INSCIT: Information-Seeking Conversations with Mixed-Initiative Interactions

• URL: http://arxiv.org/abs/2207.00746v2
• Date: Thu, 22 Jun 2023 22:46:37 GMT
• Title: INSCIT: Information-Seeking Conversations with Mixed-Initiative Interactions
• Authors: Zeqiu Wu, Ryu Parish, Hao Cheng, Sewon Min, Prithviraj Ammanabrolu, Mari Ostendorf, Hannaneh Hajishirzi
• Abstract summary: InSCIt is a dataset for Information-Seeking Conversations with mixed-initiative Interactions. It contains 4.7K user-agent turns from 805 human-human conversations. We report results of two systems based on state-of-the-art models of conversational knowledge identification and open-domain question answering.
• Score: 47.90088587508672
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In an information-seeking conversation, a user may ask questions that are under-specified or unanswerable. An ideal agent would interact by initiating different response types according to the available knowledge sources. However, most current studies either fail to or artificially incorporate such agent-side initiative. This work presents InSCIt, a dataset for Information-Seeking Conversations with mixed-initiative Interactions. It contains 4.7K user-agent turns from 805 human-human conversations where the agent searches over Wikipedia and either directly answers, asks for clarification, or provides relevant information to address user queries. The data supports two subtasks, evidence passage identification and response generation, as well as a human evaluation protocol to assess model performance. We report results of two systems based on state-of-the-art models of conversational knowledge identification and open-domain question answering. Both systems significantly underperform humans, suggesting ample room for improvement in future studies.

Related papers

• Personalized Topic Selection Model for Topic-Grounded Dialogue [24.74527189182273]
  Current models tend to predict user-uninteresting and contextually irrelevant topics. We propose a textbfPersonalized topic stextbfElection model for textbfTopic-grounded textbfDialogue, named textbfPETD. Our proposed method can generate engaging and diverse responses, outperforming state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-06-04T06:09:49Z)
• ProCIS: A Benchmark for Proactive Retrieval in Conversations [21.23826888841565]
  We introduce a large-scale dataset for proactive document retrieval that consists of over 2.8 million conversations. We conduct crowdsourcing experiments to obtain high-quality and relatively complete relevance judgments. We also collect annotations related to the parts of the conversation that are related to each document, enabling us to evaluate proactive retrieval systems.
  arXiv  Detail & Related papers  (2024-05-10T13:11:07Z)
• R2H: Building Multimodal Navigation Helpers that Respond to Help Requests [30.695642371684663]
  We first introduce a novel benchmark, Respond to Help Requests (R2H), to promote the development of multi-modal navigation helpers. R2H mainly includes two tasks: (1) Respond to Dialog History (RDH), which assesses the helper agent's ability to generate informative responses based on a given dialog history, and (2) Respond during Interaction (RdI), which evaluates the effectiveness and efficiency of the response during consistent cooperation with a task performer.
  arXiv  Detail & Related papers  (2023-05-23T17:12:09Z)
• 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)
• 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)
• Partner Matters! An Empirical Study on Fusing Personas for Personalized Response Selection in Retrieval-Based Chatbots [51.091235903442715]
  This paper makes an attempt to explore the impact of utilizing personas that describe either self or partner speakers on the task of response selection. Four persona fusion strategies are designed, which assume personas interact with contexts or responses in different ways. Empirical studies on the Persona-Chat dataset show that the partner personas can improve the accuracy of response selection.
  arXiv  Detail & Related papers  (2021-05-19T10:32:30Z)
• 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)
• Conversations with Search Engines: SERP-based Conversational Response Generation [77.1381159789032]
  We create a suitable dataset, the Search as a Conversation (SaaC) dataset, for the development of pipelines for conversations with search engines. We also develop a state-of-the-art pipeline for conversations with search engines, the Conversations with Search Engines (CaSE) using this dataset. CaSE enhances the state-of-the-art by introducing a supporting token identification module and aprior-aware pointer generator.
  arXiv  Detail & Related papers  (2020-04-29T13:07:53Z)
• IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems [80.0781718687327]
  We analyze user intent patterns in information-seeking conversations and propose an intent-aware neural response ranking model "IART" IART is built on top of the integration of user intent modeling and language representation learning with the Transformer architecture.
  arXiv  Detail & Related papers  (2020-02-03T05:59:52Z)

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.