Reasoning over Object Descriptions Improves Coreference Resolution in Task-Based Dialogue Systems

Problem
This preprint addresses the limitations in coreference resolution within task-based dialogue systems, particularly in visually grounded environments. Existing models struggle with generalization across domains and often overfit to specific datasets, leading to poor performance in real-world applications. The authors identify a gap in leveraging detailed object metadata and dialogue history effectively to enhance coreference resolution, which is crucial for accurately linking user queries to the relevant objects in complex scenes.

Method
The authors propose a unimodal test-time reasoning approach that utilizes large language models (LLMs) to perform reasoning over object descriptions and dialogue history. This method involves generating step-by-step reasoning processes that align dialogue context with the objects present in the scene. The architecture leverages LLMs, although specific model details and training compute are not disclosed. The approach is evaluated on the SIMMC 2.1 dataset, which contains task-oriented dialogues in visually rich environments. The authors emphasize the importance of structured metadata and prompt engineering in their methodology, allowing the model to operate effectively under few-shot conditions and generalize to unseen scenarios.

Results
Empirical evaluations demonstrate that the proposed reasoning approach significantly outperforms traditional encoder-based supervised methods in coreference resolution tasks. The authors report substantial improvements in accuracy on the SIMMC 2.1 dataset, although specific numerical results and effect sizes are not detailed in the summary provided. The findings indicate that the LLMs, when equipped with reasoning capabilities, can accurately link conversations to objects, showcasing enhanced performance in cross-domain evaluations compared to baseline models.

Limitations
The authors acknowledge that their approach may still be limited by the inherent biases present in the training data and the reliance on LLMs, which can introduce variability based on prompt design. Additionally, the study does not explore the scalability of the reasoning approach across larger datasets or more complex dialogue scenarios. The lack of detailed quantitative results in the summary also limits the ability to fully assess the performance improvements claimed.

Why it matters
This work has significant implications for the development of more robust task-oriented dialogue systems, particularly in applications requiring accurate object reference resolution in dynamic environments. By demonstrating the effectiveness of reasoning over object descriptions, the authors provide a pathway for future research to explore the integration of structured metadata and reasoning capabilities in dialogue systems. This could lead to advancements in user interaction quality and system reliability, ultimately enhancing the user experience in various applications, including virtual assistants and customer service bots.

Authors: Oier Ijurco, Oier Lopez de Lacalle
Source: arXiv:2604.27850
URL: https://arxiv.org/abs/2604.27850v1