318 citations · 1.2k across the 24 of their papers we have counts for
17 papers · 1 filter
Improving Conditioning in Context-Aware Sequence to Sequence Models
Xinyi Wang, Jason Weston, Michael Auli +1
Neural sequence to sequence models are well established for applications which can be cast as mapping a single input sequence into a single output sequence. In this work, we focus…
Effectiveness of self-supervised pre-training for speech recognition
Alexei Baevski, Michael Auli, Abdelrahman Mohamed
We compare self-supervised representation learning algorithms which either explicitly quantize the audio data or learn representations without quantization. We find the former to b…
Depth-Adaptive Transformer
Maha Elbayad, Jiatao Gu, Edouard Grave +1
State of the art sequence-to-sequence models for large scale tasks perform a fixed number of computations for each input sequence regardless of whether it is easy or hard to proces…
vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations
Alexei Baevski, Steffen Schneider, Michael Auli
We propose vq-wav2vec to learn discrete representations of audio segments through a wav2vec-style self-supervised context prediction task. The algorithm uses either a gumbel softma…
The Source-Target Domain Mismatch Problem in Machine Translation
Jiajun Shen, Peng-Jen Chen, Matt Le +5
While we live in an increasingly interconnected world, different places still exhibit strikingly different cultures and many events we experience in our every day life pertain only…
Simple and Effective Noisy Channel Modeling for Neural Machine Translation
Kyra Yee, Nathan Ng, Yann N. Dauphin +1
Previous work on neural noisy channel modeling relied on latent variable models that incrementally process the source and target sentence. This makes decoding decisions based on pa…