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/contextlab/abstract2paper
https://github.com/coronazap/bert_client
https://github.com/cosmoquester/seq2seq
https://github.com/cospplay/bert-master
https://github.com/coxy1989/tfmr
https://github.com/cpm0722/transformer_pytorch
https://github.com/crazydigger/Callibration-of-GPT
https://github.com/crherlihy/clinical_nli_artifacts
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
https://github.com/crx934080895/Bert-CRF_New2
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/cuber2460/bert
https://github.com/cybertronai/megatron-lm
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/cyberzhg/keras-pos-embd
https://github.com/czero69/acomoeye-NN
https://github.com/danilo-dessi/skg
https://github.com/danlim-wz/analysis_of_social_media_data_about_singapore
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/darr/nerbert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/darr/nlp_work
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/davidalbertonogueira/NLP-tutorials
https://github.com/davidbellamy/labrador
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/davidsvaughn/dts-tf
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/davisking/dlib-models
https://github.com/dawn0815/UniSA
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/dbmdz/historic-ner
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/dccuchile/CC6204
https://github.com/debnsuma/AmazonSageMaker101
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/debnsuma/Intro-Transformer-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/declare-lab/flan-alpaca
https://github.com/deepmind/haiku
https://github.com/deepmind/xquad
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/deepmipt/Slavic-BERT-NER
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/deepmipt/bert/tree/feat/multi_gpu
https://github.com/deepset-ai/FARM
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/demelin/nematode
https://github.com/deping-1/2019-false-news-detection-challenge
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/derronxu/sparsebert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.