Search Results for author: Jianshu Chen

Found 44 papers, 19 papers with code

Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension

1 code implementation ACL 2022 Chao Zhao, Wenlin Yao, Dian Yu, Kaiqiang Song, Dong Yu, Jianshu Chen

Comprehending a dialogue requires a model to capture diverse kinds of key information in the utterances, which are either scattered around or implicitly implied in different turns of conversations.

PRIMA: Planner-Reasoner Inside a Multi-task Reasoning Agent

no code implementations1 Feb 2022 Daoming Lyu, Bo Liu, Jianshu Chen

We consider the problem of multi-task reasoning (MTR), where an agent can solve multiple tasks via (first-order) logic reasoning.

Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories

2 code implementations EMNLP 2021 Wenlin Yao, Xiaoman Pan, Lifeng Jin, Jianshu Chen, Dian Yu, Dong Yu

We then train a model to identify semantic equivalence between a target word in context and one of its glosses using these aligned inventories, which exhibits strong transfer capability to many WSD tasks.

Word Sense Disambiguation

Blessing of Class Diversity in Pre-training

no code implementations29 Sep 2021 Yulai Zhao, Jianshu Chen, Simon Shaolei Du

We prove that when the classes of the pre-training task (e. g., different words in masked language model task) are sufficiently diverse, in the sense that the least singular value of the last linear layer in pre-training is large, then pre-training can significantly improve the sample efficiency of downstream tasks.

Language Modelling Natural Language Processing

Comprehensive Image Captioning via Scene Graph Decomposition

1 code implementation ECCV 2020 Yiwu Zhong, Li-Wei Wang, Jianshu Chen, Dong Yu, Yin Li

We address the challenging problem of image captioning by revisiting the representation of image scene graph.

Image Captioning

ZPR2: Joint Zero Pronoun Recovery and Resolution using Multi-Task Learning and BERT

no code implementations ACL 2020 Linfeng Song, Kun Xu, Yue Zhang, Jianshu Chen, Dong Yu

Zero pronoun recovery and resolution aim at recovering the dropped pronoun and pointing out its anaphoric mentions, respectively.

Multi-Task Learning

On Effective Parallelization of Monte Carlo Tree Search

no code implementations15 Jun 2020 Anji Liu, Yitao Liang, Ji Liu, Guy Van Den Broeck, Jianshu Chen

Second, and more importantly, we demonstrate how the proposed necessary conditions can be adopted to design more effective parallel MCTS algorithms.

Atari Games

Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension

1 code implementation ACL 2020 Hongyu Gong, Yelong Shen, Dian Yu, Jianshu Chen, Dong Yu

In this paper, we study machine reading comprehension (MRC) on long texts, where a model takes as inputs a lengthy document and a question and then extracts a text span from the document as an answer.

Chunking Machine Reading Comprehension +1

Logical Natural Language Generation from Open-Domain Tables

1 code implementation ACL 2020 Wenhu Chen, Jianshu Chen, Yu Su, Zhiyu Chen, William Yang Wang

To facilitate the study of the proposed logical NLG problem, we use the existing TabFact dataset \cite{chen2019tabfact} featured with a wide range of logical/symbolic inferences as our testbed, and propose new automatic metrics to evaluate the fidelity of generation models w. r. t.\ logical inference.

Text Generation

Improving Pre-Trained Multilingual Model with Vocabulary Expansion

no code implementations CONLL 2019 Hai Wang, Dian Yu, Kai Sun, Jianshu Chen, Dong Yu

However, in multilingual setting, it is extremely resource-consuming to pre-train a deep language model over large-scale corpora for each language.

Language Modelling Machine Reading Comprehension +5

Teaching Pretrained Models with Commonsense Reasoning: A Preliminary KB-Based Approach

no code implementations20 Sep 2019 Shiyang Li, Jianshu Chen, Dian Yu

Recently, pretrained language models (e. g., BERT) have achieved great success on many downstream natural language understanding tasks and exhibit a certain level of commonsense reasoning ability.

Few-Shot Learning Logical Reasoning +3

Learning to Recover Sparse Signals

no code implementations NeurIPS Workshop Deep_Invers 2019 Sichen Zhong, Yue Zhao, Jianshu Chen

In compressed sensing, a primary problem to solve is to reconstruct a high dimensional sparse signal from a small number of observations.

reinforcement-learning

TabFact: A Large-scale Dataset for Table-based Fact Verification

1 code implementation ICLR 2020 Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou, William Yang Wang

To this end, we construct a large-scale dataset called TabFact with 16k Wikipedia tables as the evidence for 118k human-annotated natural language statements, which are labeled as either ENTAILED or REFUTED.

