Search Results for author:
Found 18 papers, 6 papers with code
Neural Machine Translation Doesn’t Translate Gender Coreference Right Unless You Make It
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.
Gender, names and other mysteries: Towards the ambiguous for gender-inclusive translation
The vast majority of work on gender in MT focuses on 'unambiguous' inputs, where gender markers in the source language are expected to be resolved in the output.
First the worst: Finding better gender translations during beam search
Neural machine translation inference procedures like beam search generate the most likely output under the model.
Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey
The development of deep learning techniques has allowed Neural Machine Translation (NMT) models to become extremely powerful, given sufficient training data and training time.
Inference-only sub-character decomposition improves translation of unseen logographic characters
One possible approach to this problem uses sub-character decomposition for training and test sentences.
Neural Machine Translation Doesn't Translate Gender Coreference Right Unless You Make It
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.
Addressing Exposure Bias With Document Minimum Risk Training: Cambridge at the WMT20 Biomedical Translation Task
The 2020 WMT Biomedical translation task evaluated Medline abstract translations.
Using Context in Neural Machine Translation Training Objectives
We find that each of these lines of research has a clear space in it for the other, and propose merging them with a scheme that allows a document-level evaluation metric to be used in the NMT training objective.
Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem
During inference we propose a lattice-rescoring scheme which outperforms all systems evaluated in Stanovsky et al (2019) on WinoMT with no degradation of general test set BLEU, and we show this scheme can be applied to remove gender bias in the output of `black box` online commercial MT systems.
CUED@WMT19:EWC\&LMs
Two techniques provide the fabric of the Cambridge University Engineering Department{'}s (CUED) entry to the WMT19 evaluation campaign: elastic weight consolidation (EWC) and different forms of language modelling (LMs).
UCAM Biomedical translation at WMT19: Transfer learning multi-domain ensembles
The 2019 WMT Biomedical translation task involved translating Medline abstracts.
Cued@wmt19:ewc&lms
Two techniques provide the fabric of the Cambridge University Engineering Department's (CUED) entry to the WMT19 evaluation campaign: elastic weight consolidation (EWC) and different forms of language modelling (LMs).
Domain Adaptive Inference for Neural Machine Translation
We investigate adaptive ensemble weighting for Neural Machine Translation, addressing the case of improving performance on a new and potentially unknown domain without sacrificing performance on the original domain.
An Operation Sequence Model for Explainable Neural Machine Translation
We propose to achieve explainable neural machine translation (NMT) by changing the output representation to explain itself.
Multi-representation Ensembles and Delayed SGD Updates Improve Syntax-based NMT
We explore strategies for incorporating target syntax into Neural Machine Translation.
Why not be Versatile? Applications of the SGNMT Decoder for Machine Translation
SGNMT is a decoding platform for machine translation which allows paring various modern neural models of translation with different kinds of constraints and symbolic models.
SGNMT -- A Flexible NMT Decoding Platform for Quick Prototyping of New Models and Search Strategies
This paper introduces SGNMT, our experimental platform for machine translation research.
