NLPRL at WAT2019: Transformer-based Tamil -- English Indic Task Neural Machine Translation System
This paper describes the Machine Translation system for Tamil-English Indic Task organized at WAT 2019. We use Transformer- based architecture for Neural Machine Translation.
Code (0)
등록된 구현이 없습니다.
Tasks
Machine TranslationTranslationSimilar Papers 제목 키워드 기반
NLPRL Odia-English: Indic Language Neural Machine Translation System
In this manuscript, we (team name is NLPRL) describe systems description that was submitted to the translation shared tasks at WAT 2020. We describe our model as transformer based NMT by using byte-level based BPE (BBPE)…
Machine TranslationNMTTranslationHopeful Men@LT-EDI-EACL2021: Hope Speech Detection Using Indic Transliteration and Transformers
This paper aims to describe the approach we used to detect hope speech in the HopeEDI dataset. We experimented with two approaches. In the first approach, we used contextual embeddings to train classifiers using logistic…
Hope Speech DetectionregressionTransliterationHopeful_Men@LT-EDI-EACL2021: Hope Speech Detection Using Indic Transliteration and Transformers
This paper aims to describe the approach we used to detect hope speech in the HopeEDI dataset. We experimented with two approaches. In the first approach, we used contextual embeddings to train classifiers using logistic…
Hope Speech DetectionregressionTransliterationIIITDWD@TamilNLP-ACL2022: Transformer-based approach to classify abusive content in Dravidian Code-mixed text
Identifying abusive content or hate speech in social media text has raised the research community’s interest in recent times. The major driving force behind this is the widespread use of social media websites. Further, i…
Stance DetectionDLRG@DravidianLangTech-ACL2022: Abusive Comment Detection in Tamil using Multilingual Transformer Models
Online Social Network has let people to connect and interact with each other. It does, however, also provide a platform for online abusers to propagate abusive content. The vast majority of abusive remarks are written in…
Word Embeddings