Search Results for author: Rejwanul Haque

Found 21 papers, 3 papers with code

Detecting Violation of Human Rights via Social Media

no code implementations CSRNLP (LREC) 2022 Yash Pilankar, Rejwanul Haque, Mohammed Hasanuzzaman, Paul Stynes, Pramod Pathak

Social media is not just meant for entertainment, it provides platforms for sharing information, news, facts and events.

Investigating Low-resource Machine Translation for English-to-Tamil

no code implementations loresmt (AACL) 2020 Akshai Ramesh, Venkatesh Balavadhani parthasa, Rejwanul Haque, Andy Way

Statistical machine translation (SMT) which was the dominant paradigm in machine translation (MT) research for nearly three decades has recently been superseded by the end-to-end deep learning approaches to MT.

Machine Translation NMT +1

The ADAPT’s Submissions to the WMT20 Biomedical Translation Task

no code implementations WMT (EMNLP) 2020 Prashant Nayak, Rejwanul Haque, Andy Way

This paper describes the ADAPT Centre’s submissions to the WMT20 Biomedical Translation Shared Task for English-to-Basque.

Machine Translation NMT +1

The ADAPT System Description for the WMT20 News Translation Task

no code implementations WMT (EMNLP) 2020 Venkatesh Parthasarathy, Akshai Ramesh, Rejwanul Haque, Andy Way

This paper describes the ADAPT Centre’s submissions to the WMT20 News translation shared task for English-to-Tamil and Tamil-to-English.

Machine Translation NMT +1

An Error-based Investigation of Statistical and Neural Machine Translation Performance on Hindi-to-Tamil and English-to-Tamil

no code implementations AACL (WAT) 2020 Akshai Ramesh, Venkatesh Balavadhani parthasa, Rejwanul Haque, Andy Way

Statistical machine translation (SMT) was the state-of-the-art in machine translation (MT) research for more than two decades, but has since been superseded by neural MT (NMT).

Machine Translation NMT +1

The ADAPT Centre’s Neural MT Systems for the WAT 2020 Document-Level Translation Task

no code implementations AACL (WAT) 2020 Wandri Jooste, Rejwanul Haque, Andy Way

In this paper we describe the ADAPT Centre’s submissions to the WAT 2020 document-level Business Scene Dialogue (BSD) Translation task.

Data Augmentation Machine Translation +1

Identifying Complaints from Product Reviews: A Case Study on Hindi

1 code implementation ICON 2020 Raghvendra Pratap Singh, Rejwanul Haque, Mohammed Hasanuzzaman, Andy Way

Automatic recognition of customer complaints on products or services that they purchase can be crucial for the organisations, multinationals and online retailers since they can exploit this information to fulfil their customers’ expectations including managing and resolving the complaints.

Machine Translation NMT +1

Modelling Source- and Target- Language Syntactic Information as Conditional Context in Interactive Neural Machine Translation

no code implementations EAMT 2020 Kamal Kumar Gupta, Rejwanul Haque, Asif Ekbal, Pushpak Bhattacharyya, Andy Way

In this study, we model source-language syntactic constituency parse and target-language syntactic descriptions in the form of supertags as conditional context for interactive prediction in neural MT (NMT).

Machine Translation NMT +1

Arabisc: Context-Sensitive Neural Spelling Checker

1 code implementation1 Dec 2020 Yasmin Moslem, Rejwanul Haque, Andy Way

Accordingly, we made use of a bidirectional LSTM language model (LM) for our context-sensitive spelling detection and correction model which is shown to have much control over the correction process.

Language Modelling Spelling Correction

The ADAPT System Description for the STAPLE 2020 English-to-Portuguese Translation Task

no code implementations WS 2020 Rejwanul Haque, Yasmin Moslem, Andy Way

This paper describes the ADAPT Centre{'}s submission to STAPLE (Simultaneous Translation and Paraphrase for Language Education) 2020, a shared task of the 4th Workshop on Neural Generation and Translation (WNGT), for the English-to-Portuguese translation task.

Machine Translation Translation

Cannot find the paper you are looking for? You can Submit a new open access paper.