Cross-Lingual Transfer
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Cross-lingual transfer refers to transfer learning using data and models available for one language for which ample such resources are available (e.g., English) to solve tasks in another, commonly more low-resource, language.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ethahtz/multilingual_othello
공식 코드https://github.com/ethanachi/multilingual-probing-visualization
공식 코드Finding Universal Grammatical Relations in Multilingual BERT
https://github.com/facebookresearch/LASER
공식 코드Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
https://github.com/facebookresearch/XLM
공식 코드https://github.com/facebookresearch/belebele
공식 코드The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants
https://github.com/facebookresearch/cc_net
공식 코드https://github.com/facebookresearch/contriever
공식 코드Unsupervised Dense Information Retrieval with Contrastive Learning
https://github.com/fdschmidt93/free-lunch-xlt
공식 코드Free Lunch: Robust Cross-Lingual Transfer via Model Checkpoint Averaging
https://github.com/fdschmidt93/ofa-xlt
공식 코드https://github.com/fe1ixxu/ZeroShot-CrossLing-Parsing
공식 코드Zero-Shot Cross-Lingual Dependency Parsing through Contextual Embedding Transformation
https://github.com/fe1ixxu/cd_id_mmt
공식 코드Language-Aware Multilingual Machine Translation with Self-Supervised Learning
https://github.com/fe1ixxu/intra-distillation
공식 코드The Importance of Being Parameters: An Intra-Distillation Method for Serious Gains
https://github.com/fe1ixxu/mixed-gradient-few-shot
공식 코드https://github.com/felipessalvatore/CLCD
공식 코드https://github.com/ffaisal93/adapt_lang_phylogeny
공식 코드Phylogeny-Inspired Adaptation of Multilingual Models to New Languages
https://github.com/ffaisal93/neg_inf
공식 코드An Efficient Approach for Studying Cross-Lingual Transfer in Multilingual Language Models
https://github.com/fffffarmer/lsar
공식 코드Discovering Low-rank Subspaces for Language-agnostic Multilingual Representations
https://github.com/firatcekinel/fctr
공식 코드Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkish
https://github.com/for-ai/goodtriever
공식 코드From One to Many: Expanding the Scope of Toxicity Mitigation in Language Models
https://github.com/frankaging/oolong-crosslingual
공식 코드Oolong: Investigating What Makes Transfer Learning Hard with Controlled Studies
https://github.com/fredxlpy/cross_lingual_prompt_tuning
공식 코드Soft Prompt Tuning for Cross-Lingual Transfer: When Less is More
https://github.com/fredxlpy/crosslingualspaceimpactanalysis
공식 코드https://github.com/gabriel819/zeroshot_ner
공식 코드Zero-Shot Cross-Lingual NER Using Phonemic Representations for Low-Resource Languages
https://github.com/ghchen18/acl22-sixtp
공식 코드Towards Making the Most of Multilingual Pretraining for Zero-Shot Neural Machine Translation
https://github.com/ghchen18/emnlp2021-sixt
공식 코드Zero-shot Cross-lingual Transfer of Neural Machine Translation with Multilingual Pretrained Encoders
https://github.com/google-research/bleurt
공식 코드https://github.com/google-research/byt5
공식 코드ByT5: Towards a token-free future with pre-trained byte-to-byte models
https://github.com/google-research/multilingual-t5
공식 코드mT5: A massively multilingual pre-trained text-to-text transformer
https://github.com/google-research/prompt-tuning
공식 코드Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generation
https://github.com/google-research/xtreme
공식 코드XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization
https://github.com/gucci-j/lowres-cva
공식 코드How Can We Effectively Expand the Vocabulary of LLMs with 0.01GB of Target Language Text?
https://github.com/gucci-j/lowres-cve
공식 코드How Can We Effectively Expand the Vocabulary of LLMs with 0.01GB of Target Language Text?
https://github.com/hansd410/cross-lingfnd
공식 코드Cross-lingual Transfer Learning for Fake News Detector in a Low-Resource Language
https://github.com/hanzhou032/xqa-dst
공식 코드XQA-DST: Multi-Domain and Multi-Lingual Dialogue State Tracking
https://github.com/hassanmojab/metanli
공식 코드Persian Natural Language Inference: A Meta-learning approach
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.