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/Mind23-2/MindCode-138
https://github.com/Mind23-2/MindCode-144
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Mind23-2/MindCode-161
https://github.com/Mind23-2/MindCode-98
https://github.com/MindCode-4/code-13/tree/main/WinoGrande
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
https://github.com/MindCode-4/code-3/tree/main/deberta
https://github.com/MindCode-4/code-3/tree/main/deberta_v2
https://github.com/MindCode-4/code-5/tree/main/transformer
https://github.com/MindSpore-paper-code-3/code9/tree/main/soft_masked_bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/MindSpore-scientific-2/code-3/tree/main/WinoGrande
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
https://github.com/MindSpore-scientific/code-11/tree/main/Translation-Invariant/model
https://github.com/MindSpore-scientific/code-14/tree/main/WinoGrande
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
https://github.com/MindSpore-scientific/code-5/tree/main/WinoGrande
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
https://github.com/MindSpore-scientific/code-7/tree/main/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/MindSpore-scientific/code-8/tree/main/Translation-Invariant/model
https://github.com/MirunaPislar/multi-head-attention-labeller
https://github.com/Misoknisky/Bert-MultiGpu
https://github.com/MohamedAbdelsalam9/TT-Transformer
https://github.com/NVIDIA/NeMo-Curator
https://github.com/NVIDIA/sentiment-discovery
https://github.com/NehaTamore/TextSimilarity
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Neoanarika/torchexplainer
https://github.com/Nilanshrajput/Intent_classification
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Nimesh-Patel/BERT
https://github.com/Nimesh-Patel/text-to-text-transfer-transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/Nstats/bert_MRC
https://github.com/Nstats/bert_senti_analysis_ch
https://github.com/Nstats/my_bert
https://github.com/OPAYA/Transformer
https://github.com/Osobarako/alduswarrensewell
https://github.com/OweysMomenzada/Evergreen-Content-Classifier-for-german-Text
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/PaddlePaddle/PaddleNLP
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/gpt
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/gpt-3
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/t5
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.