Search Results for author:
Found 12 papers, 0 papers with code
Document Level NMT of Low-Resource Languages with Backtranslation
This paper describes our system submission to WMT20 shared task on similar language translation.
LIMSI @ WMT 2020
This paper describes LIMSI’s submissions to the translation shared tasks at WMT’20.
FJWU participation for the WMT20 Biomedical Translation Task
This paper reports system descriptions for FJWU-NRPU team for participation in the WMT20 Biomedical shared translation task.
FJWU Participation for the WMT21 Biomedical Translation Task
In this paper we present the FJWU’s system submitted to the biomedical shared task at WMT21.
Automatic Sentence Simplification in Low Resource Settings for Urdu
We present a lexical and syntactically simplified Urdu simplification corpus with a detailed analysis of the various simplification operations and human evaluation of corpus quality.
Exploring Transfer Learning for Urdu Speech Synthesis
Neural methods in Text to Speech synthesis (TTS) have demonstrated momentous advancement in terms of the naturalness and intelligibility of the synthesized speech.
Improving Document-Level Neural Machine Translation with Domain Adaptation
Recent studies have shown that translation quality of NMT systems can be improved by providing document-level contextual information.
Developing a Monolingual Sentence Simplification Corpus for Urdu
We present a lexical and syntactically simplified Urdu simplification corpus and a detailed analysis of the various simplification operations.
Simplification automatique de texte dans un contexte de faibles ressources (Automatic Text Simplification : Approaching the Problem in Low Resource Settings for French)
La simplification de textes a {\'e}merg{\'e} comme un sous-domaine actif du traitement automatique des langues, du fait des probl{\`e}mes pratiques et th{\'e}oriques qu{'}elle permet d{'}aborder, ainsi que de ses nombreuses applications pratiques.
On the Exploration of English to Urdu Machine Translation
Machine Translation is the inevitable technology to reduce communication barriers in today{'}s world.
Exploring Transfer Learning and Domain Data Selection for the Biomedical Translation
Transfer Learning and Selective data training are two of the many approaches being extensively investigated to improve the quality of Neural Machine Translation systems.
