Improving thai-lao neural machine translation with similarity lexicon

2021 ◽  
pp. 1-10
Author(s):  
Zhiqiang Yu ◽  
Yuxin Huang ◽  
Junjun Guo
Keyword(s):  
Machine Translation ◽  
Semantic Information ◽  
Neural Machine Translation ◽  
Low Resource ◽  
Translation Quality ◽  
Decoder Architecture ◽  
Baseline System ◽  
Input Sentence ◽  
Resource Conditions ◽  
Language Pair

It has been shown that the performance of neural machine translation (NMT) drops starkly in low-resource conditions. Thai-Lao is a typical low-resource language pair of tiny parallel corpus, leading to suboptimal NMT performance on it. However, Thai and Lao have considerable similarities in linguistic morphology and have bilingual lexicon which is relatively easy to obtain. To use this feature, we first build a bilingual similarity lexicon composed of pairs of similar words. Then we propose a novel NMT architecture to leverage the similarity between Thai and Lao. Specifically, besides the prevailing sentence encoder, we introduce an extra similarity lexicon encoder into the conventional encoder-decoder architecture, by which the semantic information carried by the similarity lexicon can be represented. We further provide a simple mechanism in the decoder to balance the information representations delivered from the input sentence and the similarity lexicon. Our approach can fully exploit linguistic similarity carried by the similarity lexicon to improve translation quality. Experimental results demonstrate that our approach achieves significant improvements over the state-of-the-art Transformer baseline system and previous similar works.

scholarly journals Augmenting Neural Machine Translation through Round-Trip Training Approach

2019 ◽  
Vol 9 (1) ◽  
pp. 268-278 ◽  
Author(s):  
Benyamin Ahmadnia ◽  
Bonnie J. Dorr
Keyword(s):  
Machine Translation ◽  
Training Data ◽  
Training Dataset ◽  
Round Trip ◽  
Neural Machine Translation ◽  
Low Resource ◽  
Translation Quality ◽  
High Resource ◽  
Training Approach ◽  
Language Pair

AbstractThe quality of Neural Machine Translation (NMT), as a data-driven approach, massively depends on quantity, quality and relevance of the training dataset. Such approaches have achieved promising results for bilingually high-resource scenarios but are inadequate for low-resource conditions. Generally, the NMT systems learn from millions of words from bilingual training dataset. However, human labeling process is very costly and time consuming. In this paper, we describe a round-trip training approach to bilingual low-resource NMT that takes advantage of monolingual datasets to address training data bottleneck, thus augmenting translation quality. We conduct detailed experiments on English-Spanish as a high-resource language pair as well as Persian-Spanish as a low-resource language pair. Experimental results show that this competitive approach outperforms the baseline systems and improves translation quality.

Estimating Machine Translation Quality of Any Input Sentence

2020 ◽  
Vol 30 (01) ◽  
pp. 2050002
Author(s):  
Taichi Aida ◽  
Kazuhide Yamamoto
Keyword(s):  
Machine Translation ◽  
Evaluation Model ◽  
Joint Probability ◽  
Translation System ◽  
Neural Machine Translation ◽  
Translation Quality ◽  
Machine Translation System ◽  
Input Sentence ◽  
The One

Current methods of neural machine translation may generate sentences with different levels of quality. Methods for automatically evaluating translation output from machine translation can be broadly classified into two types: a method that uses human post-edited translations for training an evaluation model, and a method that uses a reference translation that is the correct answer during evaluation. On the one hand, it is difficult to prepare post-edited translations because it is necessary to tag each word in comparison with the original translated sentences. On the other hand, users who actually employ the machine translation system do not have a correct reference translation. Therefore, we propose a method that trains the evaluation model without using human post-edited sentences and in the test set, estimates the quality of output sentences without using reference translations. We define some indices and predict the quality of translations with a regression model. For the quality of the translated sentences, we employ the BLEU score calculated from the number of word [Formula: see text]-gram matches between the translated sentence and the reference translation. After that, we compute the correlation between quality scores predicted by our method and BLEU actually computed from references. According to the experimental results, the correlation with BLEU is the highest when XGBoost uses all the indices. Moreover, looking at each index, we find that the sentence log-likelihood and the model uncertainty, which are based on the joint probability of generating the translated sentence, are important in BLEU estimation.

scholarly journals Modeling Coherence for Discourse Neural Machine Translation

2019 ◽  
Vol 33 ◽  
pp. 7338-7345 ◽  
Author(s):  
Hao Xiong ◽  
Zhongjun He ◽  
Hua Wu ◽  
Haifeng Wang
Keyword(s):  
Machine Translation ◽  
State Of The Art ◽  
The State ◽  
Neural Machine Translation ◽  
Discourse Context ◽  
Translation Quality ◽  
Discourse Coherence ◽  
Baseline System

