Multi-task Stack Propagation for Neural Quality Estimation
SCIE
SCOPUS
- Title
- Multi-task Stack Propagation for Neural Quality Estimation
- Authors
- Kim, H.; LEE, JONG HYEOK; Na, S.-H.
- Date Issued
- 2019-08
- Publisher
- ASSOC COMPUTING MACHINERY
- Abstract
- Quality estimation is an important task in machine translation that has attracted increased interest in recent years. A key problem in translation-quality estimation is the lack of a sufficient amount of the quality annotated training data. To address this shortcoming, the Predictor-Estimator was proposed recently by introducing "word prediction" as an additional pre-subtask that predicts a current target word with consideration of surrounding source and target contexts, resulting in a two-stage neural model composed of a predictor and an estimator. However, the original Predictor-Estimator is not trained on a continuous stacking model but instead in a cascaded manner that separately trains the predictor from the estimator. In addition, the Predictor-Estimator is trained based on single-task learning only, which uses target-specific quality-estimation data without using other training data that are available from other-level quality-estimation tasks. In this article, we thus propose a multi-task stack propagation, which extensively applies stack propagation to fully train the Predictor-Estimator on a continuous stacking architecture and multi-task learning to enhance the training data from related other-level quality-estimation tasks. Experimental results on WMT17 quality-estimation datasets show that the Predictor-Estimator trained with multi-task stack propagation provides statistically significant improvements over the baseline models. In particular, under an ensemble setting, the proposed multi-task stack propagation leads to state-of-the-art performance at all the sentence/word/phrase levels for WMT17 quality estimation tasks.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/100174
- DOI
- 10.1145/3321127
- ISSN
- 2375-4699
- Article Type
- Article
- Citation
- ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING, vol. 18, no. 4, 2019-08
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