Automatic Machine Translation of Poetry and a Low-Resource Language Pair

Đunđer, Ivan; Seljan, Sanja; Pavlovski, M.

doi:10.23919/mipro48935.2020.9245342

Cited by 11 publications

(8 citation statements)

References 14 publications

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“…One of the problems in machine translation is the lack of training data. This problem was reported by Seljan [17] and Dunder [18,19] for the problem of the automatic translation of poetry with a low-resource language pair. It was reported that the fluency and adequacy of the translation results were skewed to higher scores.…”

Section: Introductionmentioning

confidence: 86%

Named Entity Correction in Neural Machine Translation Using the Attention Alignment Map

Lee

Jang

2021

Applied Sciences

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Neural machine translation (NMT) methods based on various artificial neural network models have shown remarkable performance in diverse tasks and have become mainstream for machine translation currently. Despite the recent successes of NMT applications, a predefined vocabulary is still required, meaning that it cannot cope with out-of-vocabulary (OOV) or rarely occurring words. In this paper, we propose a postprocessing method for correcting machine translation outputs using a named entity recognition (NER) model to overcome the problem of OOV words in NMT tasks. We use attention alignment mapping (AAM) between the named entities of input and output sentences, and mistranslated named entities are corrected using word look-up tables. The proposed method corrects named entities only, so it does not require retraining of existing NMT models. We carried out translation experiments on a Chinese-to-Korean translation task for Korean historical documents, and the evaluation results demonstrated that the proposed method improved the bilingual evaluation understudy (BLEU) score by 3.70 from the baseline.

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Section: Introductionmentioning

confidence: 86%

Named Entity Correction in Neural Machine Translation Using the Attention Alignment Map

Lee

Jang

2021

Applied Sciences

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“…This study aims to illustrate the melodiousness of the Poem and set up a standard of how to compose a poem melodiously. We found some researcher works for poem domain such as classify the poem [3]- [6], extract features poem [7], poetry generation [8]- [10], evaluation poem [11], translated poetry [12]- [14], poem entity recognition [15], and analysis of the melodiousness of the Poem [16]- [18]. Nobody has researched extracting the melodious sound patterns before.…”

Section: Issn 2442-6571mentioning

confidence: 99%

“…Then both were joined together, as shown in Fig. 14 The percentages after the relationship rules were minimum support and minimum confidence, which could be calculated from support (A→ B) = P(A∪B) and confidence (A→B) = P(B|A) = P(A∪B)/P(A). Since the minimum confidence set = 80%, the rule was that each Wak in the Poem had the third syllable sound as vowel a; the fourth syllable sound as vowel a; which occur together in 75% of all transactions.…”

Section: Fig 7 Syllable Checkingmentioning

confidence: 99%

The extraction of beautiful sound patterns from Sunthorn Phu’s poem using machine learning technique and internal rhyme rule

Suksanguan

Waijanya

Promrit

2021

Int. J. Adv. Intell. Informatics

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The melodious poems have been written from the distinctive features of poetry or based on each country's typical style. Especially, Thai poems which composed by the use of specific forming, such as Internal Rhyme to develop melodiousness. The most attractive and well-known poems were composed by a genius Thai poet named Sunthorn Phu. He is a role model for Thai poets. UNESCO honored him as the world’s great poet and the best role model in poetry works. In this article, we proposed extracting 15,796 sentences (Waks) of the beautiful sound patterns of Phra Aphai Mani’s tales by machine learning technology in conjunction with the rules of internal Rhyme Klon-Suphap by using the Apriori Algorithm. The extraction of vowel rhymes separated by a group of Waks including 1) Poem Wak No. 1; 2) Poem Wak No. 2; 3) Poem Wak No. 3; and 4) Poem Wak No. 4. In this article, “Wak” means sentence. The created tool can extract the internal rhyme patterns and the 25 popular pattern vowels. The popular pattern illustrates the melodiousness of the Poem and sets up a standard of how to melodiously compose a poem. Then, the evaluation of the experiments was done by using 144 Waks selected from the extraction of the beautiful patterns and evaluated by the consistency score from 3 experts. The average accuracy score resulted in 95.30%.

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“…Another well known metric used for evaluating machine translation is Evaluation of Translation with Explicit Ordering (METEOR) [13]. Dun der et al [14] proposed a machine translation for poetry and a low resource language pair, such as Croatian-German. The authors collected data set that contained the works of a contemporary poet of the Croatian language and the translations of his poems in German.…”

Section: Literature Surveymentioning

confidence: 99%

Learning to Translate Kannada and English Queries for Mixed Script Information Retrieval

Lakshmi

Shambhavi

2021

cai

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Due to increase in the availability of numerous languages in the Web, cross language information retrieval is one of the happening issues in the field of natural language processing and information retrieval. Nowadays, people are habituated to combine two or more language words during oral or written discourse. Speakers have also employed intermixing of different languages and scripts in digital media while querying, blogging and on social media platforms. The way of representing two different language words of an utterance in their native scripts is known as mixed scripting. In the present work, we attempted to translate mixed script queries of Kannada and English languages into monolingual queries. We proposed three approaches for translation by constructing bilingual dictionary, word embeddings and Google translate. The proposed method outperforms the conventional dictionary based approach, when word embeddings were combined with the translations learnt from Google Translate and Dictionary.

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Automatic Machine Translation of Poetry and a Low-Resource Language Pair

Cited by 11 publications

References 14 publications

Named Entity Correction in Neural Machine Translation Using the Attention Alignment Map

Named Entity Correction in Neural Machine Translation Using the Attention Alignment Map

The extraction of beautiful sound patterns from Sunthorn Phu’s poem using machine learning technique and internal rhyme rule

Learning to Translate Kannada and English Queries for Mixed Script Information Retrieval

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