A Context-aware Attention Network for Interactive Qestion Answering

Huayu Li, Martin Renqiang Min, Yong Ge, Asim Kadav

Abstract:

Neural network based sequence-to-sequence models in an encoder-to-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.