Fact Checking Fact Verification +3

Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization

1 code implementation NeurIPS 2019 Adithya M. Devraj, Jianshu Chen

We consider a generic empirical composition optimization problem, where there are empirical averages present both outside and inside nonlinear loss functions.

From Caesar Cipher to Unsupervised Learning: A New Method for Classifier Parameter Estimation

no code implementations6 Jun 2019 Yu Liu, Li Deng, Jianshu Chen, Chang Wen Chen

To remove the need for the parallel training corpora has practical significance for real-world applications, and it is one of the main goals of unsupervised learning.

General Classification Machine Translation +2

Improving Question Answering with External Knowledge

1 code implementation WS 2019 Xiaoman Pan, Kai Sun, Dian Yu, Jianshu Chen, Heng Ji, Claire Cardie, Dong Yu

We focus on multiple-choice question answering (QA) tasks in subject areas such as science, where we require both broad background knowledge and the facts from the given subject-area reference corpus.

Multiple-choice Question Answering

DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension

1 code implementation1 Feb 2019 Kai Sun, Dian Yu, Jianshu Chen, Dong Yu, Yejin Choi, Claire Cardie

DREAM is likely to present significant challenges for existing reading comprehension systems: 84% of answers are non-extractive, 85% of questions require reasoning beyond a single sentence, and 34% of questions also involve commonsense knowledge.

Dialogue Understanding Multiple-choice +1

Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching

no code implementations ICLR 2019 Chih-Kuan Yeh, Jianshu Chen, Chengzhu Yu, Dong Yu

We consider the problem of training speech recognition systems without using any labeled data, under the assumption that the learner can only access to the input utterances and a phoneme language model estimated from a non-overlapping corpus.

speech-recognition Speech Recognition +1

Coupled Variational Bayes via Optimization Embedding

1 code implementation NeurIPS 2018 Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song

This flexible function class couples the variational distribution with the original parameters in the graphical models, allowing end-to-end learning of the graphical models by back-propagation through the variational distribution.

Variational Inference

Incorporating Structured Commonsense Knowledge in Story Completion

no code implementations1 Nov 2018 Jiaao Chen, Jianshu Chen, Zhou Yu

The ability to select an appropriate story ending is the first step towards perfect narrative comprehension.

Story Completion

Watch the Unobserved: A Simple Approach to Parallelizing Monte Carlo Tree Search

4 code implementations ICLR 2020 Anji Liu, Jianshu Chen, Mingze Yu, Yu Zhai, Xuewen Zhou, Ji Liu

Monte Carlo Tree Search (MCTS) algorithms have achieved great success on many challenging benchmarks (e. g., Computer Go).

XL-NBT: A Cross-lingual Neural Belief Tracking Framework

1 code implementation EMNLP 2018 Wenhu Chen, Jianshu Chen, Yu Su, Xin Wang, Dong Yu, Xifeng Yan, William Yang Wang

Then, we pre-train a state tracker for the source language as a teacher, which is able to exploit easy-to-access parallel data.

Transfer Learning

M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search

no code implementations NeurIPS 2018 Yelong Shen, Jianshu Chen, Po-Sen Huang, Yuqing Guo, Jianfeng Gao

In order to effectively train the agent from sparse rewards, we combine MCTS with the neural policy to generate trajectories yielding more positive rewards.

Ranked #35 on Link Prediction on WN18RR (Hits@3 metric)

Knowledge Base Completion Link Prediction +1

SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation

no code implementations ICML 2018 Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song

When function approximation is used, solving the Bellman optimality equation with stability guarantees has remained a major open problem in reinforcement learning for decades.

Q-Learning reinforcement-learning

Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes

no code implementations NeurIPS 2017 Jianshu Chen, Chong Wang, Lin Xiao, Ji He, Lihong Li, Li Deng

In sequential decision making, it is often important and useful for end users to understand the underlying patterns or causes that lead to the corresponding decisions.

