https://github.com/umanlp/XWEAT
공식 코드Are We Consistently Biased? Multidimensional Analysis of Biases in Distributional Word Vectors
Papers with Code 아카이브
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/umanlp/multi2woz
공식 코드Multi2WOZ: A Robust Multilingual Dataset and Conversational Pretraining for Task-Oriented Dialog
https://github.com/unhsaillab/taco
공식 코드https://github.com/unicamp-dl/cross-lingual-analysis
공식 코드https://github.com/unza-speech-lab/zambezi-voice
공식 코드Zambezi Voice: A Multilingual Speech Corpus for Zambian Languages
https://github.com/vaidehi99/obpe
공식 코드Overlap-based Vocabulary Generation Improves Cross-lingual Transfer Among Related Languages
https://github.com/veranep/crosslingualdetoxdebias
공식 코드https://github.com/villacu/ic_xlt
공식 코드Adaptive Cross-lingual Text Classification through In-Context One-Shot Demonstrations
https://github.com/vinairesearch/unibridge
공식 코드UniBridge: A Unified Approach to Cross-Lingual Transfer Learning for Low-Resource Languages
https://github.com/wangchunliu/drs-pretrained-lmm
공식 코드Pre-Trained Language-Meaning Models for Multilingual Parsing and Generation
https://github.com/wasiahmad/Syntax-MBERT
공식 코드Syntax-augmented Multilingual BERT for Cross-lingual Transfer
https://github.com/wasiahmad/cross_lingual_parsing
공식 코드Cross-lingual Dependency Parsing with Unlabeled Auxiliary Languages
https://github.com/willyfh/msvd-indonesian
공식 코드MSVD-Indonesian: A Benchmark for Multimodal Video-Text Tasks in Indonesian
https://github.com/wulinjuan/ssdm_mrc
공식 코드https://github.com/xenonmolecule/multisim
공식 코드Revisiting non-English Text Simplification: A Unified Multilingual Benchmark
https://github.com/xiyanghu/multilingualir
공식 코드Language Agnostic Multilingual Information Retrieval with Contrastive Learning
https://github.com/xrc10/unsup-cross-lingual-embedding-transfer
공식 코드Unsupervised Cross-lingual Transfer of Word Embedding Spaces
https://github.com/xzhao-tkl/fact-cl
공식 코드https://github.com/yandex-research/crosslingual_winograd
공식 코드https://github.com/yfqiu-nlp/mfact-summ
공식 코드Detecting and Mitigating Hallucinations in Multilingual Summarisation
https://github.com/yhy1117/x-mixup
공식 코드https://github.com/yunsukim86/sockeye-transfer
공식 코드Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared Vocabularies
https://github.com/zeinsh/Legal-Docs-Large-MLTC
공식 코드Zero-Shot Cross-Lingual Transfer in Legal Domain Using Transformer Models
https://github.com/zengyan-97/cclm
공식 코드Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-training
https://github.com/ziyi-yang/lir
공식 코드A Simple and Effective Method To Eliminate the Self Language Bias in Multilingual Representations
https://github.com/zurichnlp/multilingual-instruction-tuning
공식 코드Turning English-centric LLMs Into Polyglots: How Much Multilinguality Is Needed?
https://github.com/1-punchMan/CLTS
https://github.com/2023-MindSpore-1/ms-code-5/tree/main/luke
mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models
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/Adapter-Hub/adapter-transformers
MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer
https://github.com/AssafSinger94/sigmorphon-2020-inflection
https://github.com/Beacontownfc/rembert_paddle
Rethinking embedding coupling in pre-trained language models
https://github.com/BramVanroy/astred
Word Alignment by Fine-tuning Embeddings on Parallel Corpora
https://github.com/BunsenFeng/M-AbstainQA
The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants
https://github.com/D-Roberts/multilingual-nli-ECIR2021
https://github.com/DevSinghSachan/unsupervised-passage-reranking
Unsupervised Dense Information Retrieval with Contrastive Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.