Machine Translation for Machines: the Sentiment Classification Use Case

2019-10-01 · IJCNLP 2019 11 · Amirhossein Tebbifakhr, Luisa Bentivogli, Matteo Negri, Marco Turchi

We propose a neural machine translation (NMT) approach that, instead of pursuing adequacy and fluency ("human-oriented" quality criteria), aims to generate translations that are best suited as input to a natural language processing component designed for a specific downstream task (a "machine-oriented" criterion). Towards this objective, we present a reinforcement learning technique based on a new candidate sampling strategy, which exploits the results obtained on the downstream task as weak feedback. Experiments in sentiment classification of Twitter data in German and Italian show that feeding an English classifier with machine-oriented translations significantly improves its performance. Classification results outperform those obtained with translations produced by general-purpose NMT models as well as by an approach based on reinforcement learning. Moreover, our results on both languages approximate the classification accuracy computed on gold standard English tweets.

📄 PDF Abstract BibTeX arXiv:1910.00478

Code (0)

등록된 구현이 없습니다.

Tasks

Classification General Classification Machine TranslationNMTreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Sentiment AnalysisSentiment ClassificationTranslation

The Impact of Indirect Machine Translation on Sentiment Classification

2020-08-25 · AMTA 2020 10 · Alberto Poncelas, Pintu Lohar, Andy Way, James Hadley

Sentiment classification has been crucial for many natural language processing (NLP) applications, such as the analysis of movie reviews, tweets, or customer feedback. A sufficiently large amount of data is required to b…

Classification General Classification Machine Translation Sentiment Analysis+2

Contextual and Position-Aware Factorization Machines for Sentiment Classification

2018-01-18 · Shuai Wang, Mianwei Zhou, Geli Fei, Yi Chang 외

While existing machine learning models have achieved great success for sentiment classification, they typically do not explicitly capture sentiment-oriented word interaction, which can lead to poor results for fine-grain…

Classification General Classification Position Recommendation Systems+4

Thumbs up? Sentiment Classification using Machine Learning Techniques

2002-05-28 · Bo Pang, Lillian Lee, Shivakumar Vaithyanathan

We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning tec…

BIG-bench Machine Learning Classification General Classification Sentiment Analysis+1

Cross-lingual sentiment classification in low-resource Bengali language

2020-11-01 · EMNLP (WNUT) 2020 11 · Salim Sazzed

Sentiment analysis research in low-resource languages such as Bengali is still unexplored due to the scarcity of annotated data and the lack of text processing tools. Therefore, in this work, we focus on generating resou…

Benchmarking Classification Cross-Lingual Sentiment Classification Machine Translation+4

Resource Creation and Evaluation for Multilingual Sentiment Analysis in Social Media Texts

2014-05-01 · LREC 2014 5 · Alex Balahur, ra, Marco Turchi, Ralf Steinberger 외

This paper presents an evaluation of the use of machine translation to obtain and employ data for training multilingual sentiment classifiers. We show that the use of machine translated data obtained similar results as t…

Classification General Classification Machine Translation Natural Language Inference+4