110 citations · 242 across the 22 of their papers we have counts for
18 papers · 1 filter
Prompt2Gaussia: Uncertain Prompt-learning for Script Event Prediction
Shiyao Cui, Xin Cong, Jiawei Sheng +3
Script Event Prediction (SEP) aims to predict the subsequent event for a given event chain from a candidate list. Prior research has achieved great success by integrating external…
Universal Information Extraction with Meta-Pretrained Self-Retrieval
Xin Cong. Bowen Yu, Mengcheng Fang, Tingwen Liu +5
Universal Information Extraction~(Universal IE) aims to solve different extraction tasks in a uniform text-to-structure generation manner. Such a generation procedure tends to stru…
Event Causality Extraction with Event Argument Correlations
Shiyao Cui, Jiawei Sheng, Xin Cong +3
Event Causality Identification (ECI), which aims to detect whether a causality relation exists between two given textual events, is an important task for event causality understand…
Towards Generalized Open Information Extraction
Bowen Yu, Zhenyu Zhang, Jingyang Li +5
Open Information Extraction (OpenIE) facilitates the open-domain discovery of textual facts. However, the prevailing solutions evaluate OpenIE models on in-domain test sets aside f…
Layout-Aware Information Extraction for Document-Grounded Dialogue: Dataset, Method and Demonstration
Zhenyu Zhang, Bowen Yu, Haiyang Yu +6
Building document-grounded dialogue systems have received growing interest as documents convey a wealth of human knowledge and commonly exist in enterprises. Wherein, how to compre…
Document-Level Event Extraction via Human-Like Reading Process
Shiyao Cui, Xin Cong, Bowen Yu +3
Document-level Event Extraction (DEE) is particularly tricky due to the two challenges it poses: scattering-arguments and multi-events. The first challenge means that arguments of…
Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising Learning
Xinghua Zhang, Bowen Yu, Tingwen Liu +4
Distantly supervised named entity recognition (DS-NER) efficiently reduces labor costs but meanwhile intrinsically suffers from the label noise due to the strong assumption of dist…
CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction
Jiawei Sheng, Shu Guo, Bowen Yu +5
Event extraction (EE) is a crucial information extraction task that aims to extract event information in texts. Most existing methods assume that events appear in sentences without…
Discontinuous Named Entity Recognition as Maximal Clique Discovery
Yucheng Wang, Bowen Yu, Hongsong Zhu +3
Named entity recognition (NER) remains challenging when entity mentions can be discontinuous. Existing methods break the recognition process into several sequential steps. In train…
Label Enhanced Event Detection with Heterogeneous Graph Attention Networks
Shiyao Cui, Bowen Yu, Xin Cong +3
Event Detection (ED) aims to recognize instances of specified types of event triggers in text. Different from English ED, Chinese ED suffers from the problem of word-trigger mismat…
Few-Shot Event Detection with Prototypical Amortized Conditional Random Field
Xin Cong, Shiyao Cui, Bowen Yu +3
Event detection tends to struggle when it needs to recognize novel event types with a few samples. The previous work attempts to solve this problem in the identify-then-classify ma…
TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking
Yucheng Wang, Bowen Yu, Yueyang Zhang +3
Extracting entities and relations from unstructured text has attracted increasing attention in recent years but remains challenging, due to the intrinsic difficulty in identifying…
Adaptive Attentional Network for Few-Shot Knowledge Graph Completion
Jiawei Sheng, Shu Guo, Zhenyu Chen +4
Few-shot Knowledge Graph (KG) completion is a focus of current research, where each task aims at querying unseen facts of a relation given its few-shot reference entity pairs. Rece…
Coarse-to-Fine Pre-training for Named Entity Recognition
Mengge Xue, Bowen Yu, Zhenyu Zhang +3
More recently, Named Entity Recognition hasachieved great advances aided by pre-trainingapproaches such as BERT. However, currentpre-training techniques focus on building lan-guage…
Inductive Unsupervised Domain Adaptation for Few-Shot Classification via Clustering
Xin Cong, Bowen Yu, Tingwen Liu +3
Few-shot classification tends to struggle when it needs to adapt to diverse domains. Due to the non-overlapping label space between domains, the performance of conventional domain…
Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation
Shiyao Cui, Bowen Yu, Tingwen Liu +3
Event detection (ED), a key subtask of information extraction, aims to recognize instances of specific event types in text. Previous studies on the task have verified the effective…
Enhancing Pre-trained Chinese Character Representation with Word-aligned Attention
Yanzeng Li, Bowen Yu, Mengge Xue +1
Most Chinese pre-trained models take character as the basic unit and learn representation according to character's external contexts, ignoring the semantics expressed in the word,…
Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy
Bowen Yu, Zhenyu Zhang, Xiaobo Shu +4
Joint extraction of entities and relations aims to detect entity pairs along with their relations using a single model. Prior work typically solves this task in the extract-then-cl…