161 citations · 273 across the 16 of their papers we have counts for
9 papers · 1 filter
AR-LSAT: Investigating Analytical Reasoning of Text
Wanjun Zhong, Siyuan Wang, Duyu Tang +6
Analytical reasoning is an essential and challenging task that requires a system to analyze a scenario involving a set of particular circumstances and perform reasoning over it to…
Neural Deepfake Detection with Factual Structure of Text
Wanjun Zhong, Duyu Tang, Zenan Xu +5
Deepfake detection, the task of automatically discriminating machine-generated text, is increasingly critical with recent advances in natural language generative models. Existing a…
Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder
Daya Guo, Duyu Tang, Nan Duan +3
Generating inferential texts about an event in different perspectives requires reasoning over different contexts that the event occurs. Existing works usually ignore the context th…
LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network
Wanjun Zhong, Duyu Tang, Zhangyin Feng +7
Verifying the correctness of a textual statement requires not only semantic reasoning about the meaning of words, but also symbolic reasoning about logical operations like count, s…
A Heterogeneous Graph with Factual, Temporal and Logical Knowledge for Question Answering Over Dynamic Contexts
Wanjun Zhong, Duyu Tang, Nan Duan +3
We study question answering over a dynamic textual environment. Although neural network models achieve impressive accuracy via learning from input-output examples, they rarely leve…
Inferential Text Generation with Multiple Knowledge Sources and Meta-Learning
Daya Guo, Akari Asai, Duyu Tang +6
We study the problem of generating inferential texts of events for a variety of commonsense like \textit{if-else} relations. Existing approaches typically use limited evidence from…
Reasoning Over Semantic-Level Graph for Fact Checking
Wanjun Zhong, Jingjing Xu, Duyu Tang +5
Fact checking is a challenging task because verifying the truthfulness of a claim requires reasoning about multiple retrievable evidence. In this work, we present a method suitable…
Coupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing
Daya Guo, Duyu Tang, Nan Duan +2
In this paper, we present an approach to incorporate retrieved datapoints as supporting evidence for context-dependent semantic parsing, such as generating source code conditioned…
Question Generation from SQL Queries Improves Neural Semantic Parsing
Daya Guo, Yibo Sun, Duyu Tang +6
We study how to learn a semantic parser of state-of-the-art accuracy with less supervised training data. We conduct our study on WikiSQL, the largest hand-annotated semantic parsin…