Search Results for author: Jonathan H. Clark

Found 10 papers, 5 papers with code

XTREME-S: Evaluating Cross-lingual Speech Representations

no code implementations21 Mar 2022 Alexis Conneau, Ankur Bapna, Yu Zhang, Min Ma, Patrick von Platen, Anton Lozhkov, Colin Cherry, Ye Jia, Clara Rivera, Mihir Kale, Daan van Esch, Vera Axelrod, Simran Khanuja, Jonathan H. Clark, Orhan Firat, Michael Auli, Sebastian Ruder, Jason Riesa, Melvin Johnson

Covering 102 languages from 10+ language families, 3 different domains and 4 task families, XTREME-S aims to simplify multilingual speech representation evaluation, as well as catalyze research in "universal" speech representation learning.

Representation Learning Speech Recognition +2

CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation

4 code implementations11 Mar 2021 Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting

Pipelined NLP systems have largely been superseded by end-to-end neural modeling, yet nearly all commonly-used models still require an explicit tokenization step.

Inductive Bias

CapWAP: Captioning with a Purpose

1 code implementation9 Nov 2020 Adam Fisch, Kenton Lee, Ming-Wei Chang, Jonathan H. Clark, Regina Barzilay

In this task, we use question-answer (QA) pairs---a natural expression of information need---from users, instead of reference captions, for both training and post-inference evaluation.

Image Captioning Question Answering +1

Learning to Recognize Dialect Features

no code implementations NAACL 2021 Dorottya Demszky, Devyani Sharma, Jonathan H. Clark, Vinodkumar Prabhakaran, Jacob Eisenstein

Evaluation on a test set of 22 dialect features of Indian English demonstrates that these models learn to recognize many features with high accuracy, and that a few minimal pairs can be as effective for training as thousands of labeled examples.

Locally Non-Linear Learning for Statistical Machine Translation via Discretization and Structured Regularization

no code implementations TACL 2014 Jonathan H. Clark, Chris Dyer, Alon Lavie

Linear models, which support efficient learning and inference, are the workhorses of statistical machine translation; however, linear decision rules are less attractive from a modeling perspective.

Feature Engineering Language Modelling +3

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