Findings of the First Workshop on Simulating Conversational Intelligence in Chat

• URL: http://arxiv.org/abs/2402.06420v1
• Date: Fri, 9 Feb 2024 14:08:23 GMT
• Title: Findings of the First Workshop on Simulating Conversational Intelligence in Chat
• Authors: Yvette Graham, Mohammed Rameez Qureshi, Haider Khalid, Gerasimos Lampouras, Ignacio Iacobacci, Qun Liu
• Abstract summary: SCI-CHAT follows previous workshops on open domain dialogue but with a focus on the simulation of intelligent conversation as judged in a live human evaluation. Models aim to include the ability to follow a challenging topic over a multi-turn conversation, while positing, refuting and reasoning over arguments.
• Score: 30.82686571505668
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The aim of this workshop is to bring together experts working on open-domain dialogue research. In this speedily advancing research area many challenges still exist, such as learning information from conversations, engaging in realistic and convincing simulation of human intelligence and reasoning. SCI-CHAT follows previous workshops on open domain dialogue but with a focus on the simulation of intelligent conversation as judged in a live human evaluation. Models aim to include the ability to follow a challenging topic over a multi-turn conversation, while positing, refuting and reasoning over arguments. The workshop included both a research track and shared task. The main goal of this paper is to provide an overview of the shared task and a link to an additional paper that will include an in depth analysis of the shared task results following presentation at the workshop.

Related papers

• The Imperative of Conversation Analysis in the Era of LLMs: A Survey of Tasks, Techniques, and Trends [64.99423243200296]
  Conversation Analysis (CA) strives to uncover and analyze critical information from conversation data. In this paper, we perform a thorough review and systematize CA task to summarize the existing related work. We derive four key steps of CA from conversation scene reconstruction, to in-depth attribution analysis, and then to performing targeted training, finally generating conversations.
  arXiv  Detail & Related papers  (2024-09-21T16:52:43Z)
• Towards Harnessing Large Language Models for Comprehension of Conversational Grounding [1.8434042562191812]
  This study investigates the capabilities of large language models in classifying dialogue turns related to explicit or implicit grounding and predicting grounded knowledge elements. Our experimental results reveal challenges encountered by large language models in the two tasks. These initiatives aim to develop more effective dialogue systems that are better equipped to handle the intricacies of grounded knowledge in conversations.
  arXiv  Detail & Related papers  (2024-06-03T19:34:39Z)
• Common Ground Tracking in Multimodal Dialogue [13.763043173931024]
  We present a method for automatically identifying the current set of shared beliefs and questions under discussion'' (QUDs) of a group with a shared goal. We annotate a dataset of multimodal interactions in a shared physical space with speech transcriptions, prosodic features, gestures, actions, and facets of collaboration. We cascade into a set of formal closure rules derived from situated evidence and belief axioms and update operations.
  arXiv  Detail & Related papers  (2024-03-26T00:25:01Z)
• Dialogue Agents 101: A Beginner's Guide to Critical Ingredients for Designing Effective Conversational Systems [29.394466123216258]
  This study provides a comprehensive overview of the primary characteristics of a dialogue agent, their corresponding open-domain datasets, and the methods used to benchmark these datasets. We propose UNIT, a UNified dIalogue dataseT constructed from conversations of existing datasets for different dialogue tasks capturing the nuances for each of them.
  arXiv  Detail & Related papers  (2023-07-14T10:05:47Z)
• A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects [100.75759050696355]
  We provide a comprehensive overview of the prominent problems and advanced designs for conversational agent's proactivity in different types of dialogues. We discuss challenges that meet the real-world application needs but require a greater research focus in the future.
  arXiv  Detail & Related papers  (2023-05-04T11:38:49Z)
• KETOD: Knowledge-Enriched Task-Oriented Dialogue [77.59814785157877]
  Existing studies in dialogue system research mostly treat task-oriented dialogue and chit-chat as separate domains. We investigate how task-oriented dialogue and knowledge-grounded chit-chat can be effectively integrated into a single model.
  arXiv  Detail & Related papers  (2022-05-11T16:01:03Z)
• Re-entry Prediction for Online Conversations via Self-Supervised Learning [25.488783376789026]
  We propose three auxiliary tasks, namely, Spread Pattern, Repeated Target user, and Turn Authorship, as the self-supervised signals for re-entry prediction. Experimental results on two datasets newly collected from Twitter and Reddit show that our method outperforms the previous state-of-the-arts.
  arXiv  Detail & Related papers  (2021-09-05T08:07:52Z)
• Advances in Multi-turn Dialogue Comprehension: A Survey [51.215629336320305]
  We review the previous methods from the perspective of dialogue modeling. We discuss three typical patterns of dialogue modeling that are widely-used in dialogue comprehension tasks.
  arXiv  Detail & Related papers  (2021-03-04T15:50:17Z)
• Topic-Oriented Spoken Dialogue Summarization for Customer Service with Saliency-Aware Topic Modeling [61.67321200994117]
  In a customer service system, dialogue summarization can boost service efficiency by creating summaries for long spoken dialogues. In this work, we focus on topic-oriented dialogue summarization, which generates highly abstractive summaries. We propose a novel topic-augmented two-stage dialogue summarizer ( TDS) jointly with a saliency-aware neural topic model (SATM) for topic-oriented summarization of customer service dialogues.
  arXiv  Detail & Related papers  (2020-12-14T07:50:25Z)
• Recent Advances and Challenges in Task-oriented Dialog System [63.82055978899631]
  Task-oriented dialog systems are attracting more and more attention in academic and industrial communities. We discuss three critical topics for task-oriented dialog systems: (1) improving data efficiency to facilitate dialog modeling in low-resource settings, (2) modeling multi-turn dynamics for dialog policy learning, and (3) integrating domain knowledge into the dialog model.
  arXiv  Detail & Related papers  (2020-03-17T01:34:56Z)

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.