Knowledge-driven Answer Generation for Conversational Search

• URL: http://arxiv.org/abs/2104.06892v1
• Date: Wed, 14 Apr 2021 14:35:54 GMT
• Title: Knowledge-driven Answer Generation for Conversational Search
• Authors: Mariana Leite, Rafael Ferreira, David Semedo, Jo\~ao Magalh\~aes
• Abstract summary: We propose a knowledge-driven answer generation approach for open-domain conversational search. A conversation-wide entities' knowledge graph is used to bias search-answer generation. Experiments show that the proposed approach successfully exploits entities knowledge along the conversation.
• Score: 4.735500711531941
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The conversational search paradigm introduces a step change over the traditional search paradigm by allowing users to interact with search agents in a multi-turn and natural fashion. The conversation flows naturally and is usually centered around a target field of knowledge. In this work, we propose a knowledge-driven answer generation approach for open-domain conversational search, where a conversation-wide entities' knowledge graph is used to bias search-answer generation. First, a conversation-specific knowledge graph is extracted from the top passages retrieved with a Transformer-based re-ranker. The entities knowledge-graph is then used to bias a search-answer generator Transformer towards information rich and concise answers. This conversation specific bias is computed by identifying the most relevant passages according to the most salient entities of that particular conversation. Experiments show that the proposed approach successfully exploits entities knowledge along the conversation, and outperforms a set of baselines on the search-answer generation task.

Related papers

• Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations [66.16863141262506]
  We present a novel approach that focuses on generating internet search queries guided by social commonsense. Our proposed framework addresses passive user interactions by integrating topic tracking, commonsense response generation and instruction-driven query generation.
  arXiv  Detail & Related papers  (2023-10-22T16:14:56Z)
• From Data to Dialogue: Leveraging the Structure of Knowledge Graphs for Conversational Exploratory Search [4.861125297881693]
  We propose a knowledge-driven dialogue system for exploring news articles by asking natural language questions. Based on a user study with 54 participants, we empirically evaluate the effectiveness of the graph-based exploratory search.
  arXiv  Detail & Related papers  (2023-10-08T12:52:09Z)
• Search-Engine-augmented Dialogue Response Generation with Cheaply Supervised Query Production [98.98161995555485]
  We propose a dialogue model that can access the vast and dynamic information from any search engine for response generation. As the core module, a query producer is used to generate queries from a dialogue context to interact with a search engine. Experiments show that our query producer can achieve R@1 and R@5 rates of 62.4% and 74.8% for retrieving gold knowledge.
  arXiv  Detail & Related papers  (2023-02-16T01:58:10Z)
• Investigating Conversational Search Behavior For Domain Exploration [0.5512295869673147]
  This study investigates open-ended search behavior for navigation through unknown information landscapes. We apply statistical analyses and process mining techniques to uncover general information-seeking patterns across five different domains.
  arXiv  Detail & Related papers  (2023-01-10T17:43:03Z)
• Retrieval-Free Knowledge-Grounded Dialogue Response Generation with Adapters [52.725200145600624]
  We propose KnowExpert to bypass the retrieval process by injecting prior knowledge into the pre-trained language models with lightweight adapters. Experimental results show that KnowExpert performs comparably with the retrieval-based baselines.
  arXiv  Detail & Related papers  (2021-05-13T12:33:23Z)
• A Graph-guided Multi-round Retrieval Method for Conversational Open-domain Question Answering [52.041815783025186]
  We propose a novel graph-guided retrieval method to model the relations among answers across conversation turns. We also propose to incorporate the multi-round relevance feedback technique to explore the impact of the retrieval context on current question understanding.
  arXiv  Detail & Related papers  (2021-04-17T04:39:41Z)
• BERT Embeddings Can Track Context in Conversational Search [5.3222282321717955]
  We develop a conversational search system that helps people search for information in a natural way. System is able to understand the context where the question is posed, tracking the current state of the conversation and detecting mentions to previous questions and answers.
  arXiv  Detail & Related papers  (2021-04-13T22:02:24Z)
• Detecting and Classifying Malevolent Dialogue Responses: Taxonomy, Data and Methodology [68.8836704199096]
  Corpus-based conversational interfaces are able to generate more diverse and natural responses than template-based or retrieval-based agents. With their increased generative capacity of corpusbased conversational agents comes the need to classify and filter out malevolent responses. Previous studies on the topic of recognizing and classifying inappropriate content are mostly focused on a certain category of malevolence.
  arXiv  Detail & Related papers  (2020-08-21T22:43:27Z)
• Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting [56.268862325167575]
  We tackle conversational passage retrieval (ConvPR) with query reformulation integrated into a multi-stage ad-hoc IR system. We propose two conversational query reformulation (CQR) methods: (1) term importance estimation and (2) neural query rewriting. For the former, we expand conversational queries using important terms extracted from the conversational context with frequency-based signals. For the latter, we reformulate conversational queries into natural, standalone, human-understandable queries with a pretrained sequence-tosequence model.
  arXiv  Detail & Related papers  (2020-05-05T14:30:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.