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csebuetnlp / xl-sum

Star 271

This repository contains the code, data, and models of the paper titled "XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages" published in Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021.

multilingual machine-learning deep-learning dataset text-summarization abstractive-text-summarization abstractive-summarization text-summarisation low-resource-languages multilinguality summarization-corpora summarization-dataset multilingual-text-summarization text-summarization-dataset text-summarization-model low-resource-summarization low-resource-text-summarizarion multilingual-summarization
  • Updated Mar 26, 2024
  • Python

csebuetnlp / banglanmt

Star 148

This repository contains the code and data of the paper titled "Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for Bengali-English Machine Translation" published in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), November 16 - November 20, 2020.

machine-translation neural-machine-translation parallel-corpus parallel-corpora bangla-nlp low-resource-languages bangla-machine-translation bangla-dataset-machine-translation emnlp-2020 low-resource-nlp low-resource-machine-translation
  • Updated Oct 23, 2024
  • Python

alecokas / BiLatticeRNN-Confidence

Star 16

Confidence Estimation for Black Box Automatic Speech Recognition Systems Using Lattice Recurrent Neural Networks https://arxiv.org/abs/1910.11933 or https://ieeexplore.ieee.org/document/9053264

pytorch lstm speech-recognition attention lattice speech-processing asr lattices confidence-estimation low-resource-languages pytorch-implementation confidence-scores confusion-networks latticernn confidence-estimates
  • Updated Apr 16, 2020
  • Python

NN-Project-2 / Emotion-TTS-Emebddings

Star 11

This project explores zero-shot emotional speech synthesis using EMOD, a novel approach combining emotion and content embeddings for multilingual and cross-lingual emotion transfer. Built on a VITS-based TTS model, it preserves speaker identity while enhancing expressiveness, enabling emotion transfer across languages and genders efficiently.

text-to-speech end-to-end speech-synthesis zero-shot-learning few-shot few-shot-learning low-resource-languages emotional-speech-synthesis
  • Updated Mar 15, 2025
  • Python

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