https://github.com/DMIRLAB-Group/SSD
공식 코드Semi-Supervised Disentangled Framework for Transferable Named Entity Recognition
Papers with Code 아카이브
Cross-Lingual NER
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/YuweiYin/CROP
공식 코드https://github.com/alankarj/cross_lingual_ner
공식 코드Entity Projection via Machine Translation for Cross-Lingual NER
https://github.com/cross-lingual-ner/projection-data-transfer-cross-lingual-ner
공식 코드https://github.com/damo-nlp-sg/contproto
공식 코드https://github.com/ding-zj/glode
공식 코드https://github.com/edchengg/easyproject
공식 코드Frustratingly Easy Label Projection for Cross-lingual Transfer
https://github.com/gabriel819/zeroshot_ner
공식 코드Zero-Shot Cross-Lingual NER Using Phonemic Representations for Low-Resource Languages
https://github.com/google-research/byt5
공식 코드ByT5: Towards a token-free future with pre-trained byte-to-byte models
https://github.com/ikergarcia1996/Easy-Translate
공식 코드Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings
https://github.com/ikergarcia1996/annotation-projection-app
공식 코드Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings
https://github.com/ikergarcia1996/easy-label-projection
공식 코드Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings
https://github.com/ikergarcia1996/t-projection
공식 코드T-Projection: High Quality Annotation Projection for Sequence Labeling Tasks
https://github.com/mckysse/msd
공식 코드https://github.com/microsoft/Multilingual-Model-Transfer
공식 코드Multi-Source Cross-Lingual Model Transfer: Learning What to Share
https://github.com/microsoft/vert-papers/tree/master/papers/AdvPicker
공식 코드AdvPicker: Effectively Leveraging Unlabeled Data via Adversarial Discriminator for Cross-Lingual NER
https://github.com/microsoft/vert-papers/tree/master/papers/Meta-Cross
공식 코드Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources
https://github.com/microsoft/vert-papers/tree/master/papers/SingleMulti-TS
공식 코드https://github.com/microsoft/vert-papers/tree/master/papers/UniTrans
공식 코드https://github.com/ntunlp/Zero-Shot-Cross-Lingual-NER
공식 코드https://github.com/ntunlp/multisense_embedding_alignment
공식 코드Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings
https://github.com/randyzhouran/conner
공식 코드ConNER: Consistency Training for Cross-lingual Named Entity Recognition
https://github.com/randyzhouran/melm
공식 코드MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER
https://github.com/shijie-wu/crosslingual-nlp
공식 코드Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT
https://github.com/thespectrewithin/joint-align
공식 코드Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/byt5
ByT5: Towards a token-free future with pre-trained byte-to-byte models
https://github.com/Beacontownfc/rembert_paddle
Rethinking embedding coupling in pre-trained language models
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/rembert
Rethinking embedding coupling in pre-trained language models
https://github.com/UniversalNER/uner_code
Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark
https://github.com/edchengg/transfusion
Frustratingly Easy Label Projection for Cross-lingual Transfer
https://github.com/huggingface/transformers/tree/master/src/transformers/models/byt5
ByT5: Towards a token-free future with pre-trained byte-to-byte models
https://github.com/ikergarcia1996/Iker-Garcia-Ferrero
Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings
https://github.com/opennlg/openba-v2
Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark
https://github.com/stefan-it/fine-tuned-berts-seq
Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT
https://github.com/thespectrewithin/joint_align
Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework
https://github.com/ufal/multilexnorm2021
ByT5: Towards a token-free future with pre-trained byte-to-byte models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.