Syntactic Transfer for Low-Resource Machine Translation with Contextual Parameter Generation

The advent of large pretrained models has led to paradigm-shifting improvements throughout natural language processing. For many tasks, state-of-the-art results are now achieved by taking one of these large pretrained models and adapting it in some way for use on the desired task. While this approach has been successful on a broad range of tasks, that success is not evenly distributed within tasks—most of the gains are in high-resource settings, i.e., tasks and languages for which there is a large amount of labeled data available. Some tasks—and many languages—lack sufficient labeled data for these approaches to work well. Recently, there has been much interest in methods that could potentially close this gap and improve performance in low-resource settings. In this work, I demonstrate a novel method for adapting large pretrained models that involves dynamically generating additional parameters for the model based on an informative representation of the task and show that this method works especially well on the task of low-resource machine translation.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• O'Connor, Joe

Advisor dc:contributor.advisor

• Andreas, Jacob

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/145034

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/145034