Zero-shot Cross-lingual Transfer without Parallel Corpus
Recently, although pre-trained language models have achieved great success on multilingual NLP (Natural Language Processing) tasks, the lack of training data on many tasks in low-resource languages still limits their performance. One effective way of solving that problem is to transfer knowledge from rich-resource languages to low-resource languages. However, many previous works on cross-lingual transfer rely heavily on the parallel corpus or translation models, which are often difficult to obtain. We propose a novel approach to conduct zero-shot cross-lingual transfer with a pre-trained model. It consists of a Bilingual Task Fitting module that applies task-related bilingual information alignment; a self-training module generates pseudo soft and hard labels for unlabeled data and utilizes them to conduct self-training. We got the new SOTA on different tasks without any dependencies on the parallel corpus or translation models.
Code (0)
등록된 구현이 없습니다.
Tasks
Cross-Lingual TransferMultilingual NLPTranslationZero-Shot Cross-Lingual TransferSimilar Papers 제목 키워드 기반
Realistic Zero-Shot Cross-Lingual Transfer in Legal Topic Classification
We consider zero-shot cross-lingual transfer in legal topic classification using the recent MultiEURLEX dataset. Since the original dataset contains parallel documents, which is unrealistic for zero-shot cross-lingual tr…
Cross-Lingual TransferTopic ClassificationTranslationZero-Shot Cross-Lingual TransferSelf-Augmentation Improves Zero-Shot Cross-Lingual Transfer
Zero-shot cross-lingual transfer is a central task in multilingual NLP, allowing models trained in languages with more sufficient training resources to generalize to other low-resource languages. Earlier efforts on this …
Cross-Lingual TransferMultilingual NLPZero-Shot Cross-Lingual TransferRealistic Zero-Shot Cross-Lingual Transfer in Legal Topic Classification
We consider zero-shot cross-lingual transfer in legal topic classification using the recent Multi-EURLEX dataset. Since the original dataset contains parallel documents, which is unrealistic for zero-shot cross-lingual t…
ClassificationCross-Lingual TransferTopic ClassificationTranslation+1A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning
Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries, which are expensive and impractical for low-resource languages. To disengage from these dependencies, researchers have…
Cross-Lingual Transfertext-classificationText ClassificationTransfer Learning+2Zero-Shot Cross-Lingual Transfer is a Hard Baseline to Beat in German Fine-Grained Entity Typing
The training of NLP models often requires large amounts of labelled training data, which makes it difficult to expand existing models to new languages. While zero-shot cross-lingual transfer relies on multilingual word e…
Cross-Lingual TransferEntity TypingMultilingual Word Embeddingsnamed-entity-recognition+4