Open-domain question classification and completion in conversational information search
arXiv:2102.13495 · doi:10.1109/IKT51791.2020.9345613
Abstract
Searching for new information requires talking to the system. In this research, an Open-domain Conversational information search system has been developed. This system has been implemented using the TREC CAsT 2019 track, which is one of the first attempts to build a framework in this area. According to the user's previous questions, the system firstly completes the question (using the first and the previous question in each turn) and then classifies it (based on the question words). This system extracts the related answers according to the rules of each question. In this research, a simple yet effective method with high performance has been used, which on average, extracts 20% more relevant results than the baseline.
References in corpus (6)
- Efficient Estimation of Word Representations in Vector Space
- ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
- Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems
- BERT with History Answer Embedding for Conversational Question Answering
- Simple Applications of BERT for Ad Hoc Document Retrieval
- Voice Search and Typed Search Performance Comparison on Baidu Search System