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

Understand

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 post-editing.
• 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.