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Found 12 papers, 5 papers with code
DocRED-FE: A Document-Level Fine-Grained Entity And Relation Extraction Dataset
Joint entity and relation extraction (JERE) is one of the most important tasks in information extraction.
Joint Entity and Relation Extraction
Relation Classification
ConFiguRe: Exploring Discourse-level Chinese Figures of Speech
Different from previous token-level or sentence-level counterparts, ConFiguRe aims at extracting a figurative unit from discourse-level context, and classifying the figurative unit into the right figure type.
Task-Adaptive Pre-Training for Boosting Learning With Noisy Labels: A Study on Text Classification for African Languages
However, text classification in low-resource languages is still challenging due to the lack of annotated data.
Meta Self-Refinement for Robust Learning with Weak Supervision
However, labels from weak supervision can be rather noisy and the high capacity of DNNs makes them easy to overfit the noisy labels.
Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in Text Classification
Incorrect labels in training data occur when human annotators make mistakes or when the data is generated via weak or distant supervision.
GraphPrompt: Biomedical Entity Normalization Using Graph-based Prompt Templates
Biomedical entity normalization unifies the language across biomedical experiments and studies, and further enables us to obtain a holistic view of life sciences.
Neural Data-to-Text Generation with LM-based Text Augmentation
Our approach automatically augments the data available for training by (i) generating new text samples based on replacing specific values by alternative ones from the same category, (ii) generating new text samples based on GPT-2, and (iii) proposing an automatic method for pairing the new text samples with data samples.
Analysing the Noise Model Error for Realistic Noisy Label Data
Distant and weak supervision allow to obtain large amounts of labeled training data quickly and cheaply, but these automatic annotations tend to contain a high amount of errors.
Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages
Multilingual transformer models like mBERT and XLM-RoBERTa have obtained great improvements for many NLP tasks on a variety of languages.
Distant Supervision and Noisy Label Learning for Low Resource Named Entity Recognition: A Study on Hausa and Yorùbá
Techniques such as distant and weak supervision can be used to create labeled data in a (semi-) automatic way.
Low Resource Named Entity Recognition
named-entity-recognition
+3
An End-to-End Dialogue State Tracking System with Machine Reading Comprehension and Wide & Deep Classification
This paper describes our approach in DSTC 8 Track 4: Schema-Guided Dialogue State Tracking.
Image Manipulation with Natural Language using Two-sidedAttentive Conditional Generative Adversarial Network
We propose the Two-sidEd Attentive conditional Generative Adversarial Network (TEA-cGAN) to generate semantically manipulated images while preserving other contents such as background intact.
