TL;DR
SRQA introduces a multi-evidence question answering model that improves evidence representation, reasoning, and robustness through multilayer attention, cross evidence strategy, and adversarial training, achieving state-of-the-art results.
Contribution
The paper presents SRQA, a novel model combining multilayer attention, cross evidence reasoning, and adversarial training for enhanced factoid question answering across multiple evidences.
Findings
Outperforms state-of-the-art models on WebQA dataset.
Achieves a fuzzy score of up to 78.56%.
Demonstrates improved reasoning and noise robustness.
Abstract
The question answering system can answer questions from various fields and forms with deep neural networks, but it still lacks effective ways when facing multiple evidences. We introduce a new model called SRQA, which means Synthetic Reader for Factoid Question Answering. This model enhances the question answering system in the multi-document scenario from three aspects: model structure, optimization goal, and training method, corresponding to Multilayer Attention (MA), Cross Evidence (CE), and Adversarial Training (AT) respectively. First, we propose a multilayer attention network to obtain a better representation of the evidences. The multilayer attention mechanism conducts interaction between the question and the passage within each layer, making the token representation of evidences in each layer takes the requirement of the question into account. Second, we design a cross evidence…
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