Work

Neural Lattice Search for Domain Adaptation in Machine Translation

Year: 2017

Type: conference-paper

Abstract: Domain adaptation is a major challenge for neural machine translation (NMT). Given unknown words or new domains, NMT systems tend to generate fluent translations at the expense of adequacy. We present a stack-based lattice search algorithm for NMT and show that constraining its search space with lattices generated by phrase-based machine translation (PBMT) improves robustness. We report consistent BLEU score gains across four diverse domain adaptation tasks involving medical, IT, Koran, or subtitles texts.

Source: Edinburgh Research Explorer (University of Edinburgh)

Authors: Huda Khayrallah, Gaurav Kumar, Kevin Duh, Matt Post, Philipp Koehn

Institution: Johns Hopkins University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Defense Advanced Research Projects Agency, Advanced Research Projects Agency

Awards: G211012689, G2515541506

OpenAlex ID: W2775767017