Search Results for author: Abdullah Faiz Ur Rahman Khilji

Found 11 papers, 3 papers with code

Improved English to Hindi Multimodal Neural Machine Translation

no code implementations ACL (WAT) 2021 Sahinur Rahman Laskar, Abdullah Faiz Ur Rahman Khilji, Darsh Kaushik, Partha Pakray, Sivaji Bandyopadhyay

Neural machine translation attains a state-of-the-art approach in machine translation, but it requires adequate training data, which is a severe problem for low-resource language pairs translation.

Data Augmentation Machine Translation +2

CNLP-NITS @ LongSumm 2021: TextRank Variant for Generating Long Summaries

no code implementations NAACL (sdp) 2021 Darsh Kaushik, Abdullah Faiz Ur Rahman Khilji, Utkarsh Sinha, Partha Pakray

The huge influx of published papers in the field of machine learning makes the task of summarization of scholarly documents vital, not just to eliminate the redundancy but also to provide a complete and satisfying crux of the content.

Extractive Summarization

Multimodal Neural Machine Translation for English to Hindi

no code implementations AACL (WAT) 2020 Sahinur Rahman Laskar, Abdullah Faiz Ur Rahman Khilji, Partha Pakray, Sivaji Bandyopadhyay

Moreover, the utilization of monolingual data in the pre-training step can improve the performance of the system for low resource language translations.

Machine Translation NMT +1

EnAsCorp1.0: English-Assamese Corpus

no code implementations loresmt (AACL) 2020 Sahinur Rahman Laskar, Abdullah Faiz Ur Rahman Khilji, Partha Pakray, Sivaji Bandyopadhyay

The corpus preparation is one of the important challenging task for the domain of machine translation especially in low resource language scenarios.

Machine Translation Translation

Seq2Seq and Joint Learning Based Unix Command Line Prediction System

2 code implementations20 Jun 2020 Thoudam Doren Singh, Abdullah Faiz Ur Rahman Khilji, Divyansha, Apoorva Vikram Singh, Surmila Thokchom, Sivaji Bandyopadhyay

Experimental methods state that our model has achieved accuracy surpassing mixture of other techniques and adaptive command line interface mechanism as acclaimed in the past.

Recommendation Systems

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