Papers with Code 아카이브
Coreference Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities.
Example:
+-----------+
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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/vanpersie32/Multigpu-Bert
https://github.com/vanslerry/long_text
https://github.com/varun-suresh/experiments-with-gpt2
https://github.com/varunchaudharycs/aNswER_multirc
https://github.com/varunchaudharycs/multi_rc
https://github.com/veekaybee/what_are_embeddings
https://github.com/venkyr91193/Sentiment-Analysis-with-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/venkyr91193/Sentiment_Analysis_with_BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/vered1986/self_talk
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
https://github.com/vergrig/RuBia-Dataset
Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods
https://github.com/vgaraujov/seq2seq-spanish-plms
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/vijay120/bert
https://github.com/vikashranjan/transformer
https://github.com/vilm-ai/viet-llm-eval
https://github.com/vinairesearch/phogpt
https://github.com/vinayprabhu/X-is-all-you-need
https://github.com/viniciusoliveirasd/bert-applications
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/vishnuanil122/Bert-Binary-Text-Classifier
https://github.com/vmware-labs/bert-pretraining
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/volcengine/vegiantmodel
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/vpj/jax_transformer
https://github.com/vulus98/rethinking-attention
https://github.com/vvvm23/TchAIkovsky-Legacy
https://github.com/walkacross/transformer-pytorch
https://github.com/wangcongcong123/ttt
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/wangmz15/Chinese-Error-Correction-with-THUMT
https://github.com/wayalhruhi/gogle_bert
https://github.com/wchh127/yykf
https://github.com/weidafeng/NLU2019
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/weixsong/bilm-tf
https://github.com/wenhuchen/HDSA-Dialog
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/whqwill/seq2seq-keyphrase-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/whr94621/NJUNMT-pytorch
https://github.com/why2000/DuReader-bert
https://github.com/wooseok-AI/Korean_e2e_CR_BERT
SpanBERT: Improving Pre-training by Representing and Predicting Spans
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.