Decision Making Q-Learning +2

A Learning-to-Infer Method for Real-Time Power Grid Multi-Line Outage Identification

no code implementations21 Oct 2017 Yue Zhao, Jianshu Chen, H. Vincent Poor

Identifying a potentially large number of simultaneous line outages in power transmission networks in real time is a computationally hard problem.

Stochastic Variance Reduction Methods for Policy Evaluation

no code implementations ICML 2017 Simon S. Du, Jianshu Chen, Lihong Li, Lin Xiao, Dengyong Zhou

Policy evaluation is a crucial step in many reinforcement-learning procedures, which estimates a value function that predicts states' long-term value under a given policy.

reinforcement-learning

Unsupervised Sequence Classification using Sequential Output Statistics

no code implementations NeurIPS 2017 Yu Liu, Jianshu Chen, Li Deng

Although it is harder to optimize in its functional form, a stochastic primal-dual gradient method is developed to effectively solve the problem.

Classification General Classification

Character-level Deep Conflation for Business Data Analytics

2 code implementations8 Feb 2017 Zhe Gan, P. D. Singh, Ameet Joshi, Xiaodong He, Jianshu Chen, Jianfeng Gao, Li Deng

Connecting different text attributes associated with the same entity (conflation) is important in business data analytics since it could help merge two different tables in a database to provide a more comprehensive profile of an entity.

Unsupervised Learning of Predictors from Unpaired Input-Output Samples

no code implementations15 Jun 2016 Jianshu Chen, Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng

In particular, we show that with regularization via a generative model, learning with the proposed unsupervised objective function converges to an optimal solution.

Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads

1 code implementation EMNLP 2016 Ji He, Mari Ostendorf, Xiaodong He, Jianshu Chen, Jianfeng Gao, Lihong Li, Li Deng

We introduce an online popularity prediction and tracking task as a benchmark task for reinforcement learning with a combinatorial, natural language action space.

reinforcement-learning

Deep Reinforcement Learning with a Natural Language Action Space

3 code implementations ACL 2016 Ji He, Jianshu Chen, Xiaodong He, Jianfeng Gao, Lihong Li, Li Deng, Mari Ostendorf

This paper introduces a novel architecture for reinforcement learning with deep neural networks designed to handle state and action spaces characterized by natural language, as found in text-based games.

Q-Learning reinforcement-learning +1

Recurrent Reinforcement Learning: A Hybrid Approach

no code implementations10 Sep 2015 Xiujun Li, Lihong Li, Jianfeng Gao, Xiaodong He, Jianshu Chen, Li Deng, Ji He

Successful applications of reinforcement learning in real-world problems often require dealing with partially observable states.

reinforcement-learning

End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture

1 code implementation NeurIPS 2015 Jianshu Chen, Ji He, Yelong Shen, Lin Xiao, Xiaodong He, Jianfeng Gao, Xinying Song, Li Deng

We develop a fully discriminative learning approach for supervised Latent Dirichlet Allocation (LDA) model using Back Propagation (i. e., BP-sLDA), which maximizes the posterior probability of the prediction variable given the input document.

General Classification Topic Models

A Deep Embedding Model for Co-occurrence Learning

no code implementations11 Apr 2015 Yelong Shen, Ruoming Jin, Jianshu Chen, Xiaodong He, Jianfeng Gao, Li Deng

Co-occurrence Data is a common and important information source in many areas, such as the word co-occurrence in the sentences, friends co-occurrence in social networks and products co-occurrence in commercial transaction data, etc, which contains rich correlation and clustering information about the items.

Dictionary Learning over Distributed Models

no code implementations6 Feb 2014 Jianshu Chen, Zaid J. Towfic, Ali H. Sayed

In this paper, we consider learning dictionary models over a network of agents, where each agent is only in charge of a portion of the dictionary elements.

Dictionary Learning

Distributed Policy Evaluation Under Multiple Behavior Strategies

no code implementations30 Dec 2013 Sergio Valcarcel Macua, Jianshu Chen, Santiago Zazo, Ali H. Sayed

We apply diffusion strategies to develop a fully-distributed cooperative reinforcement learning algorithm in which agents in a network communicate only with their immediate neighbors to improve predictions about their environment.

A Primal-Dual Method for Training Recurrent Neural Networks Constrained by the Echo-State Property

no code implementations24 Nov 2013 Jianshu Chen, Li Deng

We present an architecture of a recurrent neural network (RNN) with a fully-connected deep neural network (DNN) as its feature extractor.

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