Discourse coherence plays an important role in the translation of one text. However, the previous reported models most focus on improving performance over individual sentence while ignoring cross-sentence links and dependencies, which affects the coherence of the text. In this paper, we propose to use discourse context and reward to refine the translation quality from the discourse perspective. In particular, we generate the translation of individual sentences at first. Next, we deliberate the preliminary produced translations, and train the model to learn the policy that produces discourse coherent text by a reward teacher. Practical results on multiple discourse test datasets indicate that our model significantly improves the translation quality over the state-of-the-art baseline system by +1.23 BLEU score. Moreover, our model generates more discourse coherent text and obtains +2.2 BLEU improvements when evaluated by discourse metrics.

scholarly journals Terminology Translation in Low-Resource Scenarios

Information ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 273
Author(s):  
Rejwanul Haque ◽  
Mohammed Hasanuzzaman ◽  
Andy Way
Keyword(s):  
Machine Translation ◽  
End Users ◽  
Training Data ◽  
Classification Task ◽  
Domain Experts ◽  
Low Resource ◽  
Translation Quality ◽  
Classification Framework ◽  
Industrial Setting ◽  
Language Pair

Term translation quality in machine translation (MT), which is usually measured by domain experts, is a time-consuming and expensive task. In fact, this is unimaginable in an industrial setting where customised MT systems often need to be updated for many reasons (e.g., availability of new training data, leading MT techniques). To the best of our knowledge, as of yet, there is no publicly-available solution to evaluate terminology translation in MT automatically. Hence, there is a genuine need to have a faster and less-expensive solution to this problem, which could help end-users to identify term translation problems in MT instantly. This study presents a faster and less expensive strategy for evaluating terminology translation in MT. High correlations of our evaluation results with human judgements demonstrate the effectiveness of the proposed solution. The paper also introduces a classification framework, TermCat, that can automatically classify term translation-related errors and expose specific problems in relation to terminology translation in MT. We carried out our experiments with a low resource language pair, English–Hindi, and found that our classifier, whose accuracy varies across the translation directions, error classes, the morphological nature of the languages, and MT models, generally performs competently in the terminology translation classification task.

POSTEDITING IN MACHINE TRANSLATION

2020 ◽  
Vol 2020 (30) ◽  
pp. 102-119
Author(s):  
Tetiana Korolova ◽  
Natalya Zhmayeva ◽  
Yulia Kolchah
Keyword(s):  
Machine Translation ◽  
Grammatical Structure ◽  
Incorrect Choice ◽  
Neural Machine Translation ◽  
Translation Quality ◽  
Modern Methods ◽  
Translation Services ◽  
Linguistic Units ◽  
Source Message ◽  
Language Pair

Modern industry of translation services singles out two translation quality levels that can be reached as a result of machine translation (MT) post-editing: good enough quality foresees rendering the main information of the source message, admitting stylistic, syntactic and morphological flaws while quality similar or equal to human translation is a full dress version of a post-edited text, ready to be published. The overview of MT systems enables us to consider Google Neural Machine Translation (GNMT) which is based on the most modern methods of training to reach maximum improvements the most powerful one. When analyzing texts translated by means of Google Translate the following problems were identified: distortion of the referential meaning of the source message, incorrect choice of variant equivalences, lack of terms harmonization, lack of abbreviations rendering, inconformity of linguistic units in persons, numbers and cases, incorrect choice of functional correspondings when rendering absolute constructions, gerund and participial constructions, literal translation of phrases, lack of transformations of the grammatical structure of the source message (additions, rearrangements). Taking into account the classified issues of machine translation as well as the levels of post-editing quality post-editing of the texts translated by means of MT is carried out, demands and recommendations applicable to post-editing results of MT within the language pair under analysis with respect to peculiarities of the specific MT system and the type of translated texts are provided.

scholarly journals Evaluation of English–Slovak Neural and Statistical Machine Translation

2021 ◽  
Vol 11 (7) ◽  
pp. 2948
Author(s):  
Lucia Benkova ◽  
Dasa Munkova ◽  
Ľubomír Benko ◽  
Michal Munk
Keyword(s):  
Machine Translation ◽  
Statistical Approach ◽  
Statistical Machine Translation ◽  
Specific Domain ◽  
Neural Network Approach ◽  
Neural Machine Translation ◽  
Translation Quality ◽  
The Neural Network ◽  
Language Pair

This study is focused on the comparison of phrase-based statistical machine translation (SMT) systems and neural machine translation (NMT) systems using automatic metrics for translation quality evaluation for the language pair of English and Slovak. As the statistical approach is the predecessor of neural machine translation, it was assumed that the neural network approach would generate results with a better quality. An experiment was performed using residuals to compare the scores of automatic metrics of the accuracy (BLEU_n) of the statistical machine translation with those of the neural machine translation. The results showed that the assumption of better neural machine translation quality regardless of the system used was confirmed. There were statistically significant differences between the SMT and NMT in favor of the NMT based on all BLEU_n scores. The neural machine translation achieved a better quality of translation of journalistic texts from English into Slovak, regardless of if it was a system trained on general texts, such as Google Translate, or specific ones, such as the European Commission’s (EC’s) tool, which was trained on a specific-domain.

scholarly journals Supervised Classification Based Machine Translation Quality Estimation

2020 ◽  
Vol 9 (3) ◽  
pp. 2697-2703
Keyword(s):  
Machine Translation ◽  
Supervised Classification ◽  
Translation System ◽  
Quality Estimation ◽  
Neural Machine Translation ◽  
Translation Quality ◽  
Sentence Level ◽  
Machine Translation System ◽  
Language Text ◽  
Language Pair

This submission describes the study of linguistically motivated features to estimate the translated sentence quality at sentence level on English-Hindi language pair. Several classification algorithms are employed to build the Quality Estimation (QE) models using the extracted features. We used source language text and the MT output to extract these features. Experiments show that our proposed approach is robust and producing competitive results for the DT based QE model on neural machine translation system.

