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Emily Dinan
Researcher at Facebook
Publications - 46
Citations - 5233
Emily Dinan is an academic researcher from Facebook. The author has contributed to research in topics: Computer science & Context (language use). The author has an hindex of 19, co-authored 38 publications receiving 2990 citations.
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Proceedings ArticleDOI
Personalizing Dialogue Agents: I have a dog, do you have pets too?
TL;DR: In this paper, the task of making chit-chat more engaging by conditioning on profile information is addressed, and the resulting dialogue can be used to predict profile information about the interlocutors.
Proceedings Article
Wizard of Wikipedia: Knowledge-Powered Conversational Agents
TL;DR: The best performing dialogue models are able to conduct knowledgeable discussions on open-domain topics as evaluated by automatic metrics and human evaluations, while a new benchmark allows for measuring further improvements in this important research direction.
Proceedings ArticleDOI
Adversarial NLI: A New Benchmark for Natural Language Understanding
TL;DR: This work introduces a new large-scale NLI benchmark dataset, collected via an iterative, adversarial human-and-model-in-the-loop procedure, and shows that non-expert annotators are successful at finding their weaknesses.
Posted Content
Recipes for building an open-domain chatbot
Stephen Roller,Emily Dinan,Naman Goyal,Da Ju,Mary Williamson,Yinhan Liu,Jing Xu,Myle Ott,Kurt Shuster,Eric Michael Smith,Y-Lan Boureau,Jason Weston +11 more
TL;DR: Human evaluations show the best models outperform existing approaches in multi-turn dialogue on engagingness and humanness measurements, and the limitations of this work are discussed by analyzing failure cases of the models.
Proceedings Article
Neural Text Generation With Unlikelihood Training
TL;DR: It is shown that the likelihood objective itself is at fault, resulting in a model that assigns too much probability to sequences containing repeats and frequent words, unlike those from the human training distribution, thus providing a strong alternative to existing techniques.