Open-Domain Conversational Search Assistant with Transformers
This addresses the problem of building more effective conversational assistants for users needing open-domain information, though it is incremental as it builds on existing Transformer methods.
The paper tackled open-domain conversational search by using Transformers to improve context-aware retrieval and abstractive answer generation, achieving state-of-the-art results that outperformed the TREC CAsT 2019 baseline.
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.