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/anishnarkar/experiments_deeplearning_models
https://github.com/ankurbanga/Language-Models
https://github.com/ankurbhatia24/MULTIMODAL-EMOTION-RECOGNITION
https://github.com/anthropics/evals
https://github.com/antoinecollas/transformer_neural_machine_translation
https://github.com/anton-bushuiev/ppiformer
https://github.com/anupamsingh610/bert_ner_stride
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/anuscode/nrms
https://github.com/appcoreopc/berty
https://github.com/arielsho/decomposition-table-reasoning
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/arkel23/PyTorch-Pretrained-ViT
https://github.com/asahi417/lm-question-generation
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/asahi417/lmppl
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/asahi417/relbert
https://github.com/asfathermou/human-computer-interaction
https://github.com/asgaardlab/test-case-similarity-technique
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/astrazeneca/kazu
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
https://github.com/asultan123/DL_LLC_Prefetching
https://github.com/athms/learning-from-brains
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/augustgw/early-exit-transformer
https://github.com/autobotasia/vibert
https://github.com/avichaychriqui/HeBERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/avishek-018/single_notebook_projects
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/aws-samples/amazon-sagemaker-bert-pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/awslabs/mlm-scoring
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/awslabs/sockeye
https://github.com/ays-dev/keras-transformer
https://github.com/babelscape/wikineural
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/bangoc123/transformer
https://github.com/baosongyang/WRD
https://github.com/baotramduong/Sentiment-Analysis-with-Deep-Learning-using-BERT
https://github.com/baotramduong/Twitter-Sentiment-Analysis-with-Deep-Learning-using-BERT
https://github.com/bartzi/chainer-transformer
https://github.com/basile-bron/Artificial-Intelligence-Lexical
https://github.com/bayer-science-for-a-better-life/data2text-bioleaflets
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.