Chris Brockett

Multi-Task Learning of Speaker-Role-Based Neural Conversation Models

2017 · International Joint Conference on Natural Language Processing

Building a persona-based conversation agent is challenging owing to the lack of large amounts of speaker-specific conversation data for model training. This paper addresses the problem by proposing a multi-task learning approach to training neural …

deltaBLEU: A Discriminative Metric for Generation Tasks with Intrinsically Diverse Targets

2015 · arXiv (Cornell University)

We introduce Discriminative BLEU (deltaBLEU), a novel metric for intrinsic evaluation of generated text in tasks that admit a diverse range of possible outputs. Reference strings are scored for quality by human raters on a …

Dialogue Response Ranking Training with Large-Scale Human Feedback Data

2020

Existing open-domain dialog models are generally trained to minimize the perplexity of target human responses. However, some human replies are more engaging than others, spawning more followup interactions. Current conversational models are increasingly capable of …

A Neural Network Approach to Context-Sensitive Generation of Conversational Responses

2015

Alessandro Sordoni, Michel Galley, Michael Auli, Chris Brockett, Yangfeng Ji, Margaret Mitchell, Jian-Yun Nie, Jianfeng Gao, Bill Dolan. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human …

A Diversity-Promoting Objective Function for Neural Conversation Models

2015 · arXiv (Cornell University)

Sequence-to-sequence neural network models for generation of conversational responses tend to generate safe, commonplace responses (e.g., "I don't know") regardless of the input. We suggest that the traditional objective function, i.e., the likelihood of output …

A Persona-Based Neural Conversation Model

2016 · arXiv (Cornell University)

We present persona-based models for handling the issue of speaker consistency in neural response generation. A speaker model encodes personas in distributed embeddings that capture individual characteristics such as background information and speaking style. A …

A Knowledge-Grounded Neural Conversation Model

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

Neural network models are capable of generating extremely natural sounding conversational interactions. However, these models have been mostly applied to casual scenarios (e.g., as “chatbots”) and have yet to demonstrate they can serve in more …

Jointly Optimizing Diversity and Relevance in Neural Response Generation

2019

Xiang Gao, Sungjin Lee, Yizhe Zhang, Chris Brockett, Michel Galley, Jianfeng Gao, Bill Dolan. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 …

A Diversity-Promoting Objective Function for Neural Conversation Models

2016

Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, Bill Dolan. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016.

Domain Adaptive Text Style Transfer

2019

Dianqi Li, Yizhe Zhang, Zhe Gan, Yu Cheng, Chris Brockett, Bill Dolan, Ming-Ting Sun. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language …

DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation

2020

Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. 2020.

A Controllable Model of Grounded Response Generation

2021 · Proceedings of the AAAI Conference on Artificial Intelligence

Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process, often resulting in uninteresting responses. Attempts to boost informativeness alone come at the expense of factual accuracy, …

DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation

2019 · arXiv (Cornell University)

We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer). Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning from 2005 through 2017, DialoGPT extends …