Is it Great or Terrible? Preserving Sentiment in Neural Machine Translation of Arabic Reviews

Hadeel Saadany, Constantin Orasan


Abstract
Since the advent of Neural Machine Translation (NMT) approaches there has been a tremendous improvement in the quality of automatic translation. However, NMT output still lacks accuracy in some low-resource languages and sometimes makes major errors that need extensive postediting. This is particularly noticeable with texts that do not follow common lexico-grammatical standards, such as user generated content (UGC). In this paper we investigate the challenges involved in translating book reviews from Arabic into English, with particular focus on the errors that lead to incorrect translation of sentiment polarity. Our study points to the special characteristics of Arabic UGC, examines the sentiment transfer errors made by Google Translate of Arabic UGC to English, analyzes why the problem occurs, and proposes an error typology specific of the translation of Arabic UGC. Our analysis shows that the output of online translation tools of Arabic UGC can either fail to transfer the sentiment at all by producing a neutral target text, or completely flips the sentiment polarity of the target word or phrase and hence delivers a wrong affect message. We address this problem by fine-tuning an NMT model with respect to sentiment polarity showing that this approach can significantly help with correcting sentiment errors detected in the online translation of Arabic UGC.
Anthology ID:
2020.wanlp-1.3
Volume:
Proceedings of the Fifth Arabic Natural Language Processing Workshop
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
WANLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
24–37
Language:
URL:
https://aclanthology.org/2020.wanlp-1.3
DOI:
Bibkey:
Cite (ACL):
Hadeel Saadany and Constantin Orasan. 2020. Is it Great or Terrible? Preserving Sentiment in Neural Machine Translation of Arabic Reviews. In Proceedings of the Fifth Arabic Natural Language Processing Workshop, pages 24–37, Barcelona, Spain (Online). Association for Computational Linguistics.
Cite (Informal):
Is it Great or Terrible? Preserving Sentiment in Neural Machine Translation of Arabic Reviews (Saadany & Orasan, WANLP 2020)
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PDF:
https://aclanthology.org/2020.wanlp-1.3.pdf