38,827 research outputs found
Coverage model for character-based neural machine translation
En col·laboració amb la Universitat de Barcelona (UB) i la Universitat Rovira i Virgili (URV)In recent years, Neural Machine Translation (NMT) has achieved state-of-the art performance
in translating from a language; source language, to another; target language. However,
many of the proposed methods use word embedding techniques to represent a sentence
in the source or target language. Character embedding techniques for this task has been
suggested to represent the words in a sentence better. Moreover, recent NMT models use
attention mechanism where the most relevant words in a source sentence are used to generate
a target word. The problem with this approach is that while some words are translated multiple
times, some other words are not translated. To address this problem, coverage model
has been integrated into NMT to keep track of already-translated words and focus on the
untranslated ones. In this research, we present a new architecture in which we use character
embedding for representing the source and target words, and also use coverage model to
make certain that all words are translated. We compared our model with the previous models
and our model shows comparable improvements. Our model achieves an improvement of
2.87 BLEU (BiLingual Evaluation Understudy) score over the baseline; attention model, for
German-English translation, and 0.34 BLEU score improvement for Catalan-Spanish translation
Coverage for character based neural machine translation
In recent years, Neural Machine Translation (NMT) has achieved state-of-the-art performance in translating from a language; source language, to another; target language. However, many of the proposed methods use word embedding techniques to represent a sentence in the source or target language. Character embedding techniques for this task has been suggested to represent the words in a sentence better. Moreover, recent NMT models use attention mechanism where the most relevant words in a source sentence are used to generate a target word. The problem with this approach is that while some words are translated multiple times, some other words are not translated. To address this problem, coverage model has been integrated into NMT to keep track of already-translated words and focus on the untranslated ones. In this research, we present a new architecture in which we use character embedding for representing the source and target languages, and also use coverage model to make certain that all words are translated. Experiments were performed to compare our model with coverage and character model and the results show that our model performs better than the other two models.Peer ReviewedPostprint (author's final draft
Neural Machine Translation with Adequacy-Oriented Learning
Although Neural Machine Translation (NMT) models have advanced
state-of-the-art performance in machine translation, they face problems like
the inadequate translation. We attribute this to that the standard Maximum
Likelihood Estimation (MLE) cannot judge the real translation quality due to
its several limitations. In this work, we propose an adequacy-oriented learning
mechanism for NMT by casting translation as a stochastic policy in
Reinforcement Learning (RL), where the reward is estimated by explicitly
measuring translation adequacy. Benefiting from the sequence-level training of
RL strategy and a more accurate reward designed specifically for translation,
our model outperforms multiple strong baselines, including (1) standard and
coverage-augmented attention models with MLE-based training, and (2) advanced
reinforcement and adversarial training strategies with rewards based on both
word-level BLEU and character-level chrF3. Quantitative and qualitative
analyses on different language pairs and NMT architectures demonstrate the
effectiveness and universality of the proposed approach.Comment: AAAI 201
Towards a better integration of fuzzy matches in neural machine translation through data augmentation
We identify a number of aspects that can boost the performance of Neural Fuzzy Repair (NFR), an easy-to-implement method to integrate translation memory matches and neural machine translation (NMT). We explore various ways of maximising the added value of retrieved matches within the NFR paradigm for eight language combinations, using Transformer NMT systems. In particular, we test the impact of different fuzzy matching techniques, sub-word-level segmentation methods and alignment-based features on overall translation quality. Furthermore, we propose a fuzzy match combination technique that aims to maximise the coverage of source words. This is supplemented with an analysis of how translation quality is affected by input sentence length and fuzzy match score. The results show that applying a combination of the tested modifications leads to a significant increase in estimated translation quality over all baselines for all language combinations
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