Getting Gender Right in Neural Machine Translation
Eva Vanmassenhove,Christian Hardmeier,Andy Way +2 more
- pp 3003-3008
TLDR
This paper integrated gender information into NMT systems to improve the translation quality of French-English NMT for multiple language pairs, and found that adding a gender feature to an NMT system significantly improved translation quality for some language pairs.Abstract:
Speakers of different languages must attend
to and encode strikingly different aspects of
the world in order to use their language correctly (Sapir, 1921; Slobin, 1996). One such
difference is related to the way gender is expressed in a language. Saying “I am happy”
in English, does not encode any additional
knowledge of the speaker that uttered the sentence. However, many other languages do
have grammatical gender systems and so such
knowledge would be encoded. In order to
correctly translate such a sentence into, say,
French, the inherent gender information needs
to be retained/recovered. The same sentence
would become either “Je suis heureux”, for a
male speaker or “Je suis heureuse” for a female one. Apart from morphological agreement, demographic factors (gender, age, etc.)
also influence our use of language in terms of
word choices or even on the level of syntactic constructions (Tannen, 1991; Pennebaker
et al., 2003). We integrate gender information
into NMT systems. Our contribution is twofold: (1) the compilation of large datasets with
speaker information for 20 language pairs, and
(2) a simple set of experiments that incorporate gender information into NMT for multiple language pairs. Our experiments show that
adding a gender feature to an NMT system significantly improves the translation quality for
some language pairs.read more
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References
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Proceedings ArticleDOI
Bleu: a Method for Automatic Evaluation of Machine Translation
TL;DR: This paper proposed a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run.
Proceedings Article
Neural Machine Translation by Jointly Learning to Align and Translate
TL;DR: It is conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and it is proposed to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.
Proceedings ArticleDOI
Learning Phrase Representations using RNN Encoder--Decoder for Statistical Machine Translation
Kyunghyun Cho,Bart van Merriënboer,Caglar Gulcehre,Dzmitry Bahdanau,Fethi Bougares,Holger Schwenk,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio +8 more
TL;DR: In this paper, the encoder and decoder of the RNN Encoder-Decoder model are jointly trained to maximize the conditional probability of a target sequence given a source sequence.
Proceedings Article
Sequence to Sequence Learning with Neural Networks
TL;DR: The authors used a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector.
Europarl: A Parallel Corpus for Statistical Machine Translation
TL;DR: A corpus of parallel text in 11 languages from the proceedings of the European Parliament is collected and its acquisition and application as training data for statistical machine translation (SMT) is focused on.