2021/01/20 by Rafael Ferreira, Mariana Leite, Ferreira, Rafael +5
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #H.3.3 #I.2.7 #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #cs.CL #cs.IR #cs.LG
paper · pdf · doi:10.48550/arxiv.2101.08197
arxiv created 2021/01/20 · arxiv updated 2021/01/21
Open-domain conversational search assistants aim at answering user questions about open topics in a conversational manner. In this paper we show how the Transformer architecture achieves state-of-the-art results in key IR tasks, leveraging the creation of conversational assistants that engage in open-domain conversational search with single, yet informative, answers. In particular, we propose an open-domain abstractive conversational search agent pipeline to address two major challenges: first, conversation context-aware search and second, abstractive search-answers generation. To address the first challenge, the conversation context is modeled with a query rewriting method that unfolds the context of the conversation up to a specific moment to search for the correct answers. These answers are then passed to a Transformer-based re-ranker to further improve retrieval performance. The second challenge, is tackled with recent Abstractive Transformer architectures to generate a digest of the top most relevant passages. Experiments show that Transformers deliver a solid performance across all tasks in conversational search, outperforming the best TREC CAsT 2019 baseline.