2.1k citations · 2.7k across the 15 of their papers we have counts for
8 papers · 1 filter
SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network
William Chan, Daniel Park, Chris Lee +3
We present SpeechStew, a speech recognition model that is trained on a combination of various publicly available speech recognition datasets: AMI, Broadcast News, Common Voice, Lib…
Multichannel Generative Language Model: Learning All Possible Factorizations Within and Across Channels
Harris Chan, Jamie Kiros, William Chan
A channel corresponds to a viewpoint or transformation of an underlying meaning. A pair of parallel sentences in English and French express the same underlying meaning, but through…
Non-Autoregressive Machine Translation with Latent Alignments
Chitwan Saharia, William Chan, Saurabh Saxena +1
This paper presents two strong methods, CTC and Imputer, for non-autoregressive machine translation that model latent alignments with dynamic programming. We revisit CTC for machin…
An Empirical Study of Generation Order for Machine Translation
William Chan, Mitchell Stern, Jamie Kiros +1
In this work, we present an empirical study of generation order for machine translation. Building on recent advances in insertion-based modeling, we first introduce a soft order-re…
Big Bidirectional Insertion Representations for Documents
Lala Li, William Chan
The Insertion Transformer is well suited for long form text generation due to its parallel generation capabilities, requiring generation steps to generate tokens.…
KERMIT: Generative Insertion-Based Modeling for Sequences
William Chan, Nikita Kitaev, Kelvin Guu +2
We present KERMIT, a simple insertion-based approach to generative modeling for sequences and sequence pairs. KERMIT models the joint distribution and its decompositions (i.e., mar…