MultiTalk: A Highly-Branching Dialog Testbed for Diverse Conversations
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Graph MatchingText GenerationSimilar Papers 제목 키워드 기반
DialogueAgents: A Hybrid Agent-Based Speech Synthesis Framework for Multi-Party Dialogue
Speech synthesis is crucial for human-computer interaction, enabling natural and intuitive communication. However, existing datasets involve high construction costs due to manual annotation and suffer from limited charac…
DiversitySpeech SynthesisValet: A Standardized Testbed of Traditional Imperfect-Information Card Games
AI algorithms for imperfect-information games are typically compared using performance metrics on individual games, making it difficult to assess robustness across game choices. Card games are a natural domain for imperf…
Speeding Up Mixed-Integer Programming Solvers with Sparse Learning for Branching
Machine learning is increasingly used to improve decisions within branch-and-bound algorithms for mixed-integer programming. Many existing approaches rely on deep learning, which often requires very large training datase…
Graph Neural NetworkSparse LearningLeft-Branching Transformers Excel at Right-Branching Languages: Data Shapes Word Order Preferences in Language Models
We systematically compare word order preferences in decoder-only language models across 192 artificial languages and typologically diverse natural languages. On artificial languages, models exhibit a left-branching prefe…
GENEVA: GENErating and Visualizing branching narratives using LLMs
Dialogue-based Role Playing Games (RPGs) require powerful storytelling. The narratives of these may take years to write and typically involve a large creative team. In this work, we demonstrate the potential of large gen…
Language ModelingLanguage ModellingLarge Language Model