Guided Transformer: Leveraging Multiple External Sources for Representation Learning in Conversational Search

• URL: http://arxiv.org/abs/2006.07548v1
• Date: Sat, 13 Jun 2020 03:24:53 GMT
• Title: Guided Transformer: Leveraging Multiple External Sources for Representation Learning in Conversational Search
• Authors: Helia Hashemi, Hamed Zamani, W. Bruce Croft
• Abstract summary: Asking clarifying questions in response to ambiguous or faceted queries has been recognized as a useful technique for various information retrieval systems. In this paper, we enrich the representations learned by Transformer networks using a novel attention mechanism from external information sources. Our experiments use a public dataset for search clarification and demonstrate significant improvements compared to competitive baselines.
• Score: 36.64582291809485
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Asking clarifying questions in response to ambiguous or faceted queries has been recognized as a useful technique for various information retrieval systems, especially conversational search systems with limited bandwidth interfaces. Analyzing and generating clarifying questions have been studied recently but the accurate utilization of user responses to clarifying questions has been relatively less explored. In this paper, we enrich the representations learned by Transformer networks using a novel attention mechanism from external information sources that weights each term in the conversation. We evaluate this Guided Transformer model in a conversational search scenario that includes clarifying questions. In our experiments, we use two separate external sources, including the top retrieved documents and a set of different possible clarifying questions for the query. We implement the proposed representation learning model for two downstream tasks in conversational search; document retrieval and next clarifying question selection. Our experiments use a public dataset for search clarification and demonstrate significant improvements compared to competitive baselines.

Related papers

• CLARINET: Augmenting Language Models to Ask Clarification Questions for Retrieval [52.134133938779776]
  We present CLARINET, a system that asks informative clarification questions by choosing questions whose answers would maximize certainty in the correct candidate. Our approach works by augmenting a large language model (LLM) to condition on a retrieval distribution, finetuning end-to-end to generate the question that would have maximized the rank of the true candidate at each turn.
  arXiv  Detail & Related papers  (2024-04-28T18:21:31Z)
• PAQA: Toward ProActive Open-Retrieval Question Answering [34.883834970415734]
  This work aims to tackle the challenge of generating relevant clarifying questions by taking into account the inherent ambiguities present in both user queries and documents. We propose PAQA, an extension to the existing AmbiNQ dataset, incorporating clarifying questions. We then evaluate various models and assess how passage retrieval impacts ambiguity detection and the generation of clarifying questions.
  arXiv  Detail & Related papers  (2024-02-26T14:40:34Z)
• Asking Multimodal Clarifying Questions in Mixed-Initiative Conversational Search [89.1772985740272]
  In mixed-initiative conversational search systems, clarifying questions are used to help users who struggle to express their intentions in a single query. We hypothesize that in scenarios where multimodal information is pertinent, the clarification process can be improved by using non-textual information. We collect a dataset named Melon that contains over 4k multimodal clarifying questions, enriched with over 14k images. Several analyses are conducted to understand the importance of multimodal contents during the query clarification phase.
  arXiv  Detail & Related papers  (2024-02-12T16:04:01Z)
• DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text [73.68051228972024]
  Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge. Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
  arXiv  Detail & Related papers  (2023-10-31T04:37:57Z)
• Zero-shot Clarifying Question Generation for Conversational Search [25.514678546942754]
  We propose a constrained clarifying question generation system which uses both question templates and query facets to guide the effective and precise question generation. Experiment results show that our method outperforms existing state-of-the-art zero-shot baselines by a large margin.
  arXiv  Detail & Related papers  (2023-01-30T04:43:02Z)
• Evaluating Mixed-initiative Conversational Search Systems via User Simulation [9.066817876491053]
  We propose a conversational User Simulator, called USi, for automatic evaluation of such search systems. We show that responses generated by USi are both inline with the underlying information need and comparable to human-generated answers.
  arXiv  Detail & Related papers  (2022-04-17T16:27:33Z)
• Exposing Query Identification for Search Transparency [69.06545074617685]
  We explore the feasibility of approximate exposing query identification (EQI) as a retrieval task by reversing the role of queries and documents in two classes of search systems. We derive an evaluation metric to measure the quality of a ranking of exposing queries, as well as conducting an empirical analysis focusing on various practical aspects of approximate EQI.
  arXiv  Detail & Related papers  (2021-10-14T20:19:27Z)
• Open-Retrieval Conversational Machine Reading [80.13988353794586]
  In conversational machine reading, systems need to interpret natural language rules, answer high-level questions, and ask follow-up clarification questions. Existing works assume the rule text is provided for each user question, which neglects the essential retrieval step in real scenarios. In this work, we propose and investigate an open-retrieval setting of conversational machine reading.
  arXiv  Detail & Related papers  (2021-02-17T08:55:01Z)
• 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.