A Systematic Analysis of Subwords and Cross-Lingual Transfer in Multilingual Translation

• URL: http://arxiv.org/abs/2403.20157v1
• Date: Fri, 29 Mar 2024 13:09:23 GMT
• Title: A Systematic Analysis of Subwords and Cross-Lingual Transfer in Multilingual Translation
• Authors: Francois Meyer, Jan Buys,
• Abstract summary: Subword regularisation boosts synergy in multilingual modelling, whereas BPE more effectively facilitates transfer during cross-lingual fine-tuning. Our study confirms that decisions around subword modelling can be key to optimising the benefits of multilingual modelling.
• Score: 8.30255326875704
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multilingual modelling can improve machine translation for low-resource languages, partly through shared subword representations. This paper studies the role of subword segmentation in cross-lingual transfer. We systematically compare the efficacy of several subword methods in promoting synergy and preventing interference across different linguistic typologies. Our findings show that subword regularisation boosts synergy in multilingual modelling, whereas BPE more effectively facilitates transfer during cross-lingual fine-tuning. Notably, our results suggest that differences in orthographic word boundary conventions (the morphological granularity of written words) may impede cross-lingual transfer more significantly than linguistic unrelatedness. Our study confirms that decisions around subword modelling can be key to optimising the benefits of multilingual modelling.

Related papers

• Cross-Linguistic Syntactic Difference in Multilingual BERT: How Good is It and How Does It Affect Transfer? [50.48082721476612]
  Multilingual BERT (mBERT) has demonstrated considerable cross-lingual syntactic ability. We investigate the distributions of grammatical relations induced from mBERT in the context of 24 typologically different languages.
  arXiv  Detail & Related papers  (2022-12-21T09:44:08Z)
• A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning [6.329304732560936]
  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.
  arXiv  Detail & Related papers  (2022-10-18T15:36:53Z)
• Investigating the Impact of Cross-lingual Acoustic-Phonetic Similarities on Multilingual Speech Recognition [31.575930914290762]
  A novel data-driven approach is proposed to investigate the cross-lingual acoustic-phonetic similarities. Deep neural networks are trained as mapping networks to transform the distributions from different acoustic models into a directly comparable form. A relative improvement of 8% over monolingual counterpart is achieved.
  arXiv  Detail & Related papers  (2022-07-07T15:55:41Z)
• When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer [15.578267998149743]
  We show that the absence of sub-word overlap significantly affects zero-shot transfer when languages differ in their word order. There is a strong correlation between transfer performance and word embedding alignment between languages. Our results call for focus in multilingual models on explicitly improving word embedding alignment between languages.
  arXiv  Detail & Related papers  (2021-10-27T21:25:39Z)
• An Isotropy Analysis in the Multilingual BERT Embedding Space [18.490856440975996]
  We investigate the representation degeneration problem in multilingual contextual word representations (CWRs) of BERT. Our results show that increasing the isotropy of multilingual embedding space can significantly improve its representation power and performance. Our analysis indicates that although the degenerated directions vary in different languages, they encode similar linguistic knowledge, suggesting a shared linguistic space among languages.
  arXiv  Detail & Related papers  (2021-10-09T08:29:49Z)
• Discovering Representation Sprachbund For Multilingual Pre-Training [139.05668687865688]
  We generate language representation from multilingual pre-trained models and conduct linguistic analysis. We cluster all the target languages into multiple groups and name each group as a representation sprachbund. Experiments are conducted on cross-lingual benchmarks and significant improvements are achieved compared to strong baselines.
  arXiv  Detail & Related papers  (2021-09-01T09:32:06Z)
• Adaptive Sparse Transformer for Multilingual Translation [18.017674093519332]
  A known challenge of multilingual models is the negative language interference. We propose an adaptