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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/haiyang-w/git

Language Models are Few-Shot Learners

https://github.com/halo090770/bert

Attention Is All You Need

https://github.com/han-shi/SparseBERT

Attention Is All You Need

https://github.com/hannibal046/nanorwkv

Attention Is All You Need

https://github.com/harenlin/IMDB-Sentiment-Analysis-Using-BERT-Fine-Tuning

Attention Is All You Need

https://github.com/harshit158/paper-dots

Attention Is All You Need

https://github.com/harvardnlp/annotated-transformer

Attention Is All You Need

https://github.com/hate-alert/indicabusive

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

https://github.com/haydlite/sparse-bert-ner

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

https://github.com/hazyresearch/safari

Hungry Hungry Hippos: Towards Language Modeling with State Space Models

https://github.com/hegebharat/SelfDiscovery

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

https://github.com/hegebharat/sentiment-Analysis-for-German-Datasets

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

https://github.com/helboukkouri/character-bert

Deep contextualized word representations

https://github.com/heraclex12/vietpunc

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

https://github.com/hfxunlp/transformer

Attention Is All You Need

https://github.com/hilberthit/gpt-3

Language Models are Few-Shot Learners

https://github.com/hiun/learning-transformers

Attention Is All You Need

https://github.com/hiyouga/llama-efficient-tuning

Finetuned Language Models Are Zero-Shot Learners

https://github.com/hoangtrungchinh/Bert-SQuAD-v2

Attention Is All You Need

https://github.com/hoichunlaw/COVID-BERT

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

https://github.com/hojjat-mokhtarabadi/promptsource

Language Models are Few-Shot Learners

https://github.com/horizonheart/ELMO

Deep contextualized word representations

https://github.com/hqyang/EasyECR

EasyECR: A Library for Easy Implementation and Evaluation of Event Coreference Resolution Models

https://github.com/hrbigelow/transformer-aiayn

Attention Is All You Need

https://github.com/huanghonggit/Mask-Language-Model

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

https://github.com/huberemanuel/DeBERTa

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

https://github.com/hubiodatalab/druggen

Attention Is All You Need

https://github.com/huggingface/swift-coreml-transformers

https://github.com/huggingface/transformers

Attention Is All You Need

https://github.com/hyunwookl/testam

Attention Is All You Need

https://github.com/hyunwoongko/transformer

Attention Is All You Need

https://github.com/ibm/graph_ensemble_learning

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

https://github.com/icewing1996/bert_dep

Attention Is All You Need

https://github.com/ictnlp/DiverseNMT

Attention Is All You Need

https://github.com/ictnlp/NA-MNMT

Attention Is All You Need

https://github.com/ictnlp/PTE-NMT

Attention Is All You Need

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