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/caoyujiALgLM/NLP
https://github.com/caoyujiALgLM/NLP-progress
https://github.com/carrliitos/NLPInformationExtraction
https://github.com/cccntu/ft5-demo
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/cccntu/ft5-demo-space
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/cedrickchee/pytorch-pretrained-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/chalothon/BERT_Practice
https://github.com/chambliss/Multilingual_NER
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/chandu7077/mybert
https://github.com/charles9n/bert-sklearn
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/chen-xiong-yi/OwnBERT
https://github.com/cheng18/bilm-tf
https://github.com/chengchingwen/transformers.jl
https://github.com/chiayewken/bert-qa
https://github.com/chin-gyou/dialogue-utterance-rewriter
Improving Multi-turn Dialogue Modelling with Utterance ReWriter
https://github.com/chrisleunglokhin/Capstone-BERT
https://github.com/chrisociepa/allamo
https://github.com/christianspybrook/article_recommender
https://github.com/chujiezheng/ChID-Dataset
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/chunghyunhee/twitter_disaster_NLP
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/chunhuililili/mt_dnn
https://github.com/circlePi/BERT_Chinese_Text_Class_By_pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/clear-nus/llm-human-model
https://github.com/cloudmcloudyo/capstone
https://github.com/cmsflash/efficient-attention
https://github.com/cmunnis/BERT_vs_Transformer-XL
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/coastalcph/zeroshot_lexglue
https://github.com/coco60/bert-test
https://github.com/code-implementation1/Code7/tree/main/SDNet
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
https://github.com/codedotal/gpt-code-clippy
https://github.com/codertimo/BERT-pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/colorfulscoop/tfdlg
https://github.com/common-english/bert-all
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/conceptofmind/LaMDA-pytorch
https://github.com/conceptofmind/PaLM-flax
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.