Development of Malayalam Text Generator for translation from English

R, Sunil; Manohar, Nimtha; Jayan, V.; Sulochana, K. G.

doi:10.1109/indcon.2011.6139398

Cited by 5 publications

(3 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If we identify all those verbs that are commonly used in Malayalam by the corpus analysis, then we can incorporate this in the glossary and the SMT performance can be improved based on that. Added to these worry we have 8 cases in Malayalam [9]. These eight cases will get doubled and can have 16 forms for noun.…”

Section: Proposed Solutions For the Issuesmentioning

confidence: 99%

Difficulties in Processing Malayalam Verbs for Statistical Machine Translation

Jayan

2015

IJAIA

View full text Add to dashboard Cite

In this paper we discuss the difficulties in processing the Malayalam texts for Statistical Machine Translation (SMT), especially the verb forms. Mostly the agglutinative nature of Malayalam is the main issue with the processing of text. We mainly focus on the verbs and its contribution in adding the difficulty in processing. The verb plays a crucial role in defining the sentence structure. We illustrate the issues with the existing google translation system and the trained MOSES system using limited set of English-Malayalam parallel corpus. Our reference for analysis is English-Malayalam language pair.

show abstract

Section: Proposed Solutions For the Issuesmentioning

confidence: 99%

Difficulties in Processing Malayalam Verbs for Statistical Machine Translation

Jayan

2015

IJAIA

View full text Add to dashboard Cite

show abstract

“…Some rule-based approaches toward the English-Malayalam automatic translations were proposed by Refs. [13] and [29] and Sunil et al [40]. Apart from rule-based approaches, various studies were done on SMT in the English-Malayalam translation.…”

Section: Related Workmentioning

confidence: 99%

Neural Machine Translation System for English to Indian Language Translation Using MTIL Parallel Corpus

Premjith

Kumar

2019

Journal of Intelligent Systems

View full text Add to dashboard Cite

Introduction of deep neural networks to the machine translation research ameliorated conventional machine translation systems in multiple ways, specifically in terms of translation quality. The ability of deep neural networks to learn a sensible representation of words is one of the major reasons for this improvement. Despite machine translation using deep neural architecture is showing state-of-the-art results in translating European languages, we cannot directly apply these algorithms in Indian languages mainly because of two reasons: unavailability of the good corpus and Indian languages are morphologically rich. In this paper, we propose a neural machine translation (NMT) system for four language pairs: English–Malayalam, English–Hindi, English–Tamil, and English–Punjabi. We also collected sentences from different sources and cleaned them to make four parallel corpora for each of the language pairs, and then used them to model the translation system. The encoder network in the NMT architecture was designed with long short-term memory (LSTM) networks and bi-directional recurrent neural networks (Bi-RNN). Evaluation of the obtained models was performed both automatically and manually. For automatic evaluation, the bilingual evaluation understudy (BLEU) score was used, and for manual evaluation, three metrics such as adequacy, fluency, and overall ranking were used. Analysis of the results showed the presence of lengthy sentences in English–Malayalam, and the English–Hindi corpus affected the translation. Attention mechanism was employed with a view to addressing the problem of translating lengthy sentences (sentences contain more than 50 words), and the system was able to perceive long-term contexts in the sentences.

show abstract

“…The idea for using pseudo target is to exploit structural similarity to obtain advantages similar to that of using interlingua approach. The translation is obtained through a text generation process [4] from the generated pseudo code.…”

Section: Introductionmentioning

confidence: 99%

Preprocessors in NLP applications: In the context of English to Malayalam Machine Translation

Jayan

Bhadran

2012

2012 Annual IEEE India Conference (INDICON)

View full text Add to dashboard Cite

Preprocessing the input text is an essential component in a Natural Language Processing (NLP) system. We are discussing the relevance of the preprocessors in the context of Machine Translation system developed by us based on AnglaBharati Technology. Whenever we come across with text for translation we encounter with the special formats in an input text and getting its appropriate translation is a difficult task. Sometimes they may not have definite grammatical structure and may not be able to handle using a language rule. This paper present a strategy to identify the special formats in English text like date, currency, number, time, quotes, acronym, parenthesis, etc for a rule based English Malayalam Machine Aided Translation system. AnglaBharati is a pattern directed rule based system with context free grammar like structure for English which generates a pseudo target for group of Indian languages. Preprocessor is one of the main modules in this translation System. Here it manipulates the English input text to produce an input which is more suitable for an engine to generate appropriate translation. Extensive research is carried out in this area to disambiguate and process the input text in order to get more suitable output from the translation engine.

show abstract

Development of Malayalam Text Generator for translation from English

Cited by 5 publications

References 1 publication

Difficulties in Processing Malayalam Verbs for Statistical Machine Translation

Difficulties in Processing Malayalam Verbs for Statistical Machine Translation

Neural Machine Translation System for English to Indian Language Translation Using MTIL Parallel Corpus

Preprocessors in NLP applications: In the context of English to Malayalam Machine Translation

Contact Info

Product

Resources

About