4 citations · 11 across the 13 of their papers we have counts for
14 papers
"What do others think?": Task-Oriented Conversational Modeling with Subjective Knowledge
Chao Zhao, Spandana Gella, Seokhwan Kim +7
Task-oriented Dialogue (TOD) Systems aim to build dialogue systems that assist users in accomplishing specific goals, such as booking a hotel or a restaurant. Traditional TODs rely…
Flipbot: Learning Continuous Paper Flipping via Coarse-to-Fine Exteroceptive-Proprioceptive Exploration
Chao Zhao, Chunli Jiang, Junhao Cai +3
This paper tackles the task of singulating and grasping paper-like deformable objects. We refer to such tasks as paper-flipping. In contrast to manipulating deformable objects that…
Learn to Grasp via Intention Discovery and its Application to Challenging Clutter
Chao Zhao, Chunli Jiang, Junhao Cai +3
Humans excel in grasping objects through diverse and robust policies, many of which are so probabilistically rare that exploration-based learning methods hardly observe and learn.…
ERRA: An Embodied Representation and Reasoning Architecture for Long-horizon Language-conditioned Manipulation Tasks
Chao Zhao, Shuai Yuan, Chunli Jiang +4
This letter introduces ERRA, an embodied learning architecture that enables robots to jointly obtain three fundamental capabilities (reasoning, planning, and interaction) for solvi…
NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization
Chao Zhao, Faeze Brahman, Kaiqiang Song +3
Narrative summarization aims to produce a distilled version of a narrative to describe its most salient events and characters. Summarizing a narrative is challenging as it requires…
Revisiting Generative Commonsense Reasoning: A Pre-Ordering Approach
Chao Zhao, Faeze Brahman, Tenghao Huang +1
Pre-trained models (PTMs) have lead to great improvements in natural language generation (NLG). However, it is still unclear how much commonsense knowledge they possess. With the g…
Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension
Chao Zhao, Wenlin Yao, Dian Yu +3
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…
"Let Your Characters Tell Their Story": A Dataset for Character-Centric Narrative Understanding
Faeze Brahman, Meng Huang, Oyvind Tafjord +3
When reading a literary piece, readers often make inferences about various characters' roles, personalities, relationships, intents, actions, etc. While humans can readily draw upo…
Weakly-Supervised Opinion Summarization by Leveraging External Information
Chao Zhao, Snigdha Chaturvedi
Opinion summarization from online product reviews is a challenging task, which involves identifying opinions related to various aspects of the product being reviewed. While previou…
Classification of entities via their descriptive sentences
Chao Zhao, Min Zhao, Yi Guan
Hypernym identification of open-domain entities is crucial for taxonomy construction as well as many higher-level applications. Current methods suffer from either low precision or…
De-identification of medical records using conditional random fields and long short-term memory networks
Zhipeng Jiang, Chao Zhao, Bin He +2
The CEGS N-GRID 2016 Shared Task 1 in Clinical Natural Language Processing focuses on the de-identification of psychiatric evaluation records. This paper describes two participatin…
Constructing a Hierarchical User Interest Structure based on User Profiles
Chao Zhao, Min Zhao, Yi Guan
The interests of individual internet users fall into a hierarchical structure which is useful in regards to building personalized searches and recommendations. Most studies on this…
EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning
Chao Zhao, Jingchi Jiang, Yi Guan
Objective: Electronic medical records (EMRs) contain an amount of medical knowledge which can be used for clinical decision support (CDS). Our objective is a general system that ca…
Learning and inference in knowledge-based probabilistic model for medical diagnosis
Jingchi Jiang, Chao Zhao, Yi Guan +1
Based on a weighted knowledge graph to represent first-order knowledge and combining it with a probabilistic model, we propose a methodology for the creation of a medical knowledge…