# Which One Are You Referring To? Multimodal Object Identification in Situated Dialogue

## TL;DR

This paper investigates multimodal object identification in situated dialogue, proposing three methods evaluated on the SIMMC 2.1 dataset, with the best method improving F1-score by approximately 20%.

## Contribution

It introduces and evaluates three novel methods for multimodal object identification in situated dialogue, with scene-dialogue alignment being the most effective.

## Key findings

- Scene-dialogue alignment improves performance by ~20% F1-score.
- The methods are evaluated on the largest situated dialogue dataset, SIMMC 2.1.
- Analysis highlights limitations and future directions for multimodal dialogue systems.

## Abstract

The demand for multimodal dialogue systems has been rising in various domains, emphasizing the importance of interpreting multimodal inputs from conversational and situational contexts. We explore three methods to tackle this problem and evaluate them on the largest situated dialogue dataset, SIMMC 2.1. Our best method, scene-dialogue alignment, improves the performance by ~20% F1-score compared to the SIMMC 2.1 baselines. We provide analysis and discussion regarding the limitation of our methods and the potential directions for future works. Our code is publicly available at https://github.com/holylovenia/multimodal-object-identification.