activity
20172026
most citedWizard of Wikipedia: Knowledge-Powered Conversational agents

623 citations · 1.4k across the 36 of their papers we have counts for

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Showing 2020Show all

8 papers · 1 filter

cs.CL2020★ 2 cited

Reducing conversational agents' overconfidence through linguistic calibration

Sabrina J. Mielke, Arthur Szlam, Emily Dinan +1

While improving neural dialogue agents' factual accuracy is the object of much research, another important aspect of communication, less studied in the setting of neural dialogue,…

cs.CL2020★ 97 cited

Recipes for Safety in Open-domain Chatbots

Jing Xu, Da Ju, Margaret Li +3

Models trained on large unlabeled corpora of human interactions will learn patterns and mimic behaviors therein, which include offensive or otherwise toxic behavior and unwanted bi…

cs.CL2020★ 32 cited

Controlling Style in Generated Dialogue

Eric Michael Smith, Diana Gonzalez-Rico, Emily Dinan +1

Open-domain conversation models have become good at generating natural-sounding dialogue, using very large architectures with billions of trainable parameters. The vast training da…

cs.AI2020★ 8 cited

Deploying Lifelong Open-Domain Dialogue Learning

Kurt Shuster, Jack Urbanek, Emily Dinan +2

Much of NLP research has focused on crowdsourced static datasets and the supervised learning paradigm of training once and then evaluating test performance. As argued in de Vries e…

cs.CL2020★ 42 cited

Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions

Stephen Roller, Y-Lan Boureau, Jason Weston +13

We present our view of what is necessary to build an engaging open-domain conversational agent: covering the qualities of such an agent, the pieces of the puzzle that have been bui…

cs.CL2020

Multi-Dimensional Gender Bias Classification

Emily Dinan, Angela Fan, Ledell Wu +3

Machine learning models are trained to find patterns in data. NLP models can inadvertently learn socially undesirable patterns when training on gender biased text. In this work, we…

cs.CL2020

Recipes for building an open-domain chatbot

Stephen Roller, Emily Dinan, Naman Goyal +9

Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of…

cs.AI2020★ 8 cited

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

Shrimai Prabhumoye, Margaret Li, Jack Urbanek +4

Dialogue research tends to distinguish between chit-chat and goal-oriented tasks. While the former is arguably more naturalistic and has a wider use of language, the latter has cle…