mlsanityData to 7 Oct 2026

MultiTalk: A Highly-Branching Dialog Testbed for Diverse Conversations

arXiv 2102.01263 ↗First posted 2 Feb 2021cs.CLcited by 6

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Abstract (arXiv)

We study conversational dialog in which there are many possible responses to a given history. We present the MultiTalk Dataset, a corpus of over 320,000 sentences of written conversational dialog that balances a high branching factor (10) with several conversation turns (6) through selective branch continuation. We make multiple contributions to study dialog generation in the highly branching setting. In order to evaluate a diverse set of generations, we propose a simple scoring algorithm, based on bipartite graph matching, to optimally incorporate a set of diverse references. We study multiple language generation tasks at different levels of predictive conversation depth, using textual attributes induced automatically from pretrained classifiers. Our culminating task is a challenging theory of mind problem, a controllable generation task which requires reasoning about the expected reaction of the listener.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 22 of its 37 references that are in the map. First posted on arXiv 2 Feb 2021.

Builds on

22 of 37 references.

References · 22

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10 more
  1. 2017Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memorycited by 167
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Built on by

Cited by year

Citing papers per year: 2022: 3; 2023: 2; 2024: 1.

6 papers.

  1. 2023Reducing Privacy Risks in Online Self-Disclosures with Language Modelscited by 47
  2. 2022Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Expertscited by 19
  3. 2024ConvoSense: Overcoming Monotonous Commonsense Inferences for Conversational AIcited by 4
  4. 2023CESAR: Automatic Induction of Compositional Instructions for Multi-turn Dialogscited by 3
  5. 2022CORAL: Contextual Response Retrievability Loss Function for Training Dialog Generation Modelscited by 1
  6. 2022CausalDialogue: Modeling Utterance-level Causality in Conversationscited by 1