559 citations · 2.2k across the 49 of their papers we have counts for
12 papers · 1 filter
Syntax-Enhanced Pre-trained Model
Zenan Xu, Daya Guo, Duyu Tang +7
We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre…
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…
CodeBLEU: a Method for Automatic Evaluation of Code Synthesis
Shuo Ren, Daya Guo, Shuai Lu +7
Evaluation metrics play a vital role in the growth of an area as it defines the standard of distinguishing between good and bad models. In the area of code synthesis, the commonly…
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…
Pre-training Text Representations as Meta Learning
Shangwen Lv, Yuechen Wang, Daya Guo +10
Pre-training text representations has recently been shown to significantly improve the state-of-the-art in many natural language processing tasks. The central goal of pre-training…
Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection
Ruize Wang, Duyu Tang, Nan Duan +5
We study the detection of propagandistic text fragments in news articles. Instead of merely learning from input-output datapoints in training data, we introduce an approach to inje…
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
Zhangyin Feng, Daya Guo, Duyu Tang +8
We present CodeBERT, a bimodal pre-trained model for programming language (PL) and nat-ural language (NL). CodeBERT learns general-purpose representations that support downstream N…
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
Ruize Wang, Duyu Tang, Nan Duan +6
We study the problem of injecting knowledge into large pre-trained models like BERT and RoBERTa. Existing methods typically update the original parameters of pre-trained models whe…