Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

You searched for id:"handle:1773/46824". One record found.

Search Limiters

Last 2 Years | English Only

No search limiters apply to these results.

▼ Search Limiters


University of Washington

1. Finkelstein, Paige. Human-assisted Neural Machine Translation: Harnessing Human Feedback for Machine Translation.

Degree: 2021, University of Washington

Neural machine translation (NMT) is a promising approach to the task of machine translation that has led to state-of-the-art results in many settings. However, NMT translations are still far from sufficient for many practical purposes. For this reason, there is a robust body of ongoing research on how to improve NMT systems with human feedback. This feedback can take many forms, including interactive-predictive NMT, post-editing of NMT output, and soliciting ratings or corrections of translations for the purposes of online learning. While these approaches are often effective, they are also often very time consuming and expensive. For that reason, there is important research into the question of how best to ensure that any human effort is used optimally. In this thesis, we contribute to this line of work by proposing a system that learns when it should ask for human feedback on a translation. This system makes use of an existing pre-trained NMT model, and introduces an additional feedback-requester model that learns to selectively solicit feedback from a human translator on the NMT translations. This system reduces human effort by directing attention to the most problematic sentences in a document, and the feedback-requester model itself is updated according to the translator's feedback. We also experiment with two active learning (AL) strategies for the feedback-requester model, and present a range of experiments simulating human translator use of the system and show the results over time. Advisors/Committee Members: Steinert-Threlkeld, Shane (advisor).

Subjects/Keywords: natural language processing; neural machine translation; Linguistics; Computer science; Linguistics

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Finkelstein, P. (2021). Human-assisted Neural Machine Translation: Harnessing Human Feedback for Machine Translation. (Thesis). University of Washington. Retrieved from http://hdl.handle.net/1773/46824

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Finkelstein, Paige. “Human-assisted Neural Machine Translation: Harnessing Human Feedback for Machine Translation.” 2021. Thesis, University of Washington. Accessed April 22, 2021. http://hdl.handle.net/1773/46824.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Finkelstein, Paige. “Human-assisted Neural Machine Translation: Harnessing Human Feedback for Machine Translation.” 2021. Web. 22 Apr 2021.

Vancouver:

Finkelstein P. Human-assisted Neural Machine Translation: Harnessing Human Feedback for Machine Translation. [Internet] [Thesis]. University of Washington; 2021. [cited 2021 Apr 22]. Available from: http://hdl.handle.net/1773/46824.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Finkelstein P. Human-assisted Neural Machine Translation: Harnessing Human Feedback for Machine Translation. [Thesis]. University of Washington; 2021. Available from: http://hdl.handle.net/1773/46824

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.