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UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction ...
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The Utility and Interplay of Gazetteers and Entity Segmentation for Named Entity Recognition in English ...
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Situation-Based Multiparticipant Chat Summarization: a Concept, an Exploration-Annotation Tool and an Example Collection ...
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Don't Let Discourse Confine Your Model: Sequence Perturbations for Improved Event Language Models ...
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Boundary Detection with BERT for Span-level Emotion Cause Analysis ...
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TexSmart: A System for Enhanced Natural Language Understanding ...
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AND does not mean OR: Using Formal Languages to Study Language Models’ Representations ...
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Constructing Multi-Modal Dialogue Dataset by Replacing Text with Semantically Relevant Images ...
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Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with Search ...
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Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition ...
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Document-level Event Extraction via Parallel Prediction Networks ...
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VL-BERT+: Detecting Protected Groups in Hateful Multimodal Memes ...
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Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition ...
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Abstract:
Read paper: https://www.aclanthology.org/2021.acl-long.487 Abstract: Few-shot Named Entity Recognition (NER) exploits only a handful of annotations to iden- tify and classify named entity mentions. Pro- totypical network shows superior performance on few-shot NER. However, existing prototyp- ical methods fail to differentiate rich seman- tics in other-class words, which will aggravate overfitting under few shot scenario. To address the issue, we propose a novel model, Mining Undefined Classes from Other-class (MUCO), that can automatically induce different unde- fined classes from the other class to improve few-shot NER. With these extra-labeled unde- fined classes, our method will improve the dis- criminative ability of NER classifier and en- hance the understanding of predefined classes with stand-by semantic knowledge. Experi- mental results demonstrate that our model out- performs five state-of-the-art models in both 1- shot and 5-shots settings on four NER bench- marks. We will release the code upon ...
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URL: https://dx.doi.org/10.48448/xvx2-6258 https://underline.io/lecture/25935-learning-from-miscellaneous-other-class-words-for-few-shot-named-entity-recognition
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Best of Both Worlds: Making High Accuracy Non-incremental Transformer-based Disfluency Detection Incremental ...
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Attention-based Contextual Language Model Adaptation for Speech Recognition ...
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Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech ...
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ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning ...
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LUX (Linguistic aspects Under eXamination): Discourse Analysis for Automatic Fake News Classification ...
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