Supervised Phrase Table Triangulation with Neural Word Embeddings for Low-Resource Languages

Levinboim, Tomer; Chiang, David

doi:10.18653/v1/d15-1126

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…In 2015, Levinboim et al, [18] developed a supervised learning technique that improves noisy sentence translation scores obtained by triangulation of sentence tables. They extracted word translation distributions from small amounts of source-target bilingual data (a dictionary or a parallel corpus) with which they learned to assign better scores to translation candidates obtained by triangulation.…”

Section: B Translationmentioning

confidence: 99%

A State of the Art Review on Natural Language Processing applied to the Malagasy Language

Ratianantitra

2023

iCARTi

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Based on the growing mass of information of all kinds to be processed and to facilitate human/machine dialogue, research teams and language industries are still developing applications integrating automatic natural language processing techniques. Natural Language Processing (NLP) is a discipline on the border of linguistics and computer science that concerns the application of computer programs and techniques to all aspects of human language. NLP is an area of research that is still open. It has extended its applications in various fields and applied them in several languages worldwide. This paper will review the NLP methods and resources that have been performed on the Malagasy language only. Recently, work on the automatic processing of the Malagasy language occurs even if the language is part of the group of under-endowed languages. This will allow researchers who are interested in the Malagasy language to update themselves in terms of the work already carried out, and the challenges that may be envisaged in future.

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Section: B Translationmentioning

confidence: 99%

A State of the Art Review on Natural Language Processing applied to the Malagasy Language

Ratianantitra

2023

iCARTi

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“…Bridging source and target languages through a pivot language was originally proposed for phrasebased MT (De Gispert and Marino, 2006;Cohn and Lapata, 2007). It was later adapted for Neural MT (Levinboim and Chiang, 2015), and proposed joint training for pivot-based NMT. proposed to use an existing pivottarget NMT model to guide the training of sourcetarget model.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Generalized Data Augmentation for Low-Resource Translation

Xia

Kong

Anastasopoulos

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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Translation to or from low-resource languages (LRLs) poses challenges for machine translation in terms of both adequacy and fluency. Data augmentation utilizing large amounts of monolingual data is regarded as an effective way to alleviate these problems. In this paper, we propose a general framework for data augmentation in low-resource machine translation that not only uses target-side monolingual data, but also pivots through a related highresource language (HRL). Specifically, we experiment with a two-step pivoting method to convert high-resource data to the LRL, making use of available resources to better approximate the true data distribution of the LRL. First, we inject LRL words into HRL sentences through an induced bilingual dictionary. Second, we further edit these modified sentences using a modified unsupervised machine translation framework. Extensive experiments on four low-resource datasets show that under extreme low-resource settings, our data augmentation techniques improve translation quality by up to 1.5 to 8 BLEU points compared to supervised back-translation baselines.

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“…Up to this point, representative pivot translation methods in SMT have been explained. Other related research studies in pivot translation are primarily based on the triangulation for PBMT and focuse on discussions to further improve accuracy (Zhu, He, Wu, Zhu, Wang, and Zhao 2014;Levinboim and Chiang 2015;Dabre, Cromieres, Kurohashi, and Bhattacharyya 2015). The process of correctly estimating the translation probability is a problem in triangulation.…”

Section: Related Workmentioning

confidence: 99%

“…They have reported that stable translation accuracy can be obtained even in the triangulation of two phrase tables with unbalanced table size. Levinboim and Chiang (2015) have asserted that it is especially difficult to estimate wordlevel translation probability for phrase correspondence in the triangulation stage. Subsequently, they have proposed a method for improving the quality of the triangulation by estimating the translation probability even for the correspondence of words which cannot be directly observed, using a distributed expression of words (Mikolov, Yih, and Zweig 2013).…”

Section: Related Workmentioning

confidence: 99%

Syntactic Matching Methods in Pivot Translation

Miura

Neubig

Sudoh

et al. 2018

Journal of Natural Language Processing

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The pivot translation is useful method for translating between languages that contain little or no parallel data by utilizing equivalents in an intermediate language such as English. Commonly, phrase-based or tree-based pivot translation methods merge source-pivot and pivot-target translation models into a source-target model. This tactic is known as triangulation. However, the combination is based on the surface forms of constituent words, and it often produces incorrect source-target phrase pairs because of interlingual differences and semantic ambiguities in the pivot language. The translation accuracy is thus degraded. This paper proposes a triangulation approach that utilizes syntactic subtrees in the pivot language to avoid incorrect phrase combinations by distinguishing pivot language words by their syntactic roles. The results of the experiments conducted on the United Nations Parallel Corpus demonstrate that the proposed method is superior to other pivot translation approaches in all tested combinations of languages.

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Supervised Phrase Table Triangulation with Neural Word Embeddings for Low-Resource Languages

Cited by 5 publications

References 4 publications

A State of the Art Review on Natural Language Processing applied to the Malagasy Language

A State of the Art Review on Natural Language Processing applied to the Malagasy Language

Generalized Data Augmentation for Low-Resource Translation

Syntactic Matching Methods in Pivot Translation

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