255 citations · 1.3k across the 24 of their papers we have counts for
28 papers
GODEL: Large-Scale Pre-Training for Goal-Directed Dialog
Baolin Peng, Michel Galley, Pengcheng He +6
We introduce GODEL (Grounded Open Dialogue Language Model), a large pre-trained language model for dialog. In contrast with earlier models such as DialoGPT, GODEL leverages a new p…
Automatic Document Sketching: Generating Drafts from Analogous Texts
Zeqiu Wu, Michel Galley, Chris Brockett +2
The advent of large pre-trained language models has made it possible to make high-quality predictions on how to add or change a sentence in a document. However, the high branching…
RetGen: A Joint framework for Retrieval and Grounded Text Generation Modeling
Yizhe Zhang, Siqi Sun, Xiang Gao +5
Recent advances in large-scale pre-training such as GPT-3 allow seemingly high quality text to be generated from a given prompt. However, such generation systems often suffer from…
Text Editing by Command
Felix Faltings, Michel Galley, Gerold Hintz +4
A prevailing paradigm in neural text generation is one-shot generation, where text is produced in a single step. The one-shot setting is inadequate, however, when the constraints t…
Dialogue Response Ranking Training with Large-Scale Human Feedback Data
Xiang Gao, Yizhe Zhang, Michel Galley +2
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 m…
Contextualized Perturbation for Textual Adversarial Attack
Dianqi Li, Yizhe Zhang, Hao Peng +4
Adversarial examples expose the vulnerabilities of natural language processing (NLP) models, and can be used to evaluate and improve their robustness. Existing techniques of genera…
A Recipe for Creating Multimodal Aligned Datasets for Sequential Tasks
Angela S. Lin, Sudha Rao, Asli Celikyilmaz +4
Many high-level procedural tasks can be decomposed into sequences of instructions that vary in their order and choice of tools. In the cooking domain, the web offers many partially…
A Controllable Model of Grounded Response Generation
Zeqiu Wu, Michel Galley, Chris Brockett +8
Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process, often resulting in uninteresting respon…
POINTER: Constrained Progressive Text Generation via Insertion-based Generative Pre-training
Yizhe Zhang, Guoyin Wang, Chunyuan Li +3
Large-scale pre-trained language models, such as BERT and GPT-2, have achieved excellent performance in language representation learning and free-form text generation. However, the…
DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
Yizhe Zhang, Siqi Sun, Michel Galley +6
We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer). Trained on 147M conversation-like exchanges ext…
Generating a Common Question from Multiple Documents using Multi-source Encoder-Decoder Models
Woon Sang Cho, Yizhe Zhang, Sudha Rao +2
Ambiguous user queries in search engines result in the retrieval of documents that often span multiple topics. One potential solution is for the search engine to generate multiple…
Structuring Latent Spaces for Stylized Response Generation
Xiang Gao, Yizhe Zhang, Sungjin Lee +4
Generating responses in a targeted style is a useful yet challenging task, especially in the absence of parallel data. With limited data, existing methods tend to generate response…
Domain Adaptive Text Style Transfer
Dianqi Li, Yizhe Zhang, Zhe Gan +4
Text style transfer without parallel data has achieved some practical success. However, in the scenario where less data is available, these methods may yield poor performance. In t…
Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading
Lianhui Qin, Michel Galley, Chris Brockett +5
Although neural conversation models are effective in learning how to produce fluent responses, their primary challenge lies in knowing what to say to make the conversation contentf…
Jointly Optimizing Diversity and Relevance in Neural Response Generation
Xiang Gao, Sungjin Lee, Yizhe Zhang +4
Although recent neural conversation models have shown great potential, they often generate bland and generic responses. While various approaches have been explored to diversify the…
Consistent Dialogue Generation with Self-supervised Feature Learning
Yizhe Zhang, Xiang Gao, Sungjin Lee +4
Generating responses that are consistent with the dialogue context is one of the central challenges in building engaging conversational agents. We demonstrate that neural conversat…
Dialog System Technology Challenge 7
Koichiro Yoshino, Chiori Hori, Julien Perez +14
This paper introduces the Seventh Dialog System Technology Challenges (DSTC), which use shared datasets to explore the problem of building dialog systems. Recently, end-to-end dial…
Vision-based Navigation with Language-based Assistance via Imitation Learning with Indirect Intervention
Khanh Nguyen, Debadeepta Dey, Chris Brockett +1
We present Vision-based Navigation with Language-based Assistance (VNLA), a grounded vision-language task where an agent with visual perception is guided via language to find objec…
Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization
Yizhe Zhang, Michel Galley, Jianfeng Gao +4
Responses generated by neural conversational models tend to lack informativeness and diversity. We present Adversarial Information Maximization (AIM), an adversarial learning strat…
Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models
Yi Luan, Chris Brockett, Bill Dolan +2
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 pro…
Steering Output Style and Topic in Neural Response Generation
Di Wang, Nebojsa Jojic, Chris Brockett +1
We propose simple and flexible training and decoding methods for influencing output style and topic in neural encoder-decoder based language generation. This capability is desirabl…
A Knowledge-Grounded Neural Conversation Model
Marjan Ghazvininejad, Chris Brockett, Ming-Wei Chang +4
Neural network models are capable of generating extremely natural sounding conversational interactions. Nevertheless, these models have yet to demonstrate that they can incorporate…
Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation
Nasrin Mostafazadeh, Chris Brockett, Bill Dolan +4
The popularity of image sharing on social media and the engagement it creates between users reflects the important role that visual context plays in everyday conversations. We pres…
Emulating Human Conversations using Convolutional Neural Network-based IR
Abhay Prakash, Chris Brockett, Puneet Agrawal
Conversational agents ("bots") are beginning to be widely used in conversational interfaces. To design a system that is capable of emulating human-like interactions, a conversation…