Predictor-Estimator using Multilevel Task Learning with Stack Propagation for Neural Quality Estimation
- Title
- Predictor-Estimator using Multilevel Task Learning with Stack Propagation for Neural Quality Estimation
- Authors
- KIM, HYUN; LEE, JONG HYEOK; NA, SEUNG-HOON
- Date Issued
- 2017-09-07
- Publisher
- ACL SIG‐MT
- Abstract
- In this paper, we present a two-stage neural quality estimation model that uses multilevel task learning for translation quality estimation (QE)
at the sentence, word, and phrase levels. Our approach is based on an end-to-end stacked neural model named Predictor-Estimator, which
has two stages consisting of a neural word prediction model and neural QE model. To efficiently train the two-stage model, a stack
propagation method is applied, thereby enabling us to jointly learn the word prediction model and QE model in a single learning mode.
In addition, we deploy multilevel task learning with stack propagation, where the training examples available for all QE subtasks
(i.e., sentence/word/phrase levels) are used to train a Predictor-Estimator for a specific subtask. All of our submissions to the QE
task of WMT17 are ensembles that combine a set of neural models trained under different settings of varying dimensionalities
and shuffling training examples, eventually achieving the best performances for all subtasks at the sentence, word, and
phrase levels.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/42899
- Article Type
- Conference
- Citation
- WMT17 (The Second Conference on Machine Translation), page. 562 - 568, 2017-09-07
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