A Simple Approach to Jointly Rank Passages and Select Relevant Sentences in the OBQA Context
This work addresses the need for more efficient and integrated models in open book question answering, though it is incremental as it builds on existing tasks and datasets.
The paper tackles the problem of jointly ranking passages and selecting relevant sentences in open book question answering, achieving a 28% improvement in exact matching of relevant sentences on the HotpotQA dataset compared to baselines.
In the open book question answering (OBQA) task, selecting the relevant passages and sentences from distracting information is crucial to reason the answer to a question. HotpotQA dataset is designed to teach and evaluate systems to do both passage ranking and sentence selection. Many existing frameworks use separate models to select relevant passages and sentences respectively. Such systems not only have high complexity in terms of the parameters of models but also fail to take the advantage of training these two tasks together since one task can be beneficial for the other one. In this work, we present a simple yet effective framework to address these limitations by jointly ranking passages and selecting sentences. Furthermore, we propose consistency and similarity constraints to promote the correlation and interaction between passage ranking and sentence selection.The experiments demonstrate that our framework can achieve competitive results with previous systems and outperform the baseline by 28\% in terms of exact matching of relevant sentences on the HotpotQA dataset.