70 citations · 209 across the 17 of their papers we have counts for
17 papers
NN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference
Benfeng Xu, Quan Wang, Zhendong Mao +3
In-Context Learning (ICL), which formulates target tasks as prompt completion conditioned on in-context demonstrations, has become the prevailing utilization of LLMs. In this paper…
WeCheck: Strong Factual Consistency Checker via Weakly Supervised Learning
Wenhao Wu, Wei Li, Xinyan Xiao +3
A crucial issue of current text generation models is that they often uncontrollably generate factually inconsistent text with respective of their inputs. Limited by the lack of ann…
CLOP: Video-and-Language Pre-Training with Knowledge Regularizations
Guohao Li, Hu Yang, Feng He +4
Video-and-language pre-training has shown promising results for learning generalizable representations. Most existing approaches usually model video and text in an implicit manner,…
UPainting: Unified Text-to-Image Diffusion Generation with Cross-modal Guidance
Wei Li, Xue Xu, Xinyan Xiao +8
Diffusion generative models have recently greatly improved the power of text-conditioned image generation. Existing image generation models mainly include text conditional diffusio…
Precisely the Point: Adversarial Augmentations for Faithful and Informative Text Generation
Wenhao Wu, Wei Li, Jiachen Liu +3
Though model robustness has been extensively studied in language understanding, the robustness of Seq2Seq generation remains understudied. In this paper, we conduct the first quant…
HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning
Zixuan Li, Zhongni Hou, Saiping Guan +7
A Temporal Knowledge Graph (TKG) is a sequence of KGs with respective timestamps, which adopts quadruples in the form of (\emph{subject}, \emph{relation}, \emph{object}, \emph{time…
Neural Knowledge Bank for Pretrained Transformers
Damai Dai, Wenbin Jiang, Qingxiu Dong +3
The ability of pretrained Transformers to remember factual knowledge is essential but still limited for existing models. Inspired by existing work that regards Feed-Forward Network…
Mixture of Experts for Biomedical Question Answering
Damai Dai, Wenbin Jiang, Jiyuan Zhang +5
Biomedical Question Answering (BQA) has attracted increasing attention in recent years due to its promising application prospect. It is a challenging task because the biomedical qu…
Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning
Zixuan Li, Saiping Guan, Xiaolong Jin +7
A Temporal Knowledge Graph (TKG) is a sequence of KGs corresponding to different timestamps. TKG reasoning aims to predict potential facts in the future given the historical KG seq…
Building Chinese Biomedical Language Models via Multi-Level Text Discrimination
Quan Wang, Songtai Dai, Benfeng Xu +4
Pre-trained language models (PLMs), such as BERT and GPT, have revolutionized the field of NLP, not only in the general domain but also in the biomedical domain. Most prior efforts…
Link Prediction on N-ary Relational Facts: A Graph-based Approach
Quan Wang, Haifeng Wang, Yajuan Lyu +1
Link prediction on knowledge graphs (KGs) is a key research topic. Previous work mainly focused on binary relations, paying less attention to higher-arity relations although they a…
Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction
Benfeng Xu, Quan Wang, Yajuan Lyu +2
Entities, as the essential elements in relation extraction tasks, exhibit certain structure. In this work, we formulate such structure as distinctive dependencies between mention p…
CoKE: Contextualized Knowledge Graph Embedding
Quan Wang, Pingping Huang, Haifeng Wang +6
Knowledge graph embedding, which projects symbolic entities and relations into continuous vector spaces, is gaining increasing attention. Previous methods allow a single static emb…
Adaptations of ROUGE and BLEU to Better Evaluate Machine Reading Comprehension Task
An Yang, Kai Liu, Jing Liu +2
Current evaluation metrics to question answering based machine reading comprehension (MRC) systems generally focus on the lexical overlap between the candidate and reference answer…
Joint Training of Candidate Extraction and Answer Selection for Reading Comprehension
Zhen Wang, Jiachen Liu, Xinyan Xiao +2
While sophisticated neural-based techniques have been developed in reading comprehension, most approaches model the answer in an independent manner, ignoring its relations with oth…
Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification
Yizhong Wang, Kai Liu, Jing Liu +5
Machine reading comprehension (MRC) on real web data usually requires the machine to answer a question by analyzing multiple passages retrieved by search engine. Compared with MRC…
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications
Wei He, Kai Liu, Jing Liu +10
This paper introduces DuReader, a new large-scale, open-domain Chinese ma- chine reading comprehension (MRC) dataset, designed to address real-world MRC. DuReader has three advanta…