Search Results for author: Kewei Tu

Found 66 papers, 33 papers with code

Cold-Start and Interpretability: Turning Regular Expressions into Trainable Recurrent Neural Networks

no code implementations EMNLP 2020 Chengyue Jiang, Yinggong Zhao, Shanbo Chu, Libin Shen, Kewei Tu

On the other hand, symbolic rules such as regular expressions are interpretable, require no training, and often achieve decent accuracy; but rules cannot benefit from labeled data when available and hence underperform neural networks in rich-resource scenarios.

Text Classification

Neuralizing Regular Expressions for Slot Filling

no code implementations EMNLP 2021 Chengyue Jiang, Zijian Jin, Kewei Tu

Neural models and symbolic rules such as regular expressions have their respective merits and weaknesses.

Slot Filling

Nested Named Entity Recognition as Latent Lexicalized Constituency Parsing

no code implementations ACL 2022 Chao Lou, Songlin Yang, Kewei Tu

They treat nested entities as partially-observed constituency trees and propose the masked inside algorithm for partial marginalization.

Constituency Parsing Entity Typing +2

ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition

1 code implementation13 Dec 2021 Xinyu Wang, Min Gui, Yong Jiang, Zixia Jia, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

As text representations take the most important role in MNER, in this paper, we propose {\bf I}mage-{\bf t}ext {\bf A}lignments (ITA) to align image features into the textual space, so that the attention mechanism in transformer-based pretrained textual embeddings can be better utilized.

Named Entity Recognition

Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks

1 code implementation ACL 2022 Songlin Yang, Kewei Tu

Constituency parsing and nested named entity recognition (NER) are similar tasks since they both aim to predict a collection of nested and non-crossing spans.

Constituency Parsing NER +2

Combining (second-order) graph-based and headed-span-based projective dependency parsing

1 code implementation Findings (ACL) 2022 Songlin Yang, Kewei Tu

Graph-based methods, which decompose the score of a dependency tree into scores of dependency arcs, are popular in dependency parsing for decades.

Dependency Parsing

Headed-Span-Based Projective Dependency Parsing

1 code implementation ACL 2022 Songlin Yang, Kewei Tu

In a projective dependency tree, the largest subtree rooted at each word covers a contiguous sequence (i. e., a span) in the surface order.

Constituency Parsing Dependency Parsing

Multi-View Cross-Lingual Structured Prediction with Minimum Supervision

no code implementations ACL 2021 Zechuan Hu, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

In structured prediction problems, cross-lingual transfer learning is an efficient way to train quality models for low-resource languages, and further improvement can be obtained by learning from multiple source languages.

Cross-Lingual Transfer Structured Prediction +1

Adapting Unsupervised Syntactic Parsing Methodology for Discourse Dependency Parsing

no code implementations ACL 2021 Liwen Zhang, Ge Wang, Wenjuan Han, Kewei Tu

In this paper, we propose a simple yet effective method to adapt unsupervised syntactic dependency parsing methodology for unsupervised discourse dependency parsing.

Dependency Parsing Discourse Parsing

Risk Minimization for Zero-shot Sequence Labeling

no code implementations ACL 2021 Zechuan Hu, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

In this paper, we propose a novel unified framework for zero-shot sequence labeling with minimum risk training and design a new decomposable risk function that models the relations between the predicted labels from the source models and the true labels.

Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning

1 code implementation ACL 2021 Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

We find empirically that the contextual representations computed on the retrieval-based input view, constructed through the concatenation of a sentence and its external contexts, can achieve significantly improved performance compared to the original input view based only on the sentence.

Named Entity Recognition NER

PCFGs Can Do Better: Inducing Probabilistic Context-Free Grammars with Many Symbols

1 code implementation NAACL 2021 Songlin Yang, Yanpeng Zhao, Kewei Tu

In this work, we present a new parameterization form of PCFGs based on tensor decomposition, which has at most quadratic computational complexity in the symbol number and therefore allows us to use a much larger number of symbols.

Constituency Grammar Induction

Unsupervised Natural Language Parsing (Introductory Tutorial)

no code implementations EACL 2021 Kewei Tu, Yong Jiang, Wenjuan Han, Yanpeng Zhao

Unsupervised parsing learns a syntactic parser from training sentences without parse tree annotations.

Constrained Text Generation with Global Guidance -- Case Study on CommonGen

no code implementations12 Mar 2021 Yixian Liu, Liwen Zhang, Wenjuan Han, Yue Zhang, Kewei Tu

We focus on CommonGen, the task of generating text based on a set of concepts, as a representative task of constrained text generation.

