709 citations · 2.5k across the 39 of their papers we have counts for
40 papers
Improving Machine Reading Comprehension with Contextualized Commonsense Knowledge
Kai Sun, Dian Yu, Jianshu Chen +2
In this paper, we aim to extract commonsense knowledge to improve machine reading comprehension. We propose to represent relations implicitly by situating structured knowledge in a…
On Effective Parallelization of Monte Carlo Tree Search
Anji Liu, Yitao Liang, Ji Liu +2
Despite its groundbreaking success in Go and computer games, Monte Carlo Tree Search (MCTS) is computationally expensive as it requires a substantial number of rollouts to construc…
Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension
Hongyu Gong, Yelong Shen, Dian Yu +2
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 do…
Logical Natural Language Generation from Open-Domain Tables
Wenhu Chen, Jianshu Chen, Yu Su +2
Neural natural language generation (NLG) models have recently shown remarkable progress in fluency and coherence. However, existing studies on neural NLG are primarily focused on s…
Improving Pre-Trained Multilingual Models with Vocabulary Expansion
Hai Wang, Dian Yu, Kai Sun +2
Recently, pre-trained language models have achieved remarkable success in a broad range of natural language processing tasks. However, in multilingual setting, it is extremely reso…
Teaching Pretrained Models with Commonsense Reasoning: A Preliminary KB-Based Approach
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 rea…
TabFact: A Large-scale Dataset for Table-based Fact Verification
Wenhu Chen, Hongmin Wang, Jianshu Chen +5
The problem of verifying whether a textual hypothesis holds based on the given evidence, also known as fact verification, plays an important role in the study of natural language u…
Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention
Wenhu Chen, Jianshu Chen, Pengda Qin +2
Semantically controlled neural response generation on limited-domain has achieved great performance. However, moving towards multi-domain large-scale scenarios are shown to be diff…
From Caesar Cipher to Unsupervised Learning: A New Method for Classifier Parameter Estimation
Yu Liu, Li Deng, Jianshu Chen +1
Many important classification problems, such as object classification, speech recognition, and machine translation, have been tackled by the supervised learning paradigm in the pas…
Evidence Sentence Extraction for Machine Reading Comprehension
Hai Wang, Dian Yu, Kai Sun +4
Remarkable success has been achieved in the last few years on some limited machine reading comprehension (MRC) tasks. However, it is still difficult to interpret the predictions of…
DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension
Kai Sun, Dian Yu, Jianshu Chen +3
We present DREAM, the first dialogue-based multiple-choice reading comprehension dataset. Collected from English-as-a-foreign-language examinations designed by human experts to eva…
Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching
Chih-Kuan Yeh, Jianshu Chen, Chengzhu Yu +1
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 p…
Incorporating Structured Commonsense Knowledge in Story Completion
Jiaao Chen, Jianshu Chen, Zhou Yu
The ability to select an appropriate story ending is the first step towards perfect narrative comprehension. Story ending prediction requires not only the explicit clues within the…
XL-NBT: A Cross-lingual Neural Belief Tracking Framework
Wenhu Chen, Jianshu Chen, Yu Su +4
Task-oriented dialog systems are becoming pervasive, and many companies heavily rely on them to complement human agents for customer service in call centers. With globalization, th…
M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search
Yelong Shen, Jianshu Chen, Po-Sen Huang +2
Learning to walk over a graph towards a target node for a given query and a source node is an important problem in applications such as knowledge base completion (KBC). It can be f…
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
Bo Dai, Albert Shaw, Lihong Li +5
When function approximation is used, solving the Bellman optimality equation with stability guarantees has remained a major open problem in reinforcement learning for decades. The…
A Learning-to-Infer Method for Real-Time Power Grid Multi-Line Outage Identification
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. This is because the number of hyp…
Character-level Deep Conflation for Business Data Analytics
Zhe Gan, P. D. Singh, Ameet Joshi +4
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 databa…
Stochastic Variance Reduction Methods for Policy Evaluation
Simon S. Du, Jianshu Chen, Lihong Li +2
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. In this…
Unsupervised Sequence Classification using Sequential Output Statistics
Yu Liu, Jianshu Chen, Li Deng
We consider learning a sequence classifier without labeled data by using sequential output statistics. The problem is highly valuable since obtaining labels in training data is oft…
Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear
Zachary C. Lipton, Kamyar Azizzadenesheli, Abhishek Kumar +3
Many practical environments contain catastrophic states that an optimal agent would visit infrequently or never. Even on toy problems, Deep Reinforcement Learning (DRL) agents tend…
Unsupervised Learning of Predictors from Unpaired Input-Output Samples
Jianshu Chen, Po-Sen Huang, Xiaodong He +2
Unsupervised learning is the most challenging problem in machine learning and especially in deep learning. Among many scenarios, we study an unsupervised learning problem of high e…
Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads
Ji He, Mari Ostendorf, Xiaodong He +4
We introduce an online popularity prediction and tracking task as a benchmark task for reinforcement learning with a combinatorial, natural language action space. A specified numbe…
Deep Reinforcement Learning with a Natural Language Action Space
Ji He, Jianshu Chen, Xiaodong He +4
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 fou…