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TV-TREES: Multimodal Entailment Trees for Neuro-Symbolic Video Reasoning (2402.19467v4)

Published 29 Feb 2024 in cs.CL, cs.AI, and cs.CV

Abstract: It is challenging for models to understand complex, multimodal content such as television clips, and this is in part because video-LLMs often rely on single-modality reasoning and lack interpretability. To combat these issues we propose TV-TREES, the first multimodal entailment tree generator. TV-TREES serves as an approach to video understanding that promotes interpretable joint-modality reasoning by searching for trees of entailment relationships between simple text-video evidence and higher-level conclusions that prove question-answer pairs. We also introduce the task of multimodal entailment tree generation to evaluate reasoning quality. Our method's performance on the challenging TVQA benchmark demonstrates interpretable, state-of-the-art zero-shot performance on full clips, illustrating that multimodal entailment tree generation can be a best-of-both-worlds alternative to black-box systems.

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Authors (3)
  1. Kate Sanders (19 papers)
  2. Nathaniel Weir (17 papers)
  3. Benjamin Van Durme (173 papers)
Citations (6)
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Summary

  • The paper introduces the TV-TREES framework, a novel multimodal entailment tree generator that combines visual and textual evidence for enhanced video reasoning.
  • It formulates a new task for multimodal entailment tree generation, demonstrating competitive zero-shot performance and improved interpretability.
  • The approach uses evidence retrieval, filtering, and recursive hypothesis decomposition to achieve scalable, transparent, and human-understandable reasoning.

TV-TREES: Multimodal Entailment Trees for Neuro-Symbolic Video Reasoning

Introduction

The paper "TV-TREES: Multimodal Entailment Trees for Neuro-Symbolic Video Reasoning" by Sanders, Weir, and Van Durme focuses on addressing the complexities and challenges of multi-modal video question answering (VideoQA). Traditional video-LLMs exhibit limitations in terms of single-modality reasoning, diminished performance with extended inputs, and a lack of interpretability. The authors introduce TV-TREES, the first multimodal entailment tree generator, designed to improve interpretability and facilitate joint-modality reasoning. Their framework is evaluated via the novel task of multimodal entailment tree generation, achieving state-of-the-art zero-shot performance on the TVQA dataset.

Contributions

The contributions of the paper are threefold:

  1. Multimodal Entailment Tree Generator (TV-TREES): The development of the first multimodal tree generator that produces entailment relationships from both visual and textual data.
  2. Task Introduction: The introduction and formulation of the multimodal entailment tree generation task to evaluate reasoning quality.
  3. Experimental Validation: Empirical evidence demonstrating competitive performance on the TVQA benchmark, paired with interpretability advantages over conventional black-box models.

Problem and Motivation

Automated reasoning over video content is significantly under-explored. VideoQA, where systems answer questions based on video clips and their dialogue, presents substantial challenges especially with narrative-centric data like TV shows. Existing models, primarily large transformer-based architectures, struggle with scalability, joint modality reasoning, and achieving good performance without compromising interpretability. Neuromodality reasoning, breaking down complex narratives into simpler, explainable logical steps, is proposed as an effective strategy to address these issues.

Methodology

The TV-TREES methodology hinges on three core processes:

  1. Evidence Retrieval: Given a hypothesis derived from question-answer pairs, the system retrieves relevant evidence from the video or corresponding dialogue transcript. It emphasizes contextualizing the given inputs to discern pertinent data from the entire clip effectively.
  2. Evidence Filtering: The retrieved evidence is then filtered for its relevance and sufficiency in proving the hypothesis using techniques grounded in natural language and visual entailment models.
  3. Hypothesis Decomposition: For hypotheses that can't be directly proven from retrieved evidence, decomposition into sub-hypotheses occurs recursively until atomic, directly provable facts are obtained.

These processes are integrated into an algorithm that constructs a proof tree, which visually and logically represents the entailment pathway from premises to the conclusion.

Evaluation

The paper proposes an evaluation paradigm rooted in informal logic theory, assessing three key qualia for entailment trees:

  1. Acceptability: Verifiability and coherence of node statements.
  2. Relevance: Conditional relevance of premises to their parent node.
  3. Sufficiency: Whether combined premises collectively entail the hypothesis.

These metrics are quantified via a scoring mechanism reflecting logical correctness and completeness.

Empirical Results

The TV-TREES framework was validated using the TVQA dataset. Results indicated:

  • State-of-the-Art Zero-Shot Performance: TV-TREES exceeded the performance of existing zero-shot models.
  • Interpretable Reasoning Traces: Unlike other models, TV-TREES produced human-understandable reasoning, highlighting how each conclusion was drawn from evidence.
  • Robustness: Improved performance on full-length video inputs confirmed the system's scalability and efficiency in handling extended content.

Discussion: Implications and Future Directions

Practical Implications:

TV-TREES shows potential for real-world applications in domains like video summarization, surveillance, and educational tools where understanding video content through natural language is crucial. The interpretability aspect is particularly beneficial in scenarios demanding transparency and accountability in AI decision-making.

Theoretical Implications:

The introduction of entailment trees in multi-modal contexts underscores the feasibility of neuro-symbolic reasoning in understanding and processing video content. This paves the way for further exploration into hybrid models combining symbolic AI with deep learning.

Future Work:

  • Enhanced Visual Modules: Future research could improve visual recognition aspects, potentially utilizing models trained on more comprehensive datasets.
  • Extended Contextual Understanding: Expanding the immediate context considered in visual inference could lead to better accuracy.
  • Broader Domain Applications: Employing TV-TREES in diverse domains, especially low-dialogue or highly dynamic environments, to test its generalizability.

Conclusion

This paper makes a significant step towards advanced video understanding through TV-TREES. The novel approach of multimodal entailment tree generation brings together robust reasoning performance and human-like interpretability, setting a new benchmark for video question-answering systems. By aligning machine inference processes more closely with human logical reasoning, TV-TREES opens new avenues for AI applications requiring nuanced understanding and transparency.

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