Discourse Translation from English to Telugu

Suryakanthi, T.; Sharma, Kamlesh

doi:10.1145/2791405.2791459

Cited by 4 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The subject of the current study is the translation of two categories of compound, discourse and difficult words in a sentence English into Telugu. This work is about creating algorithms that translate complicated and elaborate phrases from English to Suryakanthi and Sharma [15].…”

Section: Related Workmentioning

confidence: 99%

“…According the previous works [9][10][11][12][13][14][15][16][17], the translation among one language to other languages are chosen based on leading languages in top countries using MT. These translations are very effective in terms of standards and quality.…”

Section: Related Workmentioning

confidence: 99%

“…Sharma 2015 [15] MT English to Telugu language translation For compound and complexed perfect sentences, the translation scores are 50% and 10%.…”

Section: Suryakanthi Andmentioning

confidence: 99%

See 2 more Smart Citations

Lexical Based Reordering Models for English to Telugu Machine Translation

Vamsi,

Al Bataineh,

Doppala

2023

RIA

View full text Add to dashboard Cite

Telugu is one of the commonly spoken regional language in India. It is mostly spoken among the states of Andhra Pradesh and Telangana. In rural areas it is difficult for the people to understand the non-regional language specially while at the time of government works, land dealing transactions etc. Due to this there is a scope to develop a machine translation model from English to Telugu. The machine translation is an automatic technique of translating one language to another through Language Processing approach. To understand the Telugu language translation, the structural comparisons are done among English and Telugu languages to attain standard outcome. In this work, Lexical based reordering statistical model (LBRSM) is used for language conversion. This analyzes the language structure outcomes between word, phrase and hierarchical based models for the translation quality purpose. To maintain good quality translation TER and BLEU metrics 62.01 and 29.07 are considered for finding n-gram exact matches. From this work, the Phrase based reordering statistical model (PBRSM) achieved better results when compared with other system models in both training and testing phases.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Lexical Based Reordering Models for English to Telugu Machine Translation

Vamsi,

Al Bataineh,

Doppala

2023

RIA

View full text Add to dashboard Cite

show abstract

“…They concluded that RNN-LSTM from the dataset speech recognition, feature Telugu-id produce accuracy 93% precision 68.40% which was comparatively higher than CNN accuracy 66.11% and precision is 61.90%. Suryakanthi and Sherma done translate discourse, keeping the context in mind such that actual meaning doesn't change [5]. To evaluate system performance in translating discourse formed using connectives a test suite of around 400 sentences was developed.…”

Section: Literature Surveymentioning

confidence: 99%

Automated Caption Generation for Video Call with Language Translation

Polepaka,

Prashanth Kumar,

Umesh Chandra

et al. 2023

E3S Web Conf.

View full text Add to dashboard Cite

In the modern era, virtual communication between individuals is common. Many people’s lives have been made simpler in a number of circumstances by providing subtitles, generating automated captions for social media videos, and language translation from a source language to a targeted language. Both are included, which offers face-to-face translated captions during video conversations. React is used for application development. To send the data, socket programming is utilized. Context is understood and translated using Google translate API and speech recognition modules. With OpenAI and Whisper, captions are generated. This paper will directly create the translated user voice rather than translating the text and creating subtitles.

show abstract

“…Discourses are texts above sentence level. When a pronoun in the second sentence is referring to a subject in the first sentence it cannot be translated as a separate sentence instead while translation the first and second sentences should be interpreted as a whole and not as individual sentences [2]. The translation process involves identifying the antecedents of the anaphors which is resolution and creating the references over the discourse entity which is termed as generation [3,4].…”

Section: Introductionmentioning

confidence: 99%