Generative retrieval for conversational question answering
Institution:
1. International College, Renmin University of China, No.58, Zhongguancun Street, Haidian District, Beijing 100872, China;2. School of Insurance, Guangdong University of Finance, 527 Yingfu Road, Tianhe District, Guangzhou Guangdong 510521, China;1. Information Research Institute of Qilu University of Technology (Shandong Academy of Sciences), Jinan, PR China;2. School of Management, Xi''an University of Architecture and Technology, Xi''an, PR China;3. School of Information and Control Engineering, Xi''an University of Architecture and Technology, Xi''an, PR China;4. University of Jinan, Jinan, PR China
Abstract:
Effective passage retrieval is crucial for conversation question answering (QA) but challenging due to the ambiguity of questions. Current methods rely on the dual-encoder architecture to embed contextualized vectors of questions in conversations. However, this architecture is limited in the embedding bottleneck and the dot-product operation. To alleviate these limitations, we propose generative retrieval for conversational QA (GCoQA). GCoQA assigns distinctive identifiers for passages and retrieves passages by generating their identifiers token-by-token via the encoder–decoder architecture. In this generative way, GCoQA eliminates the need for a vector-style index and could attend to crucial tokens of the conversation context at every decoding step. We conduct experiments on three public datasets over a corpus containing about twenty million passages. The results show GCoQA achieves relative improvements of +13.6% in passage retrieval and +42.9% in document retrieval. GCoQA is also efficient in terms of memory usage and inference speed, which only consumes 1/10 of the memory and takes in less than 33% of the time. The code and data are released at https://github.com/liyongqi67/GCoQA.
