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:
+-----------+
| |
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/pritam1322/Machine-Translation-with-Transformer
https://github.com/ps2811/Bert-Multi-Label-Text-Classification-GitVersion
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/pwc-1/Paper-8/tree/main/deberta
https://github.com/pwc-1/Paper-8/tree/main/deberta_v2
https://github.com/pwc-1/Paper-9/tree/main/3/WinoGrande
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
https://github.com/pwc-1/Paper-9/tree/main/4/Translation-Invariant/model
https://github.com/pwc-1/Paper-9/tree/main/4/WinoGrande
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
https://github.com/pytorch/text
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/qbxlvnf11/BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/qbxlvnf11/contextualized-embedding-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/qbxlvnf11/pretrained-language-model-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/qipengguo/p2_webnlg2020
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/qywu/Chinese-GPT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/radoslawkrolikowski/sentiment-analysis-pytorch
https://github.com/rajlm10/Chandler
https://github.com/ramanakshay/nanogpt
https://github.com/ramanakshay/transformer
https://github.com/rasbot/plot-bot
https://github.com/rasoolims/stanza
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
https://github.com/rasta-nitzsche/Bert-For-Algerian-Dialect-DariBERTo
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/re-search/DocProduct
https://github.com/reactive-systems/circuit-repair
https://github.com/reactive-systems/ml2
https://github.com/redevaaa/Transformer-for-EEG
https://github.com/reppy4620/Dialog
https://github.com/ricardordb/bert
https://github.com/richard-peng-xia/Chinese-Noisy-Text
https://github.com/richard88888/chinese-noisy-text
https://github.com/richinkabra/CoVe-BCN
https://github.com/rickyHong/Google-BERT-repl
https://github.com/ricsinaruto/NeuralChatbots-DataFiltering
https://github.com/ricsinaruto/dialog-eval
https://github.com/rishavbhurtel/sentiment-classifier
https://github.com/roberttwomey/machine-imagination-isea
https://github.com/roberttwomey/machine-imagination-workshop
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.