A Context-aware Attention Network for Interactive Question Answering

TL;DR

This paper introduces a novel context-aware attention network for interactive question answering that effectively handles incomplete and ambiguous information by dynamically focusing on relevant context and user feedback.

Contribution

The paper proposes a new attention-based model for IQA that incorporates context-dependent and question-guided attention mechanisms, and creates new datasets for evaluation.

Findings

Significant improvement over state-of-the-art QA models.

Effective handling of incomplete and ambiguous information.

Model can decide when to answer or generate a follow-up question.

Abstract

Neural network based sequence-to-sequence models in an encoder-decoder framework have been successfully applied to solve Question Answering (QA) problems, predicting answers from statements and questions. However, almost all previous models have failed to consider detailed context information and unknown states under which systems do not have enough information to answer given questions. These scenarios with incomplete or ambiguous information are very common in the setting of Interactive Question Answering (IQA). To address this challenge, we develop a novel model, employing context-dependent word-level attention for more accurate statement representations and question-guided sentence-level attention for better context modeling. We also generate unique IQA datasets to test our model, which will be made publicly available. Employing these attention mechanisms, our model accurately understands when it can output an answer or when it requires generating a supplementary question for additional input depending on different contexts. When available, user's feedback is encoded and directly applied to update sentence-level attention to infer an answer. Extensive experiments on QA and IQA datasets quantitatively demonstrate the effectiveness of our model with significant improvement over state-of-the-art conventional QA models.