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Coreference Resolution

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하위 태스크: 2
평가표: 16
보존된 평가 기록: 154

Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities.

Example:

               +-----------+
               |           |
I voted for Obama because he was most aligned with my values", she said.
 |                                                 |            |
 +-------------------------------------------------+------------+

"I", "my", and "she" belong to the same cluster and "Obama" and "he" belong to the same cluster.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/VachanVY/gpt.jax

Language Models are Few-Shot Learners

https://github.com/VedantYadav/Transformer

Attention Is All You Need

https://github.com/Vladimetr/ASR-Knowledge-Transferring

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/Walter-B/bert-20-classes

Attention Is All You Need

https://github.com/WenYanger/General-Transformer-Pytorch

Attention Is All You Need

https://github.com/WenjieDu/PyPOTS

Attention Is All You Need

https://github.com/Wolfie8935/Implementation-of-Attention-is-all-you-need

Attention Is All You Need

https://github.com/XINZXZHANG/Bert_repo

Attention Is All You Need

https://github.com/XUJitao/WordEmbedding

Attention Is All You Need

https://github.com/XapaJIaMnu/transformative

Attention Is All You Need

https://github.com/Xinsen-Zhang/transformer

Attention Is All You Need

https://github.com/XuhanLiu/Transformer

Attention Is All You Need

https://github.com/Y1ran/NLP-BERT--ChineseVersion

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/YC-wind/embedding_study

Deep contextualized word representations

https://github.com/YYGXjpg/BERT_WL

Attention Is All You Need

https://github.com/YerevaNN/SciERC

Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction

https://github.com/Yipeng91/text_classifier_pub

Attention Is All You Need

https://github.com/YongWookHa/BERT-on-Pytorch-Template

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/YuanEric88/bert-nlu

Attention Is All You Need

https://github.com/YuehChuan/bert-Agus

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/Zeeshan75/Bert_Telugu_Ner

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/Zehui127/SQUAD_BERT

Attention Is All You Need

https://github.com/ZmeiGorynych/transformer_pytorch

Attention Is All You Need

https://github.com/ZurichNLP/sockeye

Attention Is All You Need

https://github.com/a414351664/Bert_MRC

Attention Is All You Need

https://github.com/aananda-giri/gpt2-nepali

https://github.com/aaronH45/sentimentAnalysis

Attention Is All You Need

https://github.com/aaronHuang0121/AI-CUP-2020

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/abelriboulot/onnxt5

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

https://github.com/abhaskumarsinha/Corpus2GPT

Language Models are Few-Shot Learners

https://github.com/abhaskumarsinha/Keras-implementation-of-Transformer-Architecture

Attention Is All You Need

https://github.com/abhaskumarsinha/Keras-implementation-of-Transformer-Architecture/blob/main/Transformer.py

Attention Is All You Need

https://github.com/abhaskumarsinha/MinimalGPT

Attention Is All You Need

https://github.com/abhiagg92/bert_sentiment

Attention Is All You Need

https://github.com/abhilash1910/BERTSimilarity

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/abhilashreddys/Fake-News-Article

Attention Is All You Need

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.