Sentiment Aware Neural Machine Translation

Chenglei Si, Kui Wu, Ai Ti Aw, Min-Yen Kan


Abstract
Sentiment ambiguous lexicons refer to words where their polarity depends strongly on con- text. As such, when the context is absent, their translations or their embedded sentence ends up (incorrectly) being dependent on the training data. While neural machine translation (NMT) has achieved great progress in recent years, most systems aim to produce one single correct translation for a given source sentence. We investigate the translation variation in two sentiment scenarios. We perform experiments to study the preservation of sentiment during translation with three different methods that we propose. We conducted tests with both sentiment and non-sentiment bearing contexts to examine the effectiveness of our methods. We show that NMT can generate both positive- and negative-valent translations of a source sentence, based on a given input sentiment label. Empirical evaluations show that our valence-sensitive embedding (VSE) method significantly outperforms a sequence-to-sequence (seq2seq) baseline, both in terms of BLEU score and ambiguous word translation accuracy in test, given non-sentiment bearing contexts.
Anthology ID:
D19-5227
Volume:
Proceedings of the 6th Workshop on Asian Translation
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | WAT | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
200–206
Language:
URL:
https://aclanthology.org/D19-5227
DOI:
10.18653/v1/D19-5227
Bibkey:
Cite (ACL):
Chenglei Si, Kui Wu, Ai Ti Aw, and Min-Yen Kan. 2019. Sentiment Aware Neural Machine Translation. In Proceedings of the 6th Workshop on Asian Translation, pages 200–206, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Sentiment Aware Neural Machine Translation (Si et al., EMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-5227.pdf