Common Sense Reasoning reinforcement-learning +1

Deep Inside-outside Recursive Autoencoder with All-span Objective

no code implementations COLING 2020 Ruyue Hong, Jiong Cai, Kewei Tu

Deep inside-outside recursive autoencoder (DIORA) is a neural-based model designed for unsupervised constituency parsing.

Constituency Parsing

Semi-Supervised Dependency Parsing with Arc-Factored Variational Autoencoding

no code implementations COLING 2020 Ge Wang, Kewei Tu

By relaxing the tree constraint in both the encoder and the decoder during training, we make the learning of our model fully arc-factored and thus circumvent the challenges brought by the tree constraint.

Dependency Parsing

Neural Latent Dependency Model for Sequence Labeling

no code implementations10 Nov 2020 Yang Zhou, Yong Jiang, Zechuan Hu, Kewei Tu

One limitation of linear chain CRFs is their inability to model long-range dependencies between labels.

Learning Numeral Embedding

1 code implementation Findings of the Association for Computational Linguistics 2020 Chengyue Jiang, Zhonglin Nian, Kaihao Guo, Shanbo Chu, Yinggong Zhao, Libin Shen, Kewei Tu

Numeral embeddings represented in this manner can be plugged into existing word embedding learning approaches such as skip-gram for training.

Word Similarity

Unsupervised Cross-Lingual Adaptation of Dependency Parsers Using CRF Autoencoders

1 code implementation Findings of the Association for Computational Linguistics 2020 Zhao Li, Kewei Tu

We consider the task of cross-lingual adaptation of dependency parsers without annotated target corpora and parallel corpora.

Second-Order Unsupervised Neural Dependency Parsing

1 code implementation COLING 2020 Songlin Yang, Yong Jiang, Wenjuan Han, Kewei Tu

Inspired by second-order supervised dependency parsing, we proposed a second-order extension of unsupervised neural dependency models that incorporate grandparent-child or sibling information.

Dependency Grammar Induction

Adversarial Attack and Defense of Structured Prediction Models

1 code implementation EMNLP 2020 Wenjuan Han, Liwen Zhang, Yong Jiang, Kewei Tu

To address these problems, we propose a novel and unified framework that learns to attack a structured prediction model using a sequence-to-sequence model with feedbacks from multiple reference models of the same structured prediction task.

Adversarial Attack Dependency Parsing +2

Semi-Supervised Semantic Dependency Parsing Using CRF Autoencoders

1 code implementation ACL 2020 Zixia Jia, Youmi Ma, Jiong Cai, Kewei Tu

Semantic dependency parsing, which aims to find rich bi-lexical relationships, allows words to have multiple dependency heads, resulting in graph-structured representations.

Dependency Parsing Semantic Dependency Parsing

An Empirical Comparison of Unsupervised Constituency Parsing Methods

no code implementations ACL 2020 Jun Li, Yifan Cao, Jiong Cai, Yong Jiang, Kewei Tu

Unsupervised constituency parsing aims to learn a constituency parser from a training corpus without parse tree annotations.

Constituency Parsing

Learning Numeral Embeddings

no code implementations28 Dec 2019 Chengyue Jiang, Zhonglin Nian, Kaihao Guo, Shanbo Chu, Yinggong Zhao, Libin Shen, Kewei Tu

Numeral embeddings represented in this manner can be plugged into existing word embedding learning approaches such as skip-gram for training.

Word Similarity

A Regularization-based Framework for Bilingual Grammar Induction

no code implementations IJCNLP 2019 Yong Jiang, Wenjuan Han, Kewei Tu

Grammar induction aims to discover syntactic structures from unannotated sentences.

Multilingual Grammar Induction with Continuous Language Identification

no code implementations IJCNLP 2019 Wenjuan Han, Ge Wang, Yong Jiang, Kewei Tu

The key to multilingual grammar induction is to couple grammar parameters of different languages together by exploiting the similarity between languages.

Language Identification

Bidirectional Transition-Based Dependency Parsing

1 code implementation AAAI 2019 Yunzhe Yuan, Yong Jiang, Kewei Tu

Traditionally, a transitionbased dependency parser processes an input sentence and predicts a sequence of parsing actions in a left-to-right manner.

Transition-Based Dependency Parsing

Enhancing Unsupervised Generative Dependency Parser with Contextual Information

no code implementations ACL 2019 Wenjuan Han, Yong Jiang, Kewei Tu

In this paper, we propose a novel probabilistic model called discriminative neural dependency model with valence (D-NDMV) that generates a sentence and its parse from a continuous latent representation, which encodes global contextual information of the generated sentence.

