Construction of English-Bodo Parallel Text Corpus for Statistical Machine Translation

Islam, Saiful; Paul, Abhijit; Purkayastha, Bipul Syam; Hussain, Ismail

doi:10.5121/ijnlc.2018.7509

Cited by 12 publications

(5 citation statements)

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“…Construction of a parallel corpus is very challenging and needs a high cost of human expertise. MT can produce high-quality translation results based on a massive amount of aligned parallel text corpora in both the source and target languages [7]. MT systems need resources that can provide an interpretation/suggestion of the source text and a translation hypothesis.…”

Section: Construction Of Amharic-arabic Parallel Text Corpusmentioning

confidence: 99%

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Amharic-arabic Neural Machine Translation

Gashaw

Shashirekha

2019

5th International Conference on Data Mining and Applications (DMAP 2019)

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Many automatic translation works have been addressed between major European language pairs, by taking advantage of large scale parallel corpora, but very few research works are conducted on the Amharic-Arabic language pair due to its parallel data scarcity. Two Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) based Neural Machine Translation (NMT) models are developed using Attention-based Encoder-Decoder architecture which is adapted from the open-source OpenNMT system. In order to perform the experiment, a small parallel Quranic text corpus is constructed by modifying the existing monolingual Arabic text and its equivalent translation of Amharic language text corpora available on Tanzile. LSTM and GRU based NMT models and Google Translation system are compared and found that LSTM based OpenNMT outperforms GRU based OpenNMT and Google Translation system, with a BLEU score of 12%, 11%, and 6% respectively.

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Section: Construction Of Amharic-arabic Parallel Text Corpusmentioning

confidence: 99%

“…MT is a very ambitious research task in NLP, and the demand for it is growing. Several MT systems have been developed all over the world, particularly from English to other natural languages, such as Arabic, Germany, Chinese, French, Hindi, Japanese, Spanish, and Urdu [7].…”

Section: Introductionmentioning

confidence: 99%

Amharic-arabic Neural Machine Translation

Gashaw

Shashirekha

2019

5th International Conference on Data Mining and Applications (DMAP 2019)

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“…For instance, as many as 197 Indian languages are in the UNESCO's Atlas of the "World's Languages in Danger, 2010". Even among the 22 scheduled languages of India, there is a wide disparity in resource availability, for example, for Konkani and Kashmiri (Rajan et al, 2020;Islam et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

How low is too low? A monolingual take on lemmatisation in Indian languages

Kumar¹,

Kumar²,

Bhattacharyya³

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Most prior work on ML based lemmatization has focused on high resource languages, where data sets (word forms) are readily available. For languages which have no linguistic work available, especially on morphology or in languages where the computational realization of linguistic rules is complex and cumbersome, machine learning based lemmatizers are the way to go. In this paper, we devote our attention to lemmatisation for low resource, morphologically rich scheduled Indian languages using neural methods. Here, low resource means only a small number of word forms are available. We perform tests to analyse the variance in monolingual models' performance on varying the corpus size and contextual morphological tag data for training. We show that monolingual approaches with data augmentation can give competitive accuracy even in the low resource setting, which augurs well for NLP in low resource setting.

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“…Machine Translation (MT), the task of translating texts from one natural language to another natural language automatically, is an important application of Computational Linguistics (CL) and Natural Language Processing (NLP). It can produce high-quality translation results based on a massive amount of aligned parallel text corpora in both the source and target languages [1]. MT systems need resources that can provide an interpretation/suggestion of the source text and a translation hypothesis.…”

Section: Introductionmentioning

confidence: 99%

“…Several MT systems have been developed, particularly from English to other natural languages, such as Arabic, Germany, Chinese, French, Hindi, Japanese, Spanish, and Urdu [1]. Though a limited amount of work has been done in different Ethiopian languages in the field of NLP, the MT system for Amharic-Arabic language pair is still in its infancy due to lack of parallel corpora.…”

Section: Introductionmentioning

confidence: 99%

Construction of Amharic-arabic Parallel Text Corpus for Neural Machine Translation

Gashaw¹,

Shashirekha²

2020

IJAIA

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Many automatic translation works have been addressed between major European language pairs, by taking advantage of large scale parallel corpora, but very few research works are conducted on the Amharic-Arabic language pair due to its parallel data scarcity. However, there is no benchmark parallel Amharic-Arabic text corpora available for Machine Translation task. Therefore, a small parallel Quranic text corpus is constructed by modifying the existing monolingual Arabic text and its equivalent translation of Amharic language text corpora available on Tanzile. Experiments are carried out on Two Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) based Neural Machine Translation (NMT) using Attention-based Encoder-Decoder architecture which is adapted from the open-source OpenNMT system. LSTM and GRU based NMT models and Google Translation system are compared and found that LSTM based OpenNMT outperforms GRU based OpenNMT and Google Translation system, with a BLEU score of 12%, 11%, and 6% respectively.

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Construction of English-Bodo Parallel Text Corpus for Statistical Machine Translation

Cited by 12 publications

References 0 publications

Amharic-arabic Neural Machine Translation

Amharic-arabic Neural Machine Translation

How low is too low? A monolingual take on lemmatisation in Indian languages

Construction of Amharic-arabic Parallel Text Corpus for Neural Machine Translation

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