Search Results for author: Michel Galley

Found 41 papers, 17 papers with code

Probing Factually Grounded Content Transfer with Factual Ablation

no code implementations Findings (ACL) 2022 Peter West, Chris Quirk, Michel Galley, Yejin Choi

Particularly, this domain allows us to introduce the notion of factual ablation for automatically measuring factual consistency: this captures the intuition that the model should be less likely to produce an output given a less relevant grounding document.

Automatic Document Sketching: Generating Drafts from Analogous Texts

no code implementations Findings (ACL) 2021 Zeqiu Wu, Michel Galley, Chris Brockett, Yizhe Zhang, Bill Dolan

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.

Text Generation

RetGen: A Joint framework for Retrieval and Grounded Text Generation Modeling

1 code implementation14 May 2021 Yizhe Zhang, Siqi Sun, Xiang Gao, Yuwei Fang, Chris Brockett, Michel Galley, Jianfeng Gao, Bill Dolan

We propose a framework that alleviates this data constraint by jointly training a grounded generator and document retriever on the language model signal.

Dialogue Generation Language Modelling

An Adversarially-Learned Turing Test for Dialog Generation Models

1 code implementation16 Apr 2021 Xiang Gao, Yizhe Zhang, Michel Galley, Bill Dolan

To alleviate this risk, we propose an adversarial training approach to learn a robust model, ATT (Adversarial Turing Test), that discriminates machine-generated responses from human-written replies.

Dialogue Evaluation reinforcement-learning

Ask what's missing and what's useful: Improving Clarification Question Generation using Global Knowledge

1 code implementation NAACL 2021 Bodhisattwa Prasad Majumder, Sudha Rao, Michel Galley, Julian McAuley

The ability to generate clarification questions i. e., questions that identify useful missing information in a given context, is important in reducing ambiguity.

Question Generation

Data Augmentation for Abstractive Query-Focused Multi-Document Summarization

1 code implementation2 Mar 2021 Ramakanth Pasunuru, Asli Celikyilmaz, Michel Galley, Chenyan Xiong, Yizhe Zhang, Mohit Bansal, Jianfeng Gao

The progress in Query-focused Multi-Document Summarization (QMDS) has been limited by the lack of sufficient largescale high-quality training datasets.

Data Augmentation Document Summarization +1

Text Editing by Command

no code implementations NAACL 2021 Felix Faltings, Michel Galley, Gerold Hintz, Chris Brockett, Chris Quirk, Jianfeng Gao, Bill Dolan

A prevailing paradigm in neural text generation is one-shot generation, where text is produced in a single step.

Text Generation

MixingBoard: a Knowledgeable Stylized Integrated Text Generation Platform

1 code implementation ACL 2020 Xiang Gao, Michel Galley, Bill Dolan

We present MixingBoard, a platform for quickly building demos with a focus on knowledge grounded stylized text generation.

Text Generation

A Controllable Model of Grounded Response Generation

1 code implementation1 May 2020 Zeqiu Wu, Michel Galley, Chris Brockett, Yizhe Zhang, Xiang Gao, Chris Quirk, Rik Koncel-Kedziorski, Jianfeng Gao, Hannaneh Hajishirzi, Mari Ostendorf, Bill Dolan

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.

Informativeness Pretrained Language Models +1

Structuring Latent Spaces for Stylized Response Generation

1 code implementation IJCNLP 2019 Xiang Gao, Yizhe Zhang, Sungjin Lee, Michel Galley, Chris Brockett, Jianfeng Gao, Bill Dolan

This structure allows the system to generate stylized relevant responses by sampling in the neighborhood of the conversation model prediction, and continuously control the style level.

Response Generation Style Transfer

Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading

1 code implementation ACL 2019 Lianhui Qin, Michel Galley, Chris Brockett, Xiaodong Liu, Xiang Gao, Bill Dolan, Yejin Choi, Jianfeng Gao

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 contentful and non-vacuous.

Informativeness Reading Comprehension +1

Towards Content Transfer through Grounded Text Generation

no code implementations NAACL 2019 Shrimai Prabhumoye, Chris Quirk, Michel Galley

Recent work in neural generation has attracted significant interest in controlling the form of text, such as style, persona, and politeness.

Text Generation

Consistent Dialogue Generation with Self-supervised Feature Learning

1 code implementation13 Mar 2019 Yizhe Zhang, Xiang Gao, Sungjin Lee, Chris Brockett, Michel Galley, Jianfeng Gao, Bill Dolan

Generating responses that are consistent with the dialogue context is one of the central challenges in building engaging conversational agents.

Dialogue Generation Response Generation

Jointly Optimizing Diversity and Relevance in Neural Response Generation

no code implementations NAACL 2019 Xiang Gao, Sungjin Lee, Yizhe Zhang, Chris Brockett, Michel Galley, Jianfeng Gao, Bill Dolan

In this paper, we propose a SpaceFusion model to jointly optimize diversity and relevance that essentially fuses the latent space of a sequence-to-sequence model and that of an autoencoder model by leveraging novel regularization terms.

Dialogue Generation Response Generation

Dialog System Technology Challenge 7

no code implementations11 Jan 2019 Koichiro Yoshino, Chiori Hori, Julien Perez, Luis Fernando D'Haro, Lazaros Polymenakos, Chulaka Gunasekara, Walter S. Lasecki, Jonathan K. Kummerfeld, Michel Galley, Chris Brockett, Jianfeng Gao, Bill Dolan, Xiang Gao, Huda Alamari, Tim K. Marks, Devi Parikh, Dhruv Batra

This paper introduces the Seventh Dialog System Technology Challenges (DSTC), which use shared datasets to explore the problem of building dialog systems.

Neural Approaches to Conversational AI

no code implementations ACL 2018 Jianfeng Gao, Michel Galley, Lihong Li

The present paper surveys neural approaches to conversational AI that have been developed in the last few years.

Question Answering

Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models

no code implementations IJCNLP 2017 Yi Luan, Chris Brockett, Bill Dolan, Jianfeng Gao, Michel Galley

Building a persona-based conversation agent is challenging owing to the lack of large amounts of speaker-specific conversation data for model training.

Multi-Task Learning

A Knowledge-Grounded Neural Conversation Model

2 code implementations7 Feb 2017 Marjan Ghazvininejad, Chris Brockett, Ming-Wei Chang, Bill Dolan, Jianfeng Gao, Wen-tau Yih, Michel Galley

We generalize the widely-used Seq2Seq approach by conditioning responses on both conversation history and external "facts", allowing the model to be versatile and applicable in an open-domain setting.

Slot Filling

Deep Reinforcement Learning for Dialogue Generation

8 code implementations EMNLP 2016 Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, Dan Jurafsky

Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes.

Dialogue Generation Policy Gradient Methods +1

A Persona-Based Neural Conversation Model

1 code implementation ACL 2016 Jiwei Li, Michel Galley, Chris Brockett, Georgios P. Spithourakis, Jianfeng Gao, Bill Dolan

We present persona-based models for handling the issue of speaker consistency in neural response generation.

Response Generation

A Diversity-Promoting Objective Function for Neural Conversation Models

15 code implementations NAACL 2016 Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, Bill Dolan

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.

Conversational Response Generation Response Generation

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

no code implementations IJCNLP 2015 Michel Galley, Chris Brockett, Alessandro Sordoni, Yangfeng Ji, Michael Auli, Chris Quirk, Margaret Mitchell, Jianfeng Gao, Bill Dolan

We introduce Discriminative BLEU (deltaBLEU), a novel metric for intrinsic evaluation of generated text in tasks that admit a diverse range of possible outputs.

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