A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning

• URL: http://arxiv.org/abs/2210.09934v1
• Date: Tue, 18 Oct 2022 15:36:53 GMT
• Title: A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning
• Authors: Kunbo Ding, Weijie Liu, Yuejian Fang, Weiquan Mao, Zhe Zhao, Tao Zhu, Haoyan Liu, Rong Tian, Yiren Chen
• Abstract summary: Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries. We propose Embedding-Push, Attention-Pull, and Robust targets to transfer English embeddings to virtual multilingual embeddings without semantic loss.
• Score: 6.329304732560936
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 explored training multilingual models on English-only resources and transferring them to low-resource languages. However, its effect is limited by the gap between embedding clusters of different languages. To address this issue, we propose Embedding-Push, Attention-Pull, and Robust targets to transfer English embeddings to virtual multilingual embeddings without semantic loss, thereby improving cross-lingual transferability. Experimental results on mBERT and XLM-R demonstrate that our method significantly outperforms previous works on the zero-shot cross-lingual text classification task and can obtain a better multilingual alignment.

Related papers

• Self-Augmentation Improves Zero-Shot Cross-Lingual Transfer [92.80671770992572]
  Cross-lingual transfer is a central task in multilingual NLP. Earlier efforts on this task use parallel corpora, bilingual dictionaries, or other annotated alignment data. We propose a simple yet effective method, SALT, to improve the zero-shot cross-lingual transfer.
  arXiv  Detail & Related papers  (2023-09-19T19:30:56Z)
• Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsing [68.47787275021567]
  Cross-lingual semantic parsing transfers parsing capability from a high-resource language (e.g., English) to low-resource languages with scarce training data. We propose a new approach to cross-lingual semantic parsing by explicitly minimizing cross-lingual divergence between latent variables using Optimal Transport.
  arXiv  Detail & Related papers  (2023-07-09T04:52:31Z)
• Cross-Lingual Transfer Learning for Phrase Break Prediction with Multilingual Language Model [13.730152819942445]
  Cross-lingual transfer learning can be particularly effective for improving performance in low-resource languages. This suggests that cross-lingual transfer can be inexpensive and effective for developing TTS front-end in resource-poor languages.
  arXiv  Detail & Related papers  (2023-06-05T04:10:04Z)
• DiTTO: A Feature Representation Imitation Approach for Improving Cross-Lingual Transfer [15.062937537799005]
  languages as domains for improving zero-shot transfer. We show that our approach, DiTTO, significantly outperforms the standard zero-shot fine-tuning method. Our model enables better cross-lingual transfer than standard fine-tuning methods, even in the few-shot setting.
  arXiv  Detail & Related papers  (2023-03-04T08:42:50Z)
• Cross-lingual Transferring of Pre-trained Contextualized Language Models [73.97131976850424]
  We propose a novel cross-lingual model transferring framework for PrLMs: TreLM. To handle the symbol order and sequence length differences between languages, we propose an intermediate TRILayer" structure. We show the proposed framework significantly outperforms language models trained from scratch with limited data in both performance and efficiency.
  arXiv  Detail & Related papers  (2021-07-27T06:51:13Z)
• Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training [45.48003947488825]
  We study two widely used robust training methods: adversarial training and randomized smoothing. The experimental results demonstrate that robust training can improve zero-shot cross-lingual transfer for text classification.
  arXiv  Detail & Related papers  (2021-04-17T21:21:53Z)
• Multilingual Transfer Learning for QA Using Translation as Data Augmentation [13.434957024596898]
  We explore strategies that improve cross-lingual transfer by bringing the multilingual embeddings closer in the semantic space. We propose two novel strategies, language adversarial training and language arbitration framework, which significantly improve the (zero-resource) cross-lingual transfer performance. Empirically, we show that the proposed models outperform the previous zero-shot baseline on the recently introduced multilingual MLQA and TyDiQA datasets.
  arXiv  Detail & Related papers  (2020-12-10T20:29:34Z)
• Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation [105.41167108465085]
  Cross-lingual Machine Reading (CLMRC) remains a challenging problem due to the lack of large-scale datasets in low-source languages. We propose a novel augmentation approach named Language Branch Machine Reading (LBMRC) LBMRC trains multiple machine reading comprehension (MRC) models proficient in individual language. We devise a multilingual distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages.
  arXiv  Detail & Related papers  (2020-10-27T13:12:17Z)
• From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers [62.637055980148816]
  Massively multilingual transformers pretrained with language modeling objectives have become a de facto default transfer paradigm for NLP. We show that cross-lingual transfer via massively multilingual transformers is substantially less effective in resource-lean scenarios and for distant languages.
  arXiv  Detail & Related papers  (2020-05-01T22:04:58Z)
• Robust Cross-lingual Embeddings from Parallel Sentences [65.85468628136927]
  We propose a bilingual extension of the CBOW method which leverages sentence-aligned corpora to obtain robust cross-lingual word representations. Our approach significantly improves crosslingual sentence retrieval performance over all other approaches. It also achieves parity with a deep RNN method on a zero-shot cross-lingual document classification task.
  arXiv  Detail & Related papers  (2019-12-28T16:18:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.