A BERT-based model with position-aware history answer embeddings and a learned history attention mechanism improves QuAC F1 by about one point over strong baselines, but multi-task learning with dialog acts does not help answer span prediction.
BERT with History Answer Embedding for Conversational Question Answering
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abstract
Conversational search is an emerging topic in the information retrieval community. One of the major challenges to multi-turn conversational search is to model the conversation history to answer the current question. Existing methods either prepend history turns to the current question or use complicated attention mechanisms to model the history. We propose a conceptually simple yet highly effective approach referred to as history answer embedding. It enables seamless integration of conversation history into a conversational question answering (ConvQA) model built on BERT (Bidirectional Encoder Representations from Transformers). We first explain our view that ConvQA is a simplified but concrete setting of conversational search, and then we provide a general framework to solve ConvQA. We further demonstrate the effectiveness of our approach under this framework. Finally, we analyze the impact of different numbers of history turns under different settings to provide new insights into conversation history modeling in ConvQA.
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Attentive History Selection for Conversational Question Answering
A BERT-based model with position-aware history answer embeddings and a learned history attention mechanism improves QuAC F1 by about one point over strong baselines, but multi-task learning with dialog acts does not help answer span prediction.