Finding Better Subwords for Tibetan Neural Machine Translation

Subword segmentation plays an important role in Tibetan neural machine translation (NMT). The structure of Tibetan words consists of two levels. First, words consist of a sequence of syllables, and then a syllable consists of a sequence of characters. According to this special word structure, we propose two methods for Tibetan subword segmentation, namely syllable-based and character-based methods. The former generates subwords based on the Tibetan syllables, and the latter is based on Tibetan characters. In addition, we carry out experiments with these two subword segmentation methods on low-resource Tibetan-to-Chinese NMT, respectively. The experimental results show that both of them can improve translation performance, in which the subword segmentation based on character sequences can achieve better results. Overall, our proposed character-based subword segmentation is more simple and effective. Moreover, it can achieve better experimental results without paying much attention to the linguistic features of Tibetan.

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Sublemma-Based Neural Machine Translation

Complexity ◽

10.1155/2021/5935958 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Thien Nguyen ◽

Huu Nguyen ◽

Phuoc Tran

Keyword(s):

Machine Translation ◽

Quality Data ◽

Human Judgment ◽

Linguistic Features ◽

Neural Machine Translation ◽

Low Resource ◽

Part Of Speech ◽

Proposed Model ◽

Translation Systems ◽

Ted Talks

Powerful deep learning approach frees us from feature engineering in many artificial intelligence tasks. The approach is able to extract efficient representations from the input data, if the data are large enough. Unfortunately, it is not always possible to collect large and quality data. For tasks in low-resource contexts, such as the Russian ⟶ Vietnamese machine translation, insights into the data can compensate for their humble size. In this study of modelling Russian ⟶ Vietnamese translation, we leverage the input Russian words by decomposing them into not only features but also subfeatures. First, we break down a Russian word into a set of linguistic features: part-of-speech, morphology, dependency labels, and lemma. Second, the lemma feature is further divided into subfeatures labelled with tags corresponding to their positions in the lemma. Being consistent with the source side, Vietnamese target sentences are represented as sequences of subtokens. Sublemma-based neural machine translation proves itself in our experiments on Russian-Vietnamese bilingual data collected from TED talks. Experiment results reveal that the proposed model outperforms the best available Russian ⟶ Vietnamese model by 0.97 BLEU. In addition, automatic machine judgment on the experiment results is verified by human judgment. The proposed sublemma-based model provides an alternative to existing models when we build translation systems from an inflectionally rich language, such as Russian, Czech, or Bulgarian, in low-resource contexts.

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Semantic and syntactic information for neural machine translation

Machine Translation ◽

10.1007/s10590-021-09264-2 ◽

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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Neural machine translation with a polysynthetic low resource language

Machine Translation ◽

10.1007/s10590-020-09255-9 ◽

2020 ◽

Vol 34 (4) ◽

pp. 325-346

Author(s):

John E. Ortega ◽

Richard Castro Mamani ◽

Kyunghyun Cho

Keyword(s):

Machine Translation ◽

Neural Machine Translation ◽

Low Resource

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Analyzing Subword Techniques to Improve English to Sinhala Neural Machine Translation

International Journal of Asian Language Processing ◽

10.1142/s2717554520500174 ◽

2021 ◽

pp. 2050017

Author(s):

Rashmini Naranpanawa ◽

Ravinga Perera ◽

Thilakshi Fonseka ◽

Uthayasanker Thayasivam

Keyword(s):

Machine Translation ◽

State Of The Art ◽

Statistical Machine Translation ◽

Translation System ◽

Rare Word ◽

Neural Machine Translation ◽

Parallel Corpus ◽

Low Resource ◽

Word Level ◽

Morphologically Rich Languages

Neural machine translation (NMT) is a remarkable approach which performs much better than the Statistical machine translation (SMT) models when there is an abundance of parallel corpus. However, vanilla NMT is primarily based upon word-level with a fixed vocabulary. Therefore, low resource morphologically rich languages such as Sinhala are mostly affected by the out of vocabulary (OOV) and Rare word problems. Recent advancements in subword techniques have opened up opportunities for low resource communities by enabling open vocabulary translation. In this paper, we extend our recently published state-of-the-art EN-SI translation system using the transformer and explore standard subword techniques on top of it to identify which subword approach has a greater effect on English Sinhala language pair. Our models demonstrate that subword segmentation strategies along with the state-of-the-art NMT can perform remarkably when translating English sentences into a rich morphology language regardless of a large parallel corpus.

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Improving thai-lao neural machine translation with similarity lexicon

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-212236 ◽

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.

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Improved neural machine translation for low-resource English–Assamese pair

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-219260 ◽

2021 ◽

pp. 1-12

Author(s):

Sahinur Rahman Laskar ◽

Abdullah Faiz Ur Rahman Khilji ◽

Partha Pakray ◽

Sivaji Bandyopadhyay

Keyword(s):

Machine Translation ◽

Data Augmentation ◽

Language Translation ◽

Linguistically Diverse ◽

Neural Machine Translation ◽

Low Resource ◽

Parallel Data ◽

The World ◽

Translation Accuracy ◽

Vocabulary Problems

Language translation is essential to bring the world closer and plays a significant part in building a community among people of different linguistic backgrounds. Machine translation dramatically helps in removing the language barrier and allows easier communication among linguistically diverse communities. Due to the unavailability of resources, major languages of the world are accounted as low-resource languages. This leads to a challenging task of automating translation among various such languages to benefit indigenous speakers. This article investigates neural machine translation for the English–Assamese resource-poor language pair by tackling insufficient data and out-of-vocabulary problems. We have also proposed an approach of data augmentation-based NMT, which exploits synthetic parallel data and shows significantly improved translation accuracy for English-to-Assamese and Assamese-to-English translation and obtained state-of-the-art results.

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