Papers by Alexei Baevski
Multilingual Speech Translation from Efficient Finetuning of Pretrained Models (2021.acl-long)
Copied to clipboard
Xian Li, Changhan Wang, Yun Tang, Chau Tran, Yuqing Tang, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli
| Challenge: | Recent advances in text pretraining and finetuning have improved multitasking applications significantly. |
| Approach: | They propose a minimalistic LNA finetuning approach to build multilingual speech-to-text translation using a pretrained speech encoder and text decoder. |
| Outcome: | The proposed approach surpasses the cascaded ST benchmark for 36 translation directions on the large-scale multilingual ST benchmark CoVoST 2. |
fairseq: A Fast, Extensible Toolkit for Sequence Modeling (N19-4)
Copied to clipboard
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. |
Pre-trained language model representations for language generation (N19-1)
Copied to clipboard
| Challenge: | Pre-trained language model representations have been successful in a wide range of language understanding tasks. |
| Approach: | They propose to use pre-trained language model representations to integrate them into sequence to sequence models and apply it to machine translation and abstractive summarization. |
| Outcome: | The proposed model is able to perform 5.3 BLEU in machine translation and 5.3 on the full text version of CNN/DailyMail. |
Introducing Semantics into Speech Encoders (2023.acl-long)
Copied to clipboard
Derek Xu, Shuyan Dong, Changhan Wang, Suyoun Kim, Zhaojiang Lin, Bing Liu, Akshat Shrivastava, Shang-Wen Li, Liang-Hsuan Tseng, Guan-Ting Lin, Alexei Baevski, Hung-yi Lee, Yizhou Sun, Wei Wang
| Challenge: | Existing self-supervised speech encoders contain primarily acoustic rather than semantic information. |
| Approach: | They propose a task-agnostic unsupervised way to incorporate semantic information from large language model (LLM) systems into self-supervised speech encoders without labeled audio transcriptions. |
| Outcome: | The proposed approach improves spoken language understanding (SLU) performance by over 5% on intent classification (IC), with modest gains in named entity resolution (NER) and slot filling (SF), and spoken question answering (SQA) score by over 22%. |
Reservoir Transformers (2021.acl-long)
Copied to clipboard
| Challenge: | Using random initialization, we show that some transformers obtain impressive performance even when some of the layers are frozen. |
| Approach: | They propose to freeze transformer layers and use them to improve performance . they find that the transformers obtain impressive performance even when some of the layers are randomly initialized and never updated. |
| Outcome: | The proposed model improves on translation and language modelling tasks even when some layers are frozen. |
Cloze-driven Pretraining of Self-attention Networks (D19-1)
Copied to clipboard
| Challenge: | Existing work on pretraining language models has used unidirectional (left-to-right) or bi-directional (both left-to right and right-to left) LMs with loss function. |
| Approach: | They propose a bi-directional transformer model that pretrains both directions of a large language-model-inspired self-attention cloze model and propose clozing to predict each word in the training data. |
| Outcome: | The proposed model performs well on GLUE and state of the art benchmarks consistent with BERT. |
On Generative Spoken Language Modeling from Raw Audio (2021.tacl-1)
Copied to clipboard
Kushal Lakhotia, Eugene Kharitonov, Wei-Ning Hsu, Yossi Adi, Adam Polyak, Benjamin Bolte, Tu-Anh Nguyen, Jade Copet, Alexei Baevski, Abdelrahman Mohamed, Emmanuel Dupoux
| Challenge: | Using a set of metrics to evaluate the learned representations, we aim to create a system that learns from natural interactions as infants learn their first language. |
| Approach: | They propose a task of learning acoustic and linguistic characteristics from raw audio and a set of metrics to evaluate the learned representations at acustic, linguistic and encoding levels. |
| Outcome: | The proposed models evaluate the learned representations at acoustic and linguistic levels for both encoding and generation. |
Unified Speech-Text Pre-training for Speech Translation and Recognition (2022.acl-long)
Copied to clipboard
Yun Tang, Hongyu Gong, Ning Dong, Changhan Wang, Wei-Ning Hsu, Jiatao Gu, Alexei Baevski, Xian Li, Abdelrahman Mohamed, Michael Auli, Juan Pino
| Challenge: | Existing methods to pre-train speech and text use unlabeled data to learn universal feature representations. |
| Approach: | They propose a method to jointly pre-train speech and text in an encoder-decoder modeling framework for speech translation and recognition. |
| Outcome: | The proposed method 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. |
Toward Joint Language Modeling for Speech Units and Text (2023.findings-emnlp)
Copied to clipboard
Ju-Chieh Chou, Chung-Ming Chien, Wei-Ning Hsu, Karen Livescu, Arun Babu, Alexis Conneau, Alexei Baevski, Michael Auli
| Challenge: | Speech and text are two major forms of human language and little effort has been made to model them together. |
| Approach: | They propose to combine speech and text models to create mixed speech-text data by using different tokenizers and automatic metrics to evaluate how well the model mixes speech and texts. |
| Outcome: | The proposed model improves over a speech-only baseline and shows zero-shot cross-modal transferability. |