Approach: They propose a task called fine-grained entity typing to classify entities . they propose combining textual and visual contexts to capture fine-granular semantic information .
Outcome: The proposed approach achieves superior classification performance compared to previous text-based approaches.
Unveil: Unified Visual-Textual Integration and Distillation for Multi-modal Document Retrieval (2025.acl-long)

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Challenge: Document retrieval in real-world scenarios faces significant challenges due to diverse document formats and modalities.
Approach: They propose a visual-textual embedding framework that integrates textual and visual features for robust document representation.
Outcome: The proposed visual-textual embedding framework surpasses existing methods while preserving semantic fidelity.

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