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1. Improving Multilingual ASR in the Wild Using Simple N-best Re-ranking

2. Scaling A Simple Approach to Zero-Shot Speech Recognition

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3. Toward Joint Language Modeling for Speech Units and Text

4. Scaling Speech Technology to 1,000+ Languages

5. DinoSR: Self-Distillation and Online Clustering for Self-supervised Speech Representation Learning

6. AV-data2vec: Self-supervised Learning of Audio-Visual Speech Representations with Contextualized Target Representations

7. Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language

8. Simple and Effective Unsupervised Speech Translation

9. Masked Autoencoders that Listen

10. Wav2Vec-Aug: Improved self-supervised training with limited data

11. On-demand compute reduction with stochastic wav2vec 2.0

12. Unified Speech-Text Pre-training for Speech Translation and Recognition

13. Simple and Effective Unsupervised Speech Synthesis

14. Towards End-to-end Unsupervised Speech Recognition

15. XTREME-S: Evaluating Cross-lingual Speech Representations

16. Measuring the Impact of Individual Domain Factors in Self-Supervised Pre-Training

17. data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language

18. XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale

19. Simple and Effective Zero-shot Cross-lingual Phoneme Recognition

20. Improved Language Identification Through Cross-Lingual Self-Supervised Learning

21. Unsupervised Speech Recognition

22. Large-Scale Self- and Semi-Supervised Learning for Speech Translation

23. Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training

24. A Comparison of Approaches to Document-level Machine Translation

25. Reservoir Transformers

26. Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling

27. Multilingual Speech Translation with Efficient Finetuning of Pretrained Models

28. A Comparison of Discrete Latent Variable Models for Speech Representation Learning

29. Self-training and Pre-training are Complementary for Speech Recognition

30. Beyond English-Centric Multilingual Machine Translation

31. Self-training Improves Pre-training for Natural Language Understanding

32. Unsupervised Cross-lingual Representation Learning for Speech Recognition

33. wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations

34. Robust and On-the-fly Dataset Denoising for Image Classification

35. Improving Conditioning in Context-Aware Sequence to Sequence Models

36. Effectiveness of self-supervised pre-training for speech recognition

37. Depth-Adaptive Transformer

38. vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations

39. The Source-Target Domain Mismatch Problem in Machine Translation

40. Simple and Effective Noisy Channel Modeling for Neural Machine Translation

41. On The Evaluation of Machine Translation Systems Trained With Back-Translation

42. ELI5: Long Form Question Answering

43. Facebook FAIR's WMT19 News Translation Task Submission

44. GLOSS: Generative Latent Optimization of Sentence Representations

45. wav2vec: Unsupervised Pre-training for Speech Recognition

46. fairseq: A Fast, Extensible Toolkit for Sequence Modeling

47. Pre-trained Language Model Representations for Language Generation

48. Cloze-driven Pretraining of Self-attention Networks

49. Mixture Models for Diverse Machine Translation: Tricks of the Trade

50. Pay Less Attention with Lightweight and Dynamic Convolutions