Replacing Linguists with Dummies: A Serious Need for Trivial Baselines in Multi-Task Neural Machine Translation

2019 ◽  
Vol 113 (1) ◽  
pp. 31-40
Author(s):  
Daniel Kondratyuk ◽  
Ronald Cardenas ◽  
Ondřej Bojar
Keyword(s):  
Machine Translation ◽  
Deep Neural Networks ◽  
Neural Machine Translation ◽  
Translation Quality ◽  
Syntactic Information ◽  
Multiple Tasks ◽  
Recent Developments ◽  
Resource Conditions ◽  
Main Message ◽  
Source Word

Abstract Recent developments in machine translation experiment with the idea that a model can improve the translation quality by performing multiple tasks, e.g., translating from source to target and also labeling each source word with syntactic information. The intuition is that the network would generalize knowledge over the multiple tasks, improving the translation performance, especially in low resource conditions. We devised an experiment that casts doubt on this intuition. We perform similar experiments in both multi-decoder and interleaving setups that label each target word either with a syntactic tag or a completely random tag. Surprisingly, we show that the model performs nearly as well on uncorrelated random tags as on true syntactic tags. We hint some possible explanations of this behavior. The main message from our article is that experimental results with deep neural networks should always be complemented with trivial baselines to document that the observed gain is not due to some unrelated properties of the system or training effects. True confidence in where the gains come from will probably remain problematic anyway.

scholarly journals Extremely low-resource neural machine translation for Asian languages

2020 ◽  
Vol 34 (4) ◽  
pp. 347-382
Author(s):  
Raphael Rubino ◽  
Benjamin Marie ◽  
Raj Dabre ◽  
Atushi Fujita ◽  
Masao Utiyama ◽  
...  
Keyword(s):  
Machine Translation ◽  
Data Augmentation ◽  
Statistical Machine Translation ◽  
Synthetic Data ◽  
Parameter Tuning ◽  
Data Generation ◽  
Neural Machine Translation ◽  
Low Resource ◽  
Translation Quality ◽  
Asian Languages

AbstractThis paper presents a set of effective approaches to handle extremely low-resource language pairs for self-attention based neural machine translation (NMT) focusing on English and four Asian languages. Starting from an initial set of parallel sentences used to train bilingual baseline models, we introduce additional monolingual corpora and data processing techniques to improve translation quality. We describe a series of best practices and empirically validate the methods through an evaluation conducted on eight translation directions, based on state-of-the-art NMT approaches such as hyper-parameter search, data augmentation with forward and backward translation in combination with tags and noise, as well as joint multilingual training. Experiments show that the commonly used default architecture of self-attention NMT models does not reach the best results, validating previous work on the importance of hyper-parameter tuning. Additionally, empirical results indicate the amount of synthetic data required to efficiently increase the parameters of the models leading to the best translation quality measured by automatic metrics. We show that the best NMT models trained on large amount of tagged back-translations outperform three other synthetic data generation approaches. Finally, comparison with statistical machine translation (SMT) indicates that extremely low-resource NMT requires a large amount of synthetic parallel data obtained with back-translation in order to close the performance gap with the preceding SMT approach.

scholarly journals Semantic and syntactic information for neural machine translation

2021 ◽  
Author(s):  
Jordi Armengol-Estapé ◽  
Marta R. Costa-jussà
Keyword(s):  
Machine Translation ◽  
Linked Data ◽  
Semantic Information ◽  
Semantic Features ◽  
Linguistic Features ◽  
Neural Machine Translation ◽  
Low Resource ◽  
Syntactic Information ◽  
Attentional System

AbstractIntroducing factors such as linguistic features has long been proposed in machine translation to improve the quality of translations. More recently, factored machine translation has proven to still be useful in the case of sequence-to-sequence systems. In this work, we investigate whether this gains hold in the case of the state-of-the-art architecture in neural machine translation, the Transformer, instead of recurrent architectures. We propose a new model, the Factored Transformer, to introduce an arbitrary number of word features in the source sequence in an attentional system. Specifically, we suggest two variants depending on the level at which the features are injected. Moreover, we suggest two combination mechanisms for the word features and words themselves. We experiment both with classical linguistic features and semantic features extracted from a linked data database, and with two low-resource datasets. With the best-found configuration, we show improvements of 0.8 BLEU over the baseline Transformer in the IWSLT German-to-English task. Moreover, we experiment with the more challenging FLoRes English-to-Nepali benchmark, which includes both low-resource and very distant languages, and obtain an improvement of 1.2 BLEU. These improvements are achieved with linguistic and not with semantic information.

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