75 citations · 214 across the 27 of their papers we have counts for
7 papers · 1 filter
TicketTalk: Toward human-level performance with end-to-end, transaction-based dialog systems
Bill Byrne, Karthik Krishnamoorthi, Saravanan Ganesh +1
We present a data-driven, end-to-end approach to transaction-based dialog systems that performs at near-human levels in terms of verbal response quality and factual grounding accur…
The Teacher-Student Chatroom Corpus
Andrew Caines, Helen Yannakoudakis, Helena Edmondson +4
The Teacher-Student Chatroom Corpus (TSCC) is a collection of written conversations captured during one-to-one lessons between teachers and learners of English. The lessons took pl…
Inference-only sub-character decomposition improves translation of unseen logographic characters
Danielle Saunders, Weston Feely, Bill Byrne
Neural Machine Translation (NMT) on logographic source languages struggles when translating `unseen' characters, which never appear in the training data. One possible approach to t…
Addressing Exposure Bias With Document Minimum Risk Training: Cambridge at the WMT20 Biomedical Translation Task
Danielle Saunders, Bill Byrne
The 2020 WMT Biomedical translation task evaluated Medline abstract translations. This is a small-domain translation task, meaning limited relevant training data with very distinct…
Neural Machine Translation Doesn't Translate Gender Coreference Right Unless You Make It
Danielle Saunders, Rosie Sallis, Bill Byrne
Neural Machine Translation (NMT) has been shown to struggle with grammatical gender that is dependent on the gender of human referents, which can cause gender bias effects. Many ex…
Using Context in Neural Machine Translation Training Objectives
Danielle Saunders, Felix Stahlberg, Bill Byrne
We present Neural Machine Translation (NMT) training using document-level metrics with batch-level documents. Previous sequence-objective approaches to NMT training focus exclusive…
Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem
Danielle Saunders, Bill Byrne
Training data for NLP tasks often exhibits gender bias in that fewer sentences refer to women than to men. In Neural Machine Translation (NMT) gender bias has been shown to reduce…