# MultiTalk: A Highly-Branching Dialog Testbed for Diverse Conversations

arXiv: https://arxiv.org/abs/2102.01263 · HTML: https://mlsanity.com/paper/2102.01263/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 37 references that are in the map.
- First posted on arXiv 2 Feb 2021.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [Towards a Human-like Open-Domain Chatbot](https://mlsanity.com/paper/2001.09977/) (2020)
- [A Persona-Based Neural Conversation Model](https://mlsanity.com/paper/1603.06155/) (2016)
- [DialogueRNN: An Attentive RNN for Emotion Detection in Conversations](https://mlsanity.com/paper/1811.00405/) (2018)
- [Deal or No Deal? End-to-End Learning for Negotiation Dialogues](https://mlsanity.com/paper/1706.05125/) (2017)
- [Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models](https://mlsanity.com/paper/1507.04808/) (2015)
- [Facebook FAIR's WMT19 News Translation Task Submission](https://mlsanity.com/paper/1907.06616/) (2019)
- [Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory](https://mlsanity.com/paper/1704.01074/) (2017)
- [Evaluating the Evaluation of Diversity in Natural Language Generation](https://mlsanity.com/paper/2004.02990/) (2020)
- [Neural Responding Machine for Short-Text Conversation](https://mlsanity.com/paper/1503.02364/) (2015)
- [Language GANs Falling Short](https://mlsanity.com/paper/1811.02549/) (2018)
- [EmotionLines: An Emotion Corpus of Multi-Party Conversations](https://mlsanity.com/paper/1802.08379/) (2018)
- [MojiTalk: Generating Emotional Responses at Scale](https://mlsanity.com/paper/1711.04090/) (2017)
- [Engaging Image Captioning Via Personality](https://mlsanity.com/paper/1810.10665/) (2018)
- [SocialNLP EmotionX 2019 Challenge Overview: Predicting Emotions in Spoken Dialogues and Chats](https://mlsanity.com/paper/1909.07734/) (2019)
- [Conversational Transfer Learning for Emotion Recognition](https://mlsanity.com/paper/1910.04980/) (2019)
- [Reinforcement Learning Based Emotional Editing Constraint Conversation Generation](https://mlsanity.com/paper/1904.08061/) (2019)

## Built on by (most cited first, 6 of 6)

- [Reducing Privacy Risks in Online Self-Disclosures with Language Models](https://mlsanity.com/paper/2311.09538/) (2023)
- [Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Experts](https://mlsanity.com/paper/2203.07285/) (2022)
- [ConvoSense: Overcoming Monotonous Commonsense Inferences for Conversational AI](https://mlsanity.com/paper/2401.15471/) (2024)
- [CESAR: Automatic Induction of Compositional Instructions for Multi-turn Dialogs](https://mlsanity.com/paper/2311.17376/) (2023)
- [CORAL: Contextual Response Retrievability Loss Function for Training Dialog Generation Models](https://mlsanity.com/paper/2205.10558/) (2022)
- [CausalDialogue: Modeling Utterance-level Causality in Conversations](https://mlsanity.com/paper/2212.10515/) (2022)

## 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.