Papers with Code 아카이브
Coreference Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/idiap/g2g-transformer
https://github.com/idiotprofessorchen/bert.github.io
https://github.com/iejMac/ScriptWriter
https://github.com/ikergarcia1996/Simultaneous-Machine-Translation
https://github.com/ilhamfp/indonesian-text-classification-multilingual
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/iliaschalkidis/ELMo-keras
https://github.com/imcaspar/gpt2-ml
https://github.com/imgarylai/bert-embedding
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/insait-institute/lm-evaluation-harness-bg
https://github.com/insigh/THUMT
https://github.com/ishritam/Image-captioning-with-visual-attention
https://github.com/itsShnik/adaptively-finetuning-transformers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/itspreeti25/Q-A-implementation-using-BERT-finetuned-on-multiple-datasets
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/itzprashu1/prashant
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/jadore801120/attention-is-all-you-need-pytorch
https://github.com/jageshmaharjan/BERT_Service
https://github.com/jaisenbe58r/NLP-Transformer_Translator
https://github.com/jamesdvance/predicting_clicks
https://github.com/jangjoongkeon/JK
https://github.com/jankrepl/mildlyoverfitted
https://github.com/jbdel/OMG_UMONS_submission
https://github.com/jean-kunz/ml_research_papers
https://github.com/jelifysh/Transformers
https://github.com/jeongchanyoung-1234/De-En-NMT
https://github.com/jgcbrouns/Introducing-Sparsity-in-the-Transformer
https://github.com/jiahuei/Self-Critical-SCST-TensorFlow
https://github.com/jinzhenfan/BERT
https://github.com/jiyuan/ainote
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/jmnolte/hccnet
https://github.com/joebluems/Soul_Of_AI
https://github.com/joeljang/rlphf
Exploring the Benefits of Training Expert Language Models over Instruction Tuning
https://github.com/joelniklaus/lawinstruct
https://github.com/johnr0/TaleBrush-backend
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/jongwooko/nash-pruning-official
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/joongbo/tta
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.