Neural Machine Translation Leveraging Phrase-based Models in a Hybrid Search

EMNLP, Copenhagen, Denmark, September 2017
Neural Machine Translation Leveraging Phrase-based Models in a Hybrid Search
Leonard Dahlmann, Evgeny Matusov, Pavel Petrushkov, Shahram Khadivi
eBay Authors

In this paper, we introduce a hybrid search for attention-based neural machine translation (NMT). A target phrase learned with statistical MT models extends a hypothesis in the NMT beam search when the attention of the NMT model focuses on the source words translated by this phrase. Phrases added in this way are scored with the NMT model, but also with SMT features including phrase-level translation probabilities and a target language model. Experimental results on German->English news domain and English->Russian ecommerce domain translation tasks show that using phrase-based models in NMT search improves MT quality by up to 2.3% BLEU absolute as compared to a strong NMT baseline.

Another publication from the same author: Evgeny Matusov

MT Summit, Nagoya, Japan, September 2017

Neural and Statistical Methods for Leveraging Meta-information in Machine Translation

Shahram Khadivi, Patrick Wilken, Leonard Dahlmann, Evgeny Matusov

In this paper, we discuss different methods which use meta information and richer context that may accompany source language input to improve machine translation quality. We focus on category information of input text as meta information, but the proposed methods can be extended to all textual and non-textual meta information that might be available for the input text or automatically predicted using the text content. The main novelty of this work is to use state-of-the-art neural network methods to tackle this problem within a statistical machine translation (SMT) framework. We observe translation quality improvements up to 3% in terms of BLEU score in some text categories.

Another publication from the same category: Machine Translation

MT Summit, Nagoya, Japan, September 2017

Harvesting Polysemous Terms from e-commerce Data to Enhance QA

Silvio Picinini

Polysemous words can be difficult to translate and can affect the quality of Machine Translation (MT) output. Once the MT quality is affected, it has a direct impact on post-editing and on human-assisted machine translation. The presence of these terms increases the risk of errors. We think that these important words can be used to improve and to measure quality of translations. We present three methods for finding these words from e-commerce data, based on Named Entity Recognition, Part-of-Speech and Search Queries.