Search Results for author:
Found 14 papers, 6 papers with code
Distantly-Supervised Named Entity Recognition with Uncertainty-aware Teacher Learning and Student-student Collaborative Learning
Distantly-Supervised Named Entity Recognition (DS-NER) effectively alleviates the burden of annotation, but meanwhile suffers from the label noise.
Multi-level Asymmetric Contrastive Learning for Medical Image Segmentation Pre-training
In this work, we propose a novel asymmetric contrastive learning framework named JCL for medical image segmentation with self-supervised pre-training.
Branches Mutual Promotion for End-to-End Weakly Supervised Semantic Segmentation
Existing methods adopt an online-trained classification branch to provide pseudo annotations for supervising the segmentation branch.
Mining Clues from Incomplete Utterance: A Query-enhanced Network for Incomplete Utterance Rewriting
Then, we adopt a fast and effective edit operation scoring network to model the relation between two tokens.
SANTA: Separate Strategies for Inaccurate and Incomplete Annotation Noise in Distantly-Supervised Named Entity Recognition
Distantly-Supervised Named Entity Recognition effectively alleviates the burden of time-consuming and expensive annotation in the supervised setting.
SCL-RAI: Span-based Contrastive Learning with Retrieval Augmented Inference for Unlabeled Entity Problem in NER
Named Entity Recognition is the task to locate and classify the entities in the text.
Type-enriched Hierarchical Contrastive Strategy for Fine-Grained Entity Typing
On the other hand, we design a constrained contrastive strategy on the hierarchical structure to directly model the type differences, which can simultaneously perceive the distinguishability between types at different granularity.
A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction
In this paper, we focus on extracting event arguments from an entire document, which mainly faces two critical problems: a) the long-distance dependency between trigger and arguments over sentences; b) the distracting context towards an event in the document.
Document-level Event Extraction
Event Argument Extraction
+2
DISK: Domain-constrained Instance Sketch for Math Word Problem Generation
A math word problem (MWP) is a coherent narrative which reflects the underlying logic of math equations.
SIRE: Separate Intra- and Inter-sentential Reasoning for Document-level Relation Extraction
However, not all entity pairs can be connected with a path and have the correct logical reasoning paths in their graph.
Ranked #19 on
Relation Extraction
on DocRED
Generating Math Word Problems from Equations with Topic Controlling and Commonsense Enforcement
Recent years have seen significant advancement in text generation tasks with the help of neural language models.
Coarse-to-Fine Entity Representations for Document-level Relation Extraction
In classification, we combine the entity representations from both two levels into more comprehensive representations for relation extraction.
Ranked #34 on
Relation Extraction
on DocRED
Double Graph Based Reasoning for Document-level Relation Extraction
Document-level relation extraction aims to extract relations among entities within a document.
Ranked #12 on
Relation Extraction
on DocRED
Evaluating Text Coherence at Sentence and Paragraph Levels
In this paper, to evaluate text coherence, we propose the paragraph ordering task as well as conducting sentence ordering.
