Chunting Zhou

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Carnegie Mellon University

Examining and Combating Spurious Features under Distribution Shift

A central goal of machine learning is to learn robust representations th...
06/14/2021 ∙ by Chunting Zhou, et al. ∙ 0 ∙ share

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Luna: Linear Unified Nested Attention

The quadratic computational and memory complexities of the Transformer's...
06/03/2021 ∙ by Xuezhe Ma, et al. ∙ 31 ∙ share

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Learning Structures for Deep Neural Networks

In this paper, we focus on the unsupervised setting for structure learni...
05/27/2021 ∙ by Jinhui Yuan, et al. ∙ 0 ∙ share

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Detecting Hallucinated Content in Conditional Neural Sequence Generation

Neural sequence models can generate highly fluent sentences but recent s...
11/05/2020 ∙ by Chunting Zhou, et al. ∙ 2 ∙ share

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Understanding Knowledge Distillation in Non-autoregressive Machine Translation

Non-autoregressive machine translation (NAT) systems predict a sequence ...
11/07/2019 ∙ by Chunting Zhou, et al. ∙ 0 ∙ share

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FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow

Most sequence-to-sequence (seq2seq) models are autoregressive; they gene...
09/05/2019 ∙ by Xuezhe Ma, et al. ∙ 0 ∙ share

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Handling Syntactic Divergence in Low-resource Machine Translation

Despite impressive empirical successes of neural machine translation (NM...
08/30/2019 ∙ by Chunting Zhou, et al. ∙ 0 ∙ share

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Density Matching for Bilingual Word Embedding

Recent approaches to cross-lingual word embedding have generally been ba...
04/04/2019 ∙ by Chunting Zhou, et al. ∙ 0 ∙ share

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The ARIEL-CMU Systems for LoReHLT18

This paper describes the ARIEL-CMU submissions to the Low Resource Human...
02/24/2019 ∙ by Aditi Chaudhary, et al. ∙ 0 ∙ share

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MAE: Mutual Posterior-Divergence Regularization for Variational AutoEncoders

Variational Autoencoder (VAE), a simple and effective deep generative mo...
01/06/2019 ∙ by Xuezhe Ma, et al. ∙ 6 ∙ share

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Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations

Much work in Natural Language Processing (NLP) has been for resource-ric...
08/28/2018 ∙ by Aditi Chaudhary, et al. ∙ 0 ∙ share

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StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing

Semantic parsing is the task of transducing natural language (NL) uttera...
06/20/2018 ∙ by Pengcheng Yin, et al. ∙ 0 ∙ share

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Multi-space Variational Encoder-Decoders for Semi-supervised Labeled Sequence Transduction

Labeled sequence transduction is a task of transforming one sequence int...
04/06/2017 ∙ by Chunting Zhou, et al. ∙ 0 ∙ share

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A C-LSTM Neural Network for Text Classification

Neural network models have been demonstrated to be capable of achieving ...
11/27/2015 ∙ by Chunting Zhou, et al. ∙ 0 ∙ share

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Category Enhanced Word Embedding

Distributed word representations have been demonstrated to be effective ...
11/27/2015 ∙ by Chunting Zhou, et al. ∙ 0 ∙ share

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Chunting Zhou

Featured Co-authors

Graham Neubig

Tie-Yan Liu

Zhiyuan Liu

Tao Qin

Luke Zettlemoyer

Di Wang

Florian Metze

Alan W Black

Eduard Hovy

Yiming Yang

Jiatao Gu

Examining and Combating Spurious Features under Distribution Shift

Luna: Linear Unified Nested Attention

Learning Structures for Deep Neural Networks

Detecting Hallucinated Content in Conditional Neural Sequence Generation

Understanding Knowledge Distillation in Non-autoregressive Machine Translation

FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow

Handling Syntactic Divergence in Low-resource Machine Translation

Density Matching for Bilingual Word Embedding

The ARIEL-CMU Systems for LoReHLT18

MAE: Mutual Posterior-Divergence Regularization for Variational AutoEncoders

Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations

StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing

Multi-space Variational Encoder-Decoders for Semi-supervised Labeled Sequence Transduction

A C-LSTM Neural Network for Text Classification

Category Enhanced Word Embedding