Alexei Baevski


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Unified Speech-Text Pre-training for Speech Translation and Recognition
Yun Tang | Hongyu Gong | Ning Dong | Changhan Wang | Wei-Ning Hsu | Jiatao Gu | Alexei Baevski | Xian Li | Abdelrahman Mohamed | Michael Auli | Juan Pino
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In this work, we describe a method to jointly pre-train speech and text in an encoder-decoder modeling framework for speech translation and recognition. The proposed method utilizes multi-task learning to integrate four self-supervised and supervised subtasks for cross modality learning. A self-supervised speech subtask, which leverages unlabelled speech data, and a (self-)supervised text to text subtask, which makes use of abundant text training data, take up the majority of the pre-training time. Two auxiliary supervised speech tasks are included to unify speech and text modeling space. Detailed analysis reveals learning interference among subtasks. In order to alleviate the subtask interference, two pre-training configurations are proposed for speech translation and speech recognition respectively. Our experiments show the proposed method can effectively fuse speech and text information into one model. It achieves between 1.7 and 2.3 BLEU improvement above the state of the art on the MuST-C speech translation dataset and comparable WERs to wav2vec 2.0 on the Librispeech speech recognition task.


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On Generative Spoken Language Modeling from Raw Audio
Kushal Lakhotia | Eugene Kharitonov | Wei-Ning Hsu | Yossi Adi | Adam Polyak | Benjamin Bolte | Tu-Anh Nguyen | Jade Copet | Alexei Baevski | Abdelrahman Mohamed | Emmanuel Dupoux
Transactions of the Association for Computational Linguistics, Volume 9

Abstract We introduce Generative Spoken Language Modeling, the task of learning the acoustic and linguistic characteristics of a language from raw audio (no text, no labels), and a set of metrics to automatically evaluate the learned representations at acoustic and linguistic levels for both encoding and generation. We set up baseline systems consisting of a discrete speech encoder (returning pseudo-text units), a generative language model (trained on pseudo- text), and a speech decoder (generating a waveform from pseudo-text) all trained without supervision and validate the proposed metrics with human evaluation. Across 3 speech encoders (CPC, wav2vec 2.0, HuBERT), we find that the number of discrete units (50, 100, or 200) matters in a task-dependent and encoder- dependent way, and that some combinations approach text-based systems.1

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Multilingual Speech Translation from Efficient Finetuning of Pretrained Models
Xian Li | Changhan Wang | Yun Tang | Chau Tran | Yuqing Tang | Juan Pino | Alexei Baevski | Alexis Conneau | Michael Auli
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

We present a simple yet effective approach to build multilingual speech-to-text (ST) translation through efficient transfer learning from a pretrained speech encoder and text decoder. Our key finding is that a minimalistic LNA (LayerNorm and Attention) finetuning can achieve zero-shot crosslingual and cross-modality transfer ability by only finetuning 10 50% of the pretrained parameters. This effectively leverages large pretrained models at low training cost such as wav2vec 2.0 for acoustic modeling, and mBART for multilingual text generation. This sets a new state-of-the-art for 36 translation directions (and surpassing cascaded ST for 26 of them) on the large-scale multilingual ST benchmark CoVoST 2 (+6.4 BLEU on average for En-X directions and +6.7 BLEU for X-En directions). Our approach demonstrates strong zero-shot performance in a many-to-many multilingual model (+5.6 BLEU on average across 28 non-English directions), making it an appealing approach for attaining high-quality speech translation with improved parameter and data efficiency.

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Reservoir Transformers
Sheng Shen | Alexei Baevski | Ari Morcos | Kurt Keutzer | Michael Auli | Douwe Kiela
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

We demonstrate that transformers obtain impressive performance even when some of the layers are randomly initialized and never updated. Inspired by old and well-established ideas in machine learning, we explore a variety of non-linear “reservoir” layers interspersed with regular transformer layers, and show improvements in wall-clock compute time until convergence, as well as overall performance, on various machine translation and (masked) language modelling tasks.


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Pre-trained language model representations for language generation
Sergey Edunov | Alexei Baevski | Michael Auli
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Pre-trained language model representations have been successful in a wide range of language understanding tasks. In this paper, we examine different strategies to integrate pre-trained representations into sequence to sequence models and apply it to neural machine translation and abstractive summarization. We find that pre-trained representations are most effective when added to the encoder network which slows inference by only 14%. Our experiments in machine translation show gains of up to 5.3 BLEU in a simulated resource-poor setup. While returns diminish with more labeled data, we still observe improvements when millions of sentence-pairs are available. Finally, on abstractive summarization we achieve a new state of the art on the full text version of CNN/DailyMail.

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fairseq: A Fast, Extensible Toolkit for Sequence Modeling
Myle Ott | Sergey Edunov | Alexei Baevski | Angela Fan | Sam Gross | Nathan Ng | David Grangier | Michael Auli
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)

fairseq is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks. The toolkit is based on PyTorch and supports distributed training across multiple GPUs and machines. We also support fast mixed-precision training and inference on modern GPUs. A demo video can be found at

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Cloze-driven Pretraining of Self-attention Networks
Alexei Baevski | Sergey Edunov | Yinhan Liu | Luke Zettlemoyer | Michael Auli
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We present a new approach for pretraining a bi-directional transformer model that provides significant performance gains across a variety of language understanding problems. Our model solves a cloze-style word reconstruction task, where each word is ablated and must be predicted given the rest of the text. Experiments demonstrate large performance gains on GLUE and new state of the art results on NER as well as constituency parsing benchmarks, consistent with BERT. We also present a detailed analysis of a number of factors that contribute to effective pretraining, including data domain and size, model capacity, and variations on the cloze objective.

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Facebook FAIR’s WMT19 News Translation Task Submission
Nathan Ng | Kyra Yee | Alexei Baevski | Myle Ott | Michael Auli | Sergey Edunov
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

This paper describes Facebook FAIR’s submission to the WMT19 shared news translation task. We participate in four language directions, English <-> German and English <-> Russian in both directions. Following our submission from last year, our baseline systems are large BPE-based transformer models trained with the FAIRSEQ sequence modeling toolkit. This year we experiment with different bitext data filtering schemes, as well as with adding filtered back-translated data. We also ensemble and fine-tune our models on domain-specific data, then decode using noisy channel model reranking. Our system improves on our previous system’s performance by 4.5 BLEU points and achieves the best case-sensitive BLEU score for the translation direction English→Russian.