Reinforced NMT for Sentiment and Content Preservation in Low-resource Scenario

Kumari, Divya, Ekbal, Asif, Haque, Rejwanul, Bhattacharyya, Pushpak and Way, Andy (2021) Reinforced NMT for Sentiment and Content Preservation in Low-resource Scenario. ACM Transactions on Asian and Low-Resource Language Information Processing, 20 (4). pp. 1-27. ISSN 2375-4699

Full text not available from this repository.

Abstract

The preservation of domain knowledge from source to the target is crucial in any translation workflows. Hence, translation service providers that use machine translation (MT) in production could reasonably expect that the translation process should transfer both the underlying pragmatics and the semantics of the source-side sentences into the target language. However, recent studies suggest that the MT systems often fail to preserve such crucial information (e.g., sentiment, emotion, gender traits) embedded in the source text in the target. In this context, the raw automatic translations are often directly fed to other natural language processing (NLP) applications (e.g., sentiment classifier) in a cross-lingual platform. Hence, the loss of such crucial information during the translation could negatively affect the performance of such downstream NLP tasks that heavily rely on the output of the MT systems.

In our current research, we carefully balance both the sides (i.e., sentiment and semantics) during translation, by controlling a global-attention-based neural MT (NMT), to generate translations that encode the underlying sentiment of a source sentence while preserving its non-opinionated semantic content. Toward this, we use a state-of-the-art reinforcement learning method, namely, actor-critic, that includes a novel reward combination module, to fine-tune the NMT system so that it learns to generate translations that are best suited for a downstream task, viz. sentiment classification while ensuring the source-side semantics is intact in the process. Experimental results for Hindi–English language pair show that our proposed method significantly improves the performance of the sentiment classifier and alongside results in an improved NMT system.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Electronic computers. Computer science > Computer Systems T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science > Computer Systems
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Clara Chan
Date Deposited:	27 Jul 2021 13:59
Last Modified:	28 Jul 2021 15:40
URI:	https://norma.ncirl.ie/id/eprint/4898

Actions (login required)

View Item