Constituency Grammar Induction Dependency Grammar Induction +1

Latent Variable Sentiment Grammar

1 code implementation ACL 2019 Liwen Zhang, Kewei Tu, Yue Zhang

Neural models have been investigated for sentiment classification over constituent trees.

General Classification Sentiment Analysis

Language Style Transfer from Sentences with Arbitrary Unknown Styles

no code implementations13 Aug 2018 Yanpeng Zhao, Wei Bi, Deng Cai, Xiaojiang Liu, Kewei Tu, Shuming Shi

Then, by recombining the content with the target style, we decode a sentence aligned in the target domain.

Sentence ReWriting Style Transfer

Gaussian Mixture Latent Vector Grammars

1 code implementation ACL 2018 Yanpeng Zhao, Liwen Zhang, Kewei Tu

We introduce Latent Vector Grammars (LVeGs), a new framework that extends latent variable grammars such that each nonterminal symbol is associated with a continuous vector space representing the set of (infinitely many) subtypes of the nonterminal.

Constituency Parsing Part-Of-Speech Tagging

Semi-supervised Structured Prediction with Neural CRF Autoencoder

1 code implementation EMNLP 2017 Xiao Zhang, Yong Jiang, Hao Peng, Kewei Tu, Dan Goldwasser

In this paper we propose an end-to-end neural CRF autoencoder (NCRF-AE) model for semi-supervised learning of sequential structured prediction problems.

Part-Of-Speech Tagging POS +1

Maximum A Posteriori Inference in Sum-Product Networks

no code implementations16 Aug 2017 Jun Mei, Yong Jiang, Kewei Tu

For the theoretical part, we reduce general MAP inference to its special case without evidence and hidden variables; we also show that it is NP-hard to approximate the MAP problem to $2^{n^\epsilon}$ for fixed $0 \leq \epsilon < 1$, where $n$ is the input size.

Structured Attentions for Visual Question Answering

1 code implementation ICCV 2017 Chen Zhu, Yanpeng Zhao, Shuaiyi Huang, Kewei Tu, Yi Ma

In this paper, we demonstrate the importance of encoding such relations by showing the limited effective receptive field of ResNet on two datasets, and propose to model the visual attention as a multivariate distribution over a grid-structured Conditional Random Field on image regions.

Visual Question Answering VQA

CRF Autoencoder for Unsupervised Dependency Parsing

1 code implementation EMNLP 2017 Jiong Cai, Yong Jiang, Kewei Tu

The encoder part of our model is discriminative and globally normalized which allows us to use rich features as well as universal linguistic priors.

Dependency Grammar Induction Unsupervised Dependency Parsing

Dependency Grammar Induction with Neural Lexicalization and Big Training Data

no code implementations EMNLP 2017 Wenjuan Han, Yong Jiang, Kewei Tu

We study the impact of big models (in terms of the degree of lexicalization) and big data (in terms of the training corpus size) on dependency grammar induction.

Dependency Grammar Induction

Latent Dependency Forest Models

no code implementations8 Sep 2016 Shanbo Chu, Yong Jiang, Kewei Tu

Probabilistic modeling is one of the foundations of modern machine learning and artificial intelligence.

Stochastic And-Or Grammars: A Unified Framework and Logic Perspective

no code implementations2 Jun 2015 Kewei Tu

Stochastic And-Or grammars (AOG) extend traditional stochastic grammars of language to model other types of data such as images and events.

Relational Reasoning

Mapping Energy Landscapes of Non-Convex Learning Problems

no code implementations2 Oct 2014 Maria Pavlovskaia, Kewei Tu, Song-Chun Zhu

In many statistical learning problems, the target functions to be optimized are highly non-convex in various model spaces and thus are difficult to analyze.

Unsupervised Structure Learning of Stochastic And-Or Grammars

no code implementations NeurIPS 2013 Kewei Tu, Maria Pavlovskaia, Song-Chun Zhu

Stochastic And-Or grammars compactly represent both compositionality and reconfigurability and have been used to model different types of data such as images and events.

Joint Video and Text Parsing for Understanding Events and Answering Queries

no code implementations29 Aug 2013 Kewei Tu, Meng Meng, Mun Wai Lee, Tae Eun Choe, Song-Chun Zhu

We present a probabilistic generative model for joint parsing that captures the relations between the input video/text, their corresponding parse graphs and the joint parse graph.

Semantic Parsing

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