28 citations · 53 across the 2 of their papers we have counts for
4 papers
Neural Generation Meets Real People: Towards Emotionally Engaging Mixed-Initiative Conversations
Ashwin Paranjape, Abigail See, Kathleen Kenealy +7
We present Chirpy Cardinal, an open-domain dialogue agent, as a research platform for the 2019 Alexa Prize competition. Building an open-domain socialbot that talks to real people…
Do Massively Pretrained Language Models Make Better Storytellers?
Abigail See, Aneesh Pappu, Rohun Saxena +2
Large neural language models trained on massive amounts of text have emerged as a formidable strategy for Natural Language Understanding tasks. However, the strength of these model…
What makes a good conversation? How controllable attributes affect human judgments
Abigail See, Stephen Roller, Douwe Kiela +1
A good conversation requires balance -- between simplicity and detail; staying on topic and changing it; asking questions and answering them. Although dialogue agents are commonly…
Compression of Neural Machine Translation Models via Pruning
Abigail See, Minh-Thang Luong, Christopher D. Manning
Neural Machine Translation (NMT), like many other deep learning domains, typically suffers from over-parameterization, resulting in large storage sizes. This paper examines three s…