11 citations · 39 across the 14 of their papers we have counts for
14 papers
Localize, Retrieve and Fuse: A Generalized Framework for Free-Form Question Answering over Tables
Wenting Zhao, Ye Liu, Yao Wan +3
Question answering on tabular data (a.k.a TableQA), which aims at generating answers to questions grounded on a provided table, has gained significant attention recently. Prior wor…
Named Entity Recognition via Machine Reading Comprehension: A Multi-Task Learning Approach
Yibo Wang, Wenting Zhao, Yao Wan +2
Named Entity Recognition (NER) aims to extract and classify entity mentions in the text into pre-defined types (e.g., organization or person name). Recently, many works have been p…
Self-Calibrated Cross Attention Network for Few-Shot Segmentation
Qianxiong Xu, Wenting Zhao, Guosheng Lin +1
The key to the success of few-shot segmentation (FSS) lies in how to effectively utilize support samples. Most solutions compress support foreground (FG) features into prototypes,…
Click-Conversion Multi-Task Model with Position Bias Mitigation for Sponsored Search in eCommerce
Yibo Wang, Yanbing Xue, Bo Liu +4
Position bias, the phenomenon whereby users tend to focus on higher-ranked items of the search result list regardless of the actual relevance to queries, is prevailing in many rank…
Structure-Sensitive Graph Dictionary Embedding for Graph Classification
Guangbu Liu, Tong Zhang, Xudong Wang +3
Graph structure expression plays a vital role in distinguishing various graphs. In this work, we propose a Structure-Sensitive Graph Dictionary Embedding (SS-GDE) framework to tran…
Abductive Commonsense Reasoning Exploiting Mutually Exclusive Explanations
Wenting Zhao, Justin T. Chiu, Claire Cardie +1
Abductive reasoning aims to find plausible explanations for an event. This style of reasoning is critical for commonsense tasks where there are often multiple plausible explanation…
HOP, UNION, GENERATE: Explainable Multi-hop Reasoning without Rationale Supervision
Wenting Zhao, Justin T. Chiu, Claire Cardie +1
Explainable multi-hop question answering (QA) not only predicts answers but also identifies rationales, i. e. subsets of input sentences used to derive the answers. This problem ha…
Serenity: Library Based Python Code Analysis for Code Completion and Automated Machine Learning
Wenting Zhao, Ibrahim Abdelaziz, Julian Dolby +3
Dynamically typed languages such as Python have become very popular. Among other strengths, Python's dynamic nature and its straightforward linking to native code have made it the…
Compositional Task-Oriented Parsing as Abstractive Question Answering
Wenting Zhao, Konstantine Arkoudas, Weiqi Sun +1
Task-oriented parsing (TOP) aims to convert natural language into machine-readable representations of specific tasks, such as setting an alarm. A popular approach to TOP is to appl…
Automating Crystal-Structure Phase Mapping: Combining Deep Learning with Constraint Reasoning
Di Chen, Yiwei Bai, Sebastian Ament +7
Crystal-structure phase mapping is a core, long-standing challenge in materials science that requires identifying crystal structures, or mixtures thereof, in synthesized materials.…
HOT-VAE: Learning High-Order Label Correlation for Multi-Label Classification via Attention-Based Variational Autoencoders
Wenting Zhao, Shufeng Kong, Junwen Bai +2
Understanding how environmental characteristics affect bio-diversity patterns, from individual species to communities of species, is critical for mitigating effects of global chang…
Evaluating Multi-label Classifiers with Noisy Labels
Wenting Zhao, Carla Gomes
Multi-label classification (MLC) is a generalization of standard classification where multiple labels may be assigned to a given sample. In the real world, it is more common to dea…
Zero Training Overhead Portfolios for Learning to Solve Combinatorial Problems
Yiwei Bai, Wenting Zhao, Carla P. Gomes
There has been an increasing interest in harnessing deep learning to tackle combinatorial optimization (CO) problems in recent years. Typical CO deep learning approaches leverage t…
Deep Reasoning Networks: Thinking Fast and Slow
Di Chen, Yiwei Bai, Wenting Zhao +3
We introduce Deep Reasoning Networks (DRNets), an end-to-end framework that combines deep learning with reasoning for solving complex tasks, typically in an unsupervised or weakly-…