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.
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.
2 code implementations • 1 May 2022 • Songlin Yang, Wei Liu, Kewei Tu
Recent research found it beneficial to use large state spaces for HMMs and PCFGs.
1 code implementation • 7 Apr 2022 • Songlin Yang, Kewei Tu
Second-order semantic parsing with end-to-end mean-field inference has been shown good performance.
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.
1 code implementation • 1 Mar 2022 • Xinyu Wang, Yongliang Shen, Jiong Cai, Tao Wang, Xiaobin Wang, Pengjun Xie, Fei Huang, Weiming Lu, Yueting Zhuang, Kewei Tu, Wei Lu, Yong Jiang
Our system wins 10 out of 13 tracks in the MultiCoNER shared task.
1 code implementation • 13 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.
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.
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.
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.
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.
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.
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.
no code implementations • ACL (IWPT) 2021 • Xinyu Wang, Zixia Jia, Yong Jiang, Kewei Tu
This paper describes the system used in submission from SHANGHAITECH team to the IWPT 2021 Shared Task.
1 code implementation • ACL 2021 • Songlin Yang, Yanpeng Zhao, Kewei Tu
Neural lexicalized PCFGs (L-PCFGs) have been shown effective in grammar induction.
Ranked #2 on
Constituency Grammar Induction
on PTB
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.
Ranked #1 on
Named Entity Recognition
on WNUT 2017
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.
Ranked #3 on
Constituency Grammar Induction
on PTB
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.
no code implementations • 12 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.
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.
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.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Zechuan Hu, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu
The neural linear-chain CRF model is one of the most widely-used approach to sequence labeling.
no code implementations • 10 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.
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.
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.
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.
Ranked #1 on
Dependency Grammar Induction
on WSJ10
1 code implementation • ACL 2021 • Xinyu Wang, Yong Jiang, Zhaohui Yan, Zixia Jia, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu
The objective function of knowledge distillation is typically the cross-entropy between the teacher and the student's output distributions.
1 code implementation • Asian Chapter of the Association for Computational Linguistics 2020 • Xinyu Wang, Kewei Tu
In this paper, we propose second-order graph-based neural dependency parsing using message passing and end-to-end neural networks.
Ranked #1 on
Dependency Parsing
on Chinese Treebank
1 code implementation • ACL 2021 • Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu
Pretrained contextualized embeddings are powerful word representations for structured prediction tasks.
Ranked #1 on
Part-Of-Speech Tagging
on ARK
no code implementations • COLING 2020 • Wenjuan Han, Yong Jiang, Hwee Tou Ng, Kewei Tu
Syntactic dependency parsing is an important task in natural language processing.
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.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu
Recent work proposes a family of contextual embeddings that significantly improves the accuracy of sequence labelers over non-contextual embeddings.
Ranked #2 on
Chunking
on CoNLL 2003 (German)
1 code implementation • EMNLP 2020 • Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu
The linear-chain Conditional Random Field (CRF) model is one of the most widely-used neural sequence labeling approaches.
Ranked #3 on
Chunking
on CoNLL 2003 (German)
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.
1 code implementation • ACL 2020 • Bo Pang, Erik Nijkamp, Wenjuan Han, Linqi Zhou, Yixian Liu, Kewei Tu
Open-domain dialogue generation has gained increasing attention in Natural Language Processing.
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.
1 code implementation • WS 2020 • Xinyu Wang, Yong Jiang, Kewei Tu
This paper presents the system used in our submission to the \textit{IWPT 2020 Shared Task}.
1 code implementation • ACL 2020 • Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Fei Huang, Kewei Tu
Multilingual sequence labeling is a task of predicting label sequences using a single unified model for multiple languages.
1 code implementation • CONLL 2019 • Xinyu Wang, Yixian Liu, Zixia Jia, Chengyue Jiang, Kewei Tu
This paper presents the system used in our submission to the \textit{CoNLL 2019 shared task: Cross-Framework Meaning Representation Parsing}.
no code implementations • 28 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.
no code implementations • IJCNLP 2019 • Yong Jiang, Wenjuan Han, Kewei Tu
Grammar induction aims to discover syntactic structures from unannotated sentences.
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.
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.
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.
Ranked #2 on
Dependency Grammar Induction
on WSJ10
Constituency Grammar Induction
Dependency Grammar Induction
+1
1 code implementation • ACL 2019 • Liwen Zhang, Kewei Tu, Yue Zhang
Neural models have been investigated for sentiment classification over constituent trees.
4 code implementations • ACL 2019 • Xinyu Wang, Jingxian Huang, Kewei Tu
Semantic dependency parsing aims to identify semantic relationships between words in a sentence that form a graph.
Ranked #3 on
Semantic Dependency Parsing
on DM
no code implementations • 13 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.
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.
1 code implementation • 10 Apr 2018 • Lin Qiu, Hao Zhou, Yanru Qu, Wei-Nan Zhang, Suoheng Li, Shu Rong, Dongyu Ru, Lihua Qian, Kewei Tu, Yong Yu
Information Extraction (IE) refers to automatically extracting structured relation tuples from unstructured texts.
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.
no code implementations • 16 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.
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.
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
no code implementations • EMNLP 2017 • Yong Jiang, Wenjuan Han, Kewei Tu
Unsupervised dependency parsing aims to learn a dependency parser from unannotated sentences.
Dependency Grammar Induction
Unsupervised Dependency Parsing
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.
no code implementations • 8 Sep 2016 • Shanbo Chu, Yong Jiang, Kewei Tu
Probabilistic modeling is one of the foundations of modern machine learning and artificial intelligence.
no code implementations • 2 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.
no code implementations • 2 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.
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.
no code implementations • 29 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.