Models and Tasks for Human-Centered Machine Translation

Marine Carpuat


Abstract
In this talk, I will describe current research directions in my group that aim to make machine translation (MT) more human-centered. Instead of viewing MT solely as a task that aims to transduce a source sentence into a well-formed target language equivalent, we revisit all steps of the MT research and development lifecycle with the goal of designing MT systems that are able to help people communicate across language barriers. I will present methods to better characterize the parallel training data that powers MT systems, and how the degree of equivalence impacts translation quality. I will introduce models that enable flexible conditional language generation, and will discuss recent work on framing machine translation tasks and evaluation to center human factors.
Anthology ID:
2021.mmtlrl-1.1
Volume:
Proceedings of the First Workshop on Multimodal Machine Translation for Low Resource Languages (MMTLRL 2021)
Month:
September
Year:
2021
Address:
Online (Virtual Mode)
Editors:
Thoudam Doren Singh, Cristina España i Bonet, Sivaji Bandyopadhyay, Josef van Genabith
Venue:
MMTLRL
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
1
Language:
URL:
https://aclanthology.org/2021.mmtlrl-1.1
DOI:
Bibkey:
Cite (ACL):
Marine Carpuat. 2021. Models and Tasks for Human-Centered Machine Translation. In Proceedings of the First Workshop on Multimodal Machine Translation for Low Resource Languages (MMTLRL 2021), page 1, Online (Virtual Mode). INCOMA Ltd..
Cite (Informal):
Models and Tasks for Human-Centered Machine Translation (Carpuat, MMTLRL 2021)
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PDF:
https://aclanthology.org/2021.mmtlrl-1.1.pdf