Search Results for author: Rifat Shahriyar

Found 7 papers, 5 papers with code

XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages

1 code implementation25 Jun 2021 Tahmid Hasan, Abhik Bhattacharjee, Md Saiful Islam, Kazi Samin, Yuan-Fang Li, Yong-Bin Kang, M. Sohel Rahman, Rifat Shahriyar

XL-Sum induces competitive results compared to the ones obtained using similar monolingual datasets: we show higher than 11 ROUGE-2 scores on 10 languages we benchmark on, with some of them exceeding 15, as obtained by multilingual training.

Abstractive Text Summarization

Text2App: A Framework for Creating Android Apps from Text Descriptions

2 code implementations16 Apr 2021 Masum Hasan, Kazi Sajeed Mehrab, Wasi Uddin Ahmad, Rifat Shahriyar

We overcome this limitation by transforming natural language into an abstract intermediate formal language representing an application with a substantially smaller number of tokens.

Code Generation Language Modelling

BERT2Code: Can Pretrained Language Models be Leveraged for Code Search?

no code implementations16 Apr 2021 Abdullah Al Ishtiaq, Masum Hasan, Md. Mahim Anjum Haque, Kazi Sajeed Mehrab, Tanveer Muttaqueen, Tahmid Hasan, Anindya Iqbal, Rifat Shahriyar

In this work, we leverage the efficacy of these embedding models using a simple, lightweight 2-layer neural network in the task of semantic code search.

Code Search

BanglaBERT: Combating Embedding Barrier in Multilingual Models for Low-Resource Language Understanding

1 code implementation1 Jan 2021 Abhik Bhattacharjee, Tahmid Hasan, Kazi Samin, Md Saiful Islam, M. Sohel Rahman, Anindya Iqbal, Rifat Shahriyar

As a bi-product of the standard NLU benchmarks, we introduce a new downstream dataset on natural language inference (NLI) and show that BanglaBERT outperforms previous state-of-the-art results on all tasks by up to 3. 5%.

Document Classification Language Modelling +3

Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for Bengali-English Machine Translation

1 code implementation EMNLP 2020 Tahmid Hasan, Abhik Bhattacharjee, Kazi Samin, Masum Hasan, Madhusudan Basak, M. Sohel Rahman, Rifat Shahriyar

With the segmenter and the two methods combined, we compile a high-quality Bengali-English parallel corpus comprising of 2. 75 million sentence pairs, more than 2 million of which were not available before.

Machine Translation Sentence segmentation

Early Prediction for Merged vs Abandoned Code Changes in Modern Code Reviews

no code implementations7 Dec 2019 Md. Khairul Islam, Toufique Ahmed, Rifat Shahriyar, Anindya Iqbal, Gias Uddin

In our empirical study on the 146, 612 code changes from the three software projects, we find that (1) The new features like reviewer dimensions that are introduced in PredCR are the most informative.

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