Unified Language Representation for Question Answering over Text, Tables, and Images
This addresses the challenge of cross-modal reasoning for question answering systems, offering a more flexible and data-efficient solution.
The paper tackles the problem of answering complex questions using multiple sources like text, tables, and images by proposing a unified language representation approach, which outperforms existing methods by 10.6-32.3 points on datasets such as MultimodalQA and MMCoQA.
When trying to answer complex questions, people often rely on multiple sources of information, such as visual, textual, and tabular data. Previous approaches to this problem have focused on designing input features or model structure in the multi-modal space, which is inflexible for cross-modal reasoning or data-efficient training. In this paper, we call for an alternative paradigm, which transforms the images and tables into unified language representations, so that we can simplify the task into a simpler textual QA problem that can be solved using three steps: retrieval, ranking, and generation, all within a language space. This idea takes advantage of the power of pre-trained language models and is implemented in a framework called Solar. Our experimental results show that Solar outperforms all existing methods by 10.6-32.3 pts on two datasets, MultimodalQA and MMCoQA, across ten different metrics. Additionally, Solar achieves the best performance on the WebQA leaderboard