Choosing the right word: Using bidirectional LSTM tagger for writing support systems. (September 2019)

• Record Type: Journal Article
• Authors: Makarenkov, Victor; Rokach, Lior; Shapira, Bracha
• Is Part Of: Engineering applications of artificial intelligence. Volume 84(2019)
• Page Start: 1
• Page End: 10
• Publication Date: 2019-09
• Subjects: LSTM -- Writing support -- Language modeling -- Grammatical error correction -- Lexical substitution; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2019.05.003
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Scientific writing is difficult. It is even harder for those for whom English is a second language (ESL learners). Scholars around the world spend a significant amount of time and resources proofreading their work before submitting it for review or publication. In this paper we present a novel machine learning based application for proper word choice task. Proper word choice is a generalization the lexical substitution (LS) and grammatical error correction (GEC) tasks. We demonstrate and evaluate the usefulness of applying bidirectional Long Short Term Memory (LSTM) tagger, for this task. While state-of-the-art grammatical error correction uses error-specific classifiers and machine translation methods, we demonstrate an unsupervised method that is based solely on a high quality text corpus and does not require manually annotated data. We use a bidirectional Recurrent Neural Network (RNN) with LSTM for learning the proper word choice based on a word's sentential context. We demonstrate and evaluate our application in various settings, including both a domain-specific (scientific), writing task and a general-purpose writing task. We perform both strict machine and human evaluation. We show that our domain-specific and general-purpose models outperform state-of-the-art general context learning. As an additional contribution of this research, we also share our code, pre-trained models, and a new ESL learner test set with the research community.