Evaluating Mixed-initiative Conversational Search Systems via User Simulation

• URL: http://arxiv.org/abs/2204.08046v2
• Date: Wed, 20 Apr 2022 09:33:46 GMT
• Title: Evaluating Mixed-initiative Conversational Search Systems via User Simulation
• Authors: Ivan Sekuli\'c, Mohammad Aliannejadi, Fabio Crestani
• Abstract summary: 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.
• Score: 9.066817876491053
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Clarifying the underlying user information need by asking clarifying questions is an important feature of modern conversational search system. However, evaluation of such systems through answering prompted clarifying questions requires significant human effort, which can be time-consuming and expensive. In this paper, we propose a conversational User Simulator, called USi, for automatic evaluation of such conversational search systems. Given a description of an information need, USi is capable of automatically answering clarifying questions about the topic throughout the search session. Through a set of experiments, including automated natural language generation metrics and crowdsourcing studies, we show that responses generated by USi are both inline with the underlying information need and comparable to human-generated answers. Moreover, we make the first steps towards multi-turn interactions, where conversational search systems asks multiple questions to the (simulated) user with a goal of clarifying the user need. To this end, we expand on currently available datasets for studying clarifying questions, i.e., Qulac and ClariQ, by performing a crowdsourcing-based multi-turn data acquisition. We show that our generative, GPT2-based model, is capable of providing accurate and natural answers to unseen clarifying questions in the single-turn setting and discuss capabilities of our model in the multi-turn setting. We provide the code, data, and the pre-trained model to be used for further research on the topic.

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)
• 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)
• What should I Ask: A Knowledge-driven Approach for Follow-up Questions Generation in Conversational Surveys [63.51903260461746]
  We propose a novel task for knowledge-driven follow-up question generation in conversational surveys. We constructed a new human-annotated dataset of human-written follow-up questions with dialogue history and labeled knowledge. We then propose a two-staged knowledge-driven model for the task, which generates informative and coherent follow-up questions.
  arXiv  Detail & Related papers  (2022-05-23T00:57:33Z)
• End-to-end Spoken Conversational Question Answering: Task, Dataset and Model [92.18621726802726]
  In spoken question answering, the systems are designed to answer questions from contiguous text spans within the related speech transcripts. We propose a new Spoken Conversational Question Answering task (SCQA), aiming at enabling the systems to model complex dialogue flows. Our main objective is to build the system to deal with conversational questions based on the audio recordings, and to explore the plausibility of providing more cues from different modalities with systems in information gathering.
  arXiv  Detail & Related papers  (2022-04-29T17:56:59Z)
• BERT-CoQAC: BERT-based Conversational Question Answering in Context [10.811729691130349]
  We introduce a framework based on a publically available pre-trained language model called BERT for incorporating history turns into the system. Experiment results revealed that our framework is comparable in performance with the state-of-the-art models on the QuAC leader board.
  arXiv  Detail & Related papers  (2021-04-23T03:05:17Z)
• 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)
• Towards Data Distillation for End-to-end Spoken Conversational Question Answering [65.124088336738]
  We propose a new Spoken Conversational Question Answering task (SCQA) SCQA aims at enabling QA systems to model complex dialogues flow given the speech utterances and text corpora. Our main objective is to build a QA system to deal with conversational questions both in spoken and text forms.
  arXiv  Detail & Related papers  (2020-10-18T05:53:39Z)
• 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)

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.