Search Results for author: Xuezhe Ma

Found 39 papers, 21 papers with code

AESOP: Paraphrase Generation with Adaptive Syntactic Control

1 code implementation EMNLP 2021 Jiao Sun, Xuezhe Ma, Nanyun Peng

We propose to control paraphrase generation through carefully chosen target syntactic structures to generate more proper and higher quality paraphrases.

Data Augmentation Language Modelling +1

Prompt Consistency for Zero-Shot Task Generalization

1 code implementation29 Apr 2022 Chunting Zhou, Junxian He, Xuezhe Ma, Taylor Berg-Kirkpatrick, Graham Neubig

One of the most impressive results of recent NLP history is the ability of pre-trained language models to solve new tasks in a zero-shot setting.

Learning Representations Robust to Group Shifts and Adversarial Examples

no code implementations18 Feb 2022 Ming-Chang Chiu, Xuezhe Ma

Despite the high performance achieved by deep neural networks on various tasks, extensive studies have demonstrated that small tweaks in the input could fail the model predictions.

Representation Learning

Towards a Unified View of Parameter-Efficient Transfer Learning

1 code implementation ICLR 2022 Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, Graham Neubig

Furthermore, our unified framework enables the transfer of design elements across different approaches, and as a result we are able to instantiate new parameter-efficient fine-tuning methods that tune less parameters than previous methods while being more effective, achieving comparable results to fine-tuning all parameters on all four tasks.

Machine Translation Text Classification +2

Examining and Combating Spurious Features under Distribution Shift

1 code implementation14 Jun 2021 Chunting Zhou, Xuezhe Ma, Paul Michel, Graham Neubig

Group distributionally robust optimization (DRO) provides an effective tool to alleviate covariate shift by minimizing the worst-case training loss over a set of pre-defined groups.

COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences

1 code implementation Findings (ACL) 2021 Shikhar Singh, Nuan Wen, Yu Hou, Pegah Alipoormolabashi, Te-Lin Wu, Xuezhe Ma, Nanyun Peng

To this end, we introduce a new commonsense reasoning benchmark dataset comprising natural language true/false statements, with each sample paired with its complementary counterpart, resulting in 4k sentence pairs.

Pretrained Language Models

Personalized Response Generation via Generative Split Memory Network

1 code implementation NAACL 2021 Yuwei Wu, Xuezhe Ma, Diyi Yang

Despite the impressive successes of generation and dialogue systems, how to endow a text generation system with particular personality traits to deliver more personalized responses remains under-investigated.

Response Generation Text Generation

Apollo: An Adaptive Parameter-wised Diagonal Quasi-Newton Method for Nonconvex Stochastic Optimization

no code implementations1 Jan 2021 Xuezhe Ma

In this paper, we introduce Apollo, a quasi-newton method for noncovex stochastic optimization, which dynamically incorporates the curvature of the loss function by approximating the Hessian via a diagonal matrix.

Stochastic Optimization

Apollo: An Adaptive Parameter-wise Diagonal Quasi-Newton Method for Nonconvex Stochastic Optimization

2 code implementations28 Sep 2020 Xuezhe Ma

In this paper, we introduce Apollo, a quasi-Newton method for nonconvex stochastic optimization, which dynamically incorporates the curvature of the loss function by approximating the Hessian via a diagonal matrix.

Stochastic Optimization

A Two-Step Approach for Implicit Event Argument Detection

no code implementations ACL 2020 Zhisong Zhang, Xiang Kong, Zhengzhong Liu, Xuezhe Ma, Eduard Hovy

It remains a challenge to detect implicit arguments, calling for more future work of document-level modeling for this task.

Decoupling Global and Local Representations via Invertible Generative Flows

1 code implementation ICLR 2021 Xuezhe Ma, Xiang Kong, Shanghang Zhang, Eduard Hovy

In this work, we propose a new generative model that is capable of automatically decoupling global and local representations of images in an entirely unsupervised setting, by embedding a generative flow in the VAE framework to model the decoder.

Density Estimation Image Generation +2

Cross-lingual Dependency Parsing with Unlabeled Auxiliary Languages

1 code implementation CONLL 2019 Wasi Uddin Ahmad, Zhisong Zhang, Xuezhe Ma, Kai-Wei Chang, Nanyun Peng

We conduct experiments on cross-lingual dependency parsing where we train a dependency parser on a source language and transfer it to a wide range of target languages.

Cross-Lingual Transfer Dependency Parsing +2

Handling Syntactic Divergence in Low-resource Machine Translation

1 code implementation IJCNLP 2019 Chunting Zhou, Xuezhe Ma, Junjie Hu, Graham Neubig

Despite impressive empirical successes of neural machine translation (NMT) on standard benchmarks, limited parallel data impedes the application of NMT models to many language pairs.

Data Augmentation Machine Translation +1

An Empirical Investigation of Structured Output Modeling for Graph-based Neural Dependency Parsing

1 code implementation ACL 2019 Zhisong Zhang, Xuezhe Ma, Eduard Hovy

In this paper, we investigate the aspect of structured output modeling for the state-of-the-art graph-based neural dependency parser (Dozat and Manning, 2017).

14 Dependency Parsing

Choosing Transfer Languages for Cross-Lingual Learning

1 code implementation ACL 2019 Yu-Hsiang Lin, Chian-Yu Chen, Jean Lee, Zirui Li, Yuyan Zhang, Mengzhou Xia, Shruti Rijhwani, Junxian He, Zhisong Zhang, Xuezhe Ma, Antonios Anastasopoulos, Patrick Littell, Graham Neubig

Cross-lingual transfer, where a high-resource transfer language is used to improve the accuracy of a low-resource task language, is now an invaluable tool for improving performance of natural language processing (NLP) on low-resource languages.

Cross-Lingual Transfer

Density Matching for Bilingual Word Embedding

1 code implementation NAACL 2019 Chunting Zhou, Xuezhe Ma, Di Wang, Graham Neubig

Recent approaches to cross-lingual word embedding have generally been based on linear transformations between the sets of embedding vectors in the two languages.

Bilingual Lexicon Induction Word Embeddings +1

MaCow: Masked Convolutional Generative Flow

2 code implementations NeurIPS 2019 Xuezhe Ma, Xiang Kong, Shanghang Zhang, Eduard Hovy

Flow-based generative models, conceptually attractive due to tractability of both the exact log-likelihood computation and latent-variable inference, and efficiency of both training and sampling, has led to a number of impressive empirical successes and spawned many advanced variants and theoretical investigations.

Density Estimation Image Generation

MAE: Mutual Posterior-Divergence Regularization for Variational AutoEncoders

no code implementations ICLR 2019 Xuezhe Ma, Chunting Zhou, Eduard Hovy

Variational Autoencoder (VAE), a simple and effective deep generative model, has led to a number of impressive empirical successes and spawned many advanced variants and theoretical investigations.

Density Estimation Image Generation +1

Stack-Pointer Networks for Dependency Parsing

3 code implementations ACL 2018 Xuezhe Ma, Zecong Hu, Jingzhou Liu, Nanyun Peng, Graham Neubig, Eduard Hovy

Combining pointer networks~\citep{vinyals2015pointer} with an internal stack, the proposed model first reads and encodes the whole sentence, then builds the dependency tree top-down (from root-to-leaf) in a depth-first fashion.

Dependency Parsing

STCP: Simplified-Traditional Chinese Conversion and Proofreading

no code implementations IJCNLP 2017 Jiarui Xu, Xuezhe Ma, Chen-Tse Tsai, Eduard Hovy

This paper aims to provide an effective tool for conversion between Simplified Chinese and Traditional Chinese.

Softmax Q-Distribution Estimation for Structured Prediction: A Theoretical Interpretation for RAML

no code implementations ICLR 2018 Xuezhe Ma, Pengcheng Yin, Jingzhou Liu, Graham Neubig, Eduard Hovy

Reward augmented maximum likelihood (RAML), a simple and effective learning framework to directly optimize towards the reward function in structured prediction tasks, has led to a number of impressive empirical successes.

Dependency Parsing Image Captioning +4

An Interpretable Knowledge Transfer Model for Knowledge Base Completion

no code implementations ACL 2017 Qizhe Xie, Xuezhe Ma, Zihang Dai, Eduard Hovy

Knowledge bases are important resources for a variety of natural language processing tasks but suffer from incompleteness.

Knowledge Base Completion Transfer Learning

Neural Probabilistic Model for Non-projective MST Parsing

no code implementations IJCNLP 2017 Xuezhe Ma, Eduard Hovy

In this paper, we propose a probabilistic parsing model, which defines a proper conditional probability distribution over non-projective dependency trees for a given sentence, using neural representations as inputs.


Dropout with Expectation-linear Regularization

no code implementations26 Sep 2016 Xuezhe Ma, Yingkai Gao, Zhiting Hu, Yao-Liang Yu, Yuntian Deng, Eduard Hovy

Algorithmically, we show that our proposed measure of the inference gap can be used to regularize the standard dropout training objective, resulting in an \emph{explicit} control of the gap.

Image Classification

Harnessing Deep Neural Networks with Logic Rules

2 code implementations ACL 2016 Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, Eduard Hovy, Eric Xing

Combining deep neural networks with structured logic rules is desirable to harness flexibility and reduce uninterpretability of the neural models.

Named Entity Recognition Sentiment Analysis

Unsupervised Ranking Model for Entity Coreference Resolution

no code implementations NAACL 2016 Xuezhe Ma, Zhengzhong Liu, Eduard Hovy

Coreference resolution is one of the first stages in deep language understanding and its importance has been well recognized in the natural language processing community.

Coreference Resolution

End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

25 code implementations ACL 2016 Xuezhe Ma, Eduard Hovy

State-of-the-art sequence labeling systems traditionally require large amounts of task-specific knowledge in the form of hand-crafted features and data pre-processing.

Feature Engineering Named Entity Recognition +3

Probabilistic Models for High-Order Projective Dependency Parsing

no code implementations14 Feb 2015 Xuezhe Ma, Hai Zhao

This paper presents generalized probabilistic models for high-order projective dependency parsing and an algorithmic framework for learning these statistical models involving dependency trees.

Dependency Parsing

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