Topic adaptation for machine translation of e-commerce content

MT Summit- 2015/10
Topic adaptation for machine translation of e-commerce content
Prashant Mathur, Marcello Federico, Selçuk Köprü, Shahram Khadivi, Hassan Sawaf
Categories
eBay Authors
Abstract
Topic adaptation for machine translation of e-commerce content
Authors
Prashant Mathur, Marcello Federico, Selçuk Köprü, Shahram Khadivi and Hassan Sawaf 
Publication date
2015/10
Conference
MT Summit
Volume
15
Pages
pp. 270-282

Another publication from the same category: Machine Translation

Association for Machine Translation in the Americas (AMTA), Oct. 2016

Guided Alignment Training for Topic-Aware Neural Machine Translation

Wenhu Chen, Evgeny Matusov, Shahram Khadivi, Jan-Thorsten Peter

In this paper, we propose an effective way for biasing the attention mechanism of a sequence-to-sequence neural machine translation (NMT) model towards the well-studied statistical word alignment models. We show that our novel guided alignment training approach improves translation quality on real-life e-commerce texts consisting of product titles and descriptions, overcoming the problems posed by many unknown words and a large type/token ratio. We also show that meta-data associated with input texts such as topic or category information can significantly improve translation quality when used as an additional signal to the decoder part of the network. With both novel features, the BLEU score of the NMT system on a product title set improves from 18.6 to 21.3%. Even larger MT quality gains are obtained through domain adaptation of a general domain NMT system to e-commerce data. The developed NMT system also performs well on the IWSLT speech translation task, where an ensemble of four variant systems outperforms the phrase-based baseline by 2.1% BLEU absolute.