161 citations · 273 across the 16 of their papers we have counts for
17 papers
Optimizing Evaluation Metrics for Multi-Task Learning via the Alternating Direction Method of Multipliers
Ge-Yang Ke, Yan Pan, Jian Yin +1
Multi-task learning (MTL) aims to improve the generalization performance of multiple tasks by exploiting the shared factors among them. Various metrics (e.g., F-score, Area Under t…
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…
GraphCodeBERT: Pre-training Code Representations with Data Flow
Daya Guo, Shuo Ren, Shuai Lu +15
Pre-trained models for programming language have achieved dramatic empirical improvements on a variety of code-related tasks such as code search, code completion, code summarizatio…
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…
Feature Pyramid Hashing
Yifan Yang, Libing Geng, Hanjiang Lai +2
In recent years, deep-networks-based hashing has become a leading approach for large-scale image retrieval. Most deep hashing approaches use the high layer to extract the powerful…
Deep Policy Hashing Network with Listwise Supervision
Shaoying Wang, Haijiang Lai, Yifan Yang +1
Deep-networks-based hashing has become a leading approach for large-scale image retrieval, which learns a similarity-preserving network to map similar images to nearby hash codes.…
Towards Multi-pose Guided Virtual Try-on Network
Haoye Dong, Xiaodan Liang, Bochao Wang +3
Virtual try-on system under arbitrary human poses has huge application potential, yet raises quite a lot of challenges, e.g. self-occlusions, heavy misalignment among diverse poses…
Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis
Haoye Dong, Xiaodan Liang, Ke Gong +3
Despite remarkable advances in image synthesis research, existing works often fail in manipulating images under the context of large geometric transformations. Synthesizing person…
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…
Improving Deep Binary Embedding Networks by Order-aware Reweighting of Triplets
Jikai Chen, Hanjiang Lai, Libing Geng +1
In this paper, we focus on triplet-based deep binary embedding networks for image retrieval task. The triplet loss has been shown to be most effective for the ranking problem. Howe…
Deep Recurrent Regression for Facial Landmark Detection
Hanjiang Lai, Shengtao Xiao, Yan Pan +5
We propose a novel end-to-end deep architecture for face landmark detection, based on a deep convolutional and deconvolutional network followed by carefully designed recurrent netw…