Zero-shot Clarifying Question Generation for Conversational Search

• URL: http://arxiv.org/abs/2301.12660v1
• Date: Mon, 30 Jan 2023 04:43:02 GMT
• Title: Zero-shot Clarifying Question Generation for Conversational Search
• Authors: Zhenduo Wang, Yuancheng Tu, Corby Rosset, Nick Craswell, Ming Wu, Qingyao Ai
• Abstract summary: 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.
• Score: 25.514678546942754
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A long-standing challenge for search and conversational assistants is query intention detection in ambiguous queries. Asking clarifying questions in conversational search has been widely studied and considered an effective solution to resolve query ambiguity. Existing work have explored various approaches for clarifying question ranking and generation. However, due to the lack of real conversational search data, they have to use artificial datasets for training, which limits their generalizability to real-world search scenarios. As a result, the industry has shown reluctance to implement them in reality, further suspending the availability of real conversational search interaction data. The above dilemma can be formulated as a cold start problem of clarifying question generation and conversational search in general. Furthermore, even if we do have large-scale conversational logs, it is not realistic to gather training data that can comprehensively cover all possible queries and topics in open-domain search scenarios. The risk of fitting bias when training a clarifying question retrieval/generation model on incomprehensive dataset is thus another important challenge. In this work, we innovatively explore generating clarifying questions in a zero-shot setting to overcome the cold start problem and we propose a constrained clarifying question generation system which uses both question templates and query facets to guide the effective and precise question generation. The experiment results show that our method outperforms existing state-of-the-art zero-shot baselines by a large margin. Human annotations to our model outputs also indicate our method generates 25.2\% more natural questions, 18.1\% more useful questions, 6.1\% less unnatural and 4\% less useless questions.

Related papers

• Analyzing Human Questioning Behavior and Causal Curiosity through Natural Queries [91.70689724416698]
  We present NatQuest, a collection of 13,500 naturally occurring questions from three diverse sources. Our analysis reveals a significant presence of causal questions (up to 42%) within the dataset.
  arXiv  Detail & Related papers  (2024-05-30T17:55:28Z)
• 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)
• Qsnail: A Questionnaire Dataset for Sequential Question Generation [76.616068047362]
  We present the first dataset specifically constructed for the questionnaire generation task, which comprises 13,168 human-written questionnaires. We conduct experiments on Qsnail, and the results reveal that retrieval models and traditional generative models do not fully align with the given research topic and intents. Despite enhancements through the chain-of-thought prompt and finetuning, questionnaires generated by language models still fall short of human-written questionnaires.
  arXiv  Detail & Related papers  (2024-02-22T04:14:10Z)
• 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)
• ZeQR: Zero-shot Query Reformulation for Conversational Search [11.644235288057123]
  We introduce a novel Zero-shot Query Reformulation (or Query Rewriting) framework that reformulates queries based on previous dialogue contexts without requiring supervision from conversational search data. Specifically, our framework utilizes language models designed for machine reading comprehension tasks to explicitly resolve two common ambiguities: coreference and omission, in raw queries. It also provides greater explainability and effectively enhances query intent understanding because ambiguities are explicitly and proactively resolved.
  arXiv  Detail & Related papers  (2023-07-18T16:05:25Z)
• 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)
• Inquisitive Question Generation for High Level Text Comprehension [60.21497846332531]
  We introduce INQUISITIVE, a dataset of 19K questions that are elicited while a person is reading through a document. We show that readers engage in a series of pragmatic strategies to seek information. We evaluate question generation models based on GPT-2 and show that our model is able to generate reasonable questions.
  arXiv  Detail & Related papers  (2020-10-04T19:03:39Z)
• Guided Transformer: Leveraging Multiple External Sources for Representation Learning in Conversational Search [36.64582291809485]
  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.
  arXiv  Detail & Related papers  (2020-06-13T03:24:53Z)
• Query Resolution for Conversational Search with Limited Supervision [63.131221660019776]
  We propose QuReTeC (Query Resolution by Term Classification), a neural query resolution model based on bidirectional transformers. We show that QuReTeC outperforms state-of-the-art models, and furthermore, that our distant supervision method can be used to substantially reduce the amount of human-curated data required to train QuReTeC.
  arXiv  Detail & Related papers  (2020-05-24T11:37:22Z)

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.