Papers with fairseq
Fairseq S2T: Fast Speech-to-Text Modeling with Fairseq (2020.aacl-demo)
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| Challenge: | End-to-end sequence-to sequence (S2S) modeling has witnessed rapid growth in speech-totext (ST) tasks. |
| Approach: | They introduce fairseq S2T, a fairsq extension for speech-to-text modeling tasks such as end-to end speech recognition and speech-text translation. |
| Outcome: | The proposed extension provides end-to-end workflows from data pre-processing, model training to offline (online) inference. |
A Call for Clarity in Beam Search: How It Works and When It Stops (2024.lrec-main)
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| Challenge: | Empirical results show that a modified beam decoding implementation improves decoding performance of strong, neural language generation models. |
| Approach: | They propose a modification to a beam decoding implementation that generalizes the stopping criterion and provides flexibility to the depth of search. |
| Outcome: | The proposed method improves decoding performance of strong models on news text summarization and machine translation over diverse language pairs with negligible inference slowdown. |
fairseq: A Fast, Extensible Toolkit for Sequence Modeling (N19-4)
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Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli
| Challenge: | OpenNMT is a community-built toolkit written in multiple languages with an emphasis on extensibility. |
| Approach: | They propose to use PyTorch to train custom sequence models for translation, summarization, language modeling, and other tasks. |
| Outcome: | The proposed toolkit is fast, extensible, and useful for both research and production. |
fairseq Sˆ2: A Scalable and Integrable Speech Synthesis Toolkit (2021.emnlp-demo)
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| Challenge: | Speech synthesis is the task of generating speech waveforms with desired characteristics, including but not limited to textual content, speaker identity, and speaking styles. |
| Approach: | They propose a fairseq extension for speech synthesis that implements autoregressive and non-AR text-to-speech models and their multi-speaker variants. |
| Outcome: | The proposed extension can train autoregressive and non-AR models and their multi-speaker variants with less curated data and has automatic metrics to facilitate faster iteration and analysis. |
Non-Autoregressive Neural Machine Translation: A Call for Clarity (2022.emnlp-main)
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| Challenge: | Non-autoregressive translation models require a single forward pass to generate the output sequence instead of iteratively producing each predicted token. |
| Approach: | They propose to use a single forward pass to generate the output sequence instead of iteratively producing each predicted token. |
| Outcome: | The proposed models improve translation quality and speed under third-party testing environments. |
GottBERT: a pure German Language Model (2024.emnlp-main)
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Raphael Scheible, Johann Frei, Fabian Thomczyk, Henry He, Patric Tippmann, Jochen Knaus, Victor Jaravine, Frank Kramer, Martin Boeker
| Challenge: | Pre-trained language models have advanced natural language processing (NLP) despite the introduction of BERT, single-language models are still relevant. |
| Approach: | They present a German singlelanguage RoBERT model pre-trained exclusively on the German portion of the OSCAR dataset. |
| Outcome: | The GottBERT model outperforms the existing models on Named Entity Recognition and text classification tasks. |