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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 understand 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. We show that history prepending methods degrade dramatically when given a long conversation history while our method is robust and shows advantages under such a situation, which provides new insights into conversation history modeling in ConvQA.
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Attentive History Selection for Conversational Question Answering
Conversational AI is a rapidly developing research field in both industry and academia. As one of the major branches of conversational AI, question answering and conversational search has attracted significant attention of researchers in the information retrieval community. It has been a long overdue feature for search engines or conversational assistants to retrieve information iteratively and interactively in a conversational manner. Previous work argues that conversational question answering (ConvQA) is a simplified but concrete setting of conversational search. In this setting, one of the major challenges is to leverage the conversation history to understand and answer the current question. In this work, we propose a novel solution for ConvQA that involves three aspects. First, we propose a positional history answer embedding method to encode conversation history with position information using BERT (Bidirectional Encoder Representations from Transformers) in a natural way. BERT is a powerful technique for text representation. Second, we design a history attention mechanism (HAM) to conduct a "soft selection" for conversation histories. This method attends to history turns with different weights based on how helpful they are on answering the current question. Third, in addition to handling conversation history, we take advantage of multi-task learning (MTL) to do answer prediction along with another essential conversation task (dialog act prediction) using a uniform model architecture. MTL is able to learn more expressive and generic representations to improve the performance of ConvQA. We demonstrate the effectiveness of our model with extensive experimental evaluations on QuAC, a large-scale ConvQA dataset. We show that position information plays an important role in conversation history modeling. We also visualize the history attention and provide new insights into conversation history understanding. The complete implementation of our model will be open-sourced.
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- Award ID(s):
- 1715095
- PAR ID:
- 10143774
- Date Published:
- Journal Name:
- Proceedings of the 28th ACM International Conference on Information and Knowledge Management - CIKM '19
- Page Range / eLocation ID:
- 1391 to 1400
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3357384.3357905
