mlsanityData to 14 Oct 2026

TILFA: A Unified Framework for Text, Image, and Layout Fusion in Argument Mining

arXiv 2310.05210 ↗First posted 8 Oct 2023cs.AI cs.CLcited by 3

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Abstract (arXiv)

A main goal of Argument Mining (AM) is to analyze an author's stance. Unlike previous AM datasets focusing only on text, the shared task at the 10th Workshop on Argument Mining introduces a dataset including both text and images. Importantly, these images contain both visual elements and optical characters. Our new framework, TILFA (A Unified Framework for Text, Image, and Layout Fusion in Argument Mining), is designed to handle this mixed data. It excels at not only understanding text but also detecting optical characters and recognizing layout details in images. Our model significantly outperforms existing baselines, earning our team, KnowComp, the 1st place in the leaderboard of Argumentative Stance Classification subtask in this shared task.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 23 of its 32 references that are in the map. First posted on arXiv 8 Oct 2023.

Builds on

23 of 32 references.

References · 23

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11 more
  1. 2023CAR: Conceptualization-Augmented Reasoner for Zero-Shot Commonsense Question Answeringcited by 28
  2. 2023CAT: A Contextualized Conceptualization and Instantiation Framework for Commonsense Reasoningcited by 25
  3. 2023COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspectivecited by 22
  4. 2023Incomplete Multi-View Multi-Label Learning via Label-Guided Masked View- and Category-Aware Transformerscited by 17
  5. 2022ROCK: Causal Inference Principles for Reasoning about Commonsense Causalitycited by 16
  6. 2023Getting Sick After Seeing a Doctor? Diagnosing and Mitigating Knowledge Conflicts in Event Temporal Reasoningcited by 13
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  8. 2023A Multi-Modal Context Reasoning Approach for Conditional Inference on Joint Textual and Visual Cluescited by 11
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Cited by year

Citing papers per year: 2024: 3.

3 papers.

  1. 2024MIND: Multimodal Shopping Intention Distillation from Large Vision-language Models for E-commerce Purchase Understandingcited by 10
  2. 2024ComparisonQA: Evaluating Factuality Robustness of LLMs Through Knowledge Frequency Control and Uncertaintycited by 10
  3. 2024KNOWCOMP POKEMON Team at DialAM-2024: A Two-Stage Pipeline for Detecting Relations in Dialogical Argument Miningcited by 2