Emily Dinan

I love your chain mail! Making knights smile in a fantasy game world:\n Open-domain goal-oriented dialogue agents

2020 · arXiv (Cornell University)

Dialogue research tends to distinguish between chit-chat and goal-oriented\ntasks. While the former is arguably more naturalistic and has a wider use of\nlanguage, the latter has clearer metrics and a straightforward learning signal.\nHumans effortlessly combine the …

Personalizing Dialogue Agents: I have a dog, do you have pets too?

2018 · arXiv (Cornell University)

Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating. In this work we present the task of making chit-chat more engaging …

Retrieve and Refine: Improved Sequence Generation Models For Dialogue

2018

Sequence generation models for dialogue are known to have several problems: they tend to produce short, generic sentences that are uninformative and unengaging. Retrieval models on the other hand can surface interesting responses, but are …

Wizard of Wikipedia: Knowledge-Powered Conversational agents

2018 · arXiv (Cornell University)

In open-domain dialogue intelligent agents should exhibit the use of knowledge, however there are few convincing demonstrations of this to date. The most popular sequence to sequence models typically "generate and hope" generic utterances that …

Neural Text Generation with Unlikelihood Training

2019 · arXiv (Cornell University)

Neural text generation is a key tool in natural language applications, but it is well known there are major problems at its core. In particular, standard likelihood training and decoding leads to dull and repetitive …

Recipes for Building an Open-Domain Chatbot

2021

Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Eric Michael Smith, Y-Lan Boureau, Jason Weston. Proceedings of the 16th Conference of the European Chapter of the Association …

Controlling Style in Generated Dialogue

2020 · arXiv (Cornell University)

Open-domain conversation models have become good at generating natural-sounding dialogue, using very large architectures with billions of trainable parameters. The vast training data required to train these architectures aggregates many different styles, tones, and qualities. …