5.2k citations · 10k across the 79 of their papers we have counts for
14 papers · 2 filters
Improving Conditioning in Context-Aware Sequence to Sequence Models
Xinyi Wang, Jason Weston, Michael Auli +1
Neural sequence to sequence models are well established for applications which can be cast as mapping a single input sequence into a single output sequence. In this work, we focus…
Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training
Margaret Li, Stephen Roller, Ilia Kulikov +4
Generative dialogue models currently suffer from a number of problems which standard maximum likelihood training does not address. They tend to produce generations that (i) rely to…
Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation
Emily Dinan, Angela Fan, Adina Williams +3
Models often easily learn biases present in the training data, and their predictions directly reflect this bias. We analyze gender bias in dialogue data, and examine how this bias…
The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents
Kurt Shuster, Da Ju, Stephen Roller +3
We introduce dodecaDialogue: a set of 12 tasks that measures if a conversational agent can communicate engagingly with personality and empathy, ask questions, answer questions by u…
Adversarial NLI: A New Benchmark for Natural Language Understanding
Yixin Nie, Adina Williams, Emily Dinan +3
We introduce a new large-scale NLI benchmark dataset, collected via an iterative, adversarial human-and-model-in-the-loop procedure. We show that training models on this new datase…
Finding Generalizable Evidence by Learning to Convince Q&A Models
Ethan Perez, Siddharth Karamcheti, Rob Fergus +3
We propose a system that finds the strongest supporting evidence for a given answer to a question, using passage-based question-answering (QA) as a testbed. We train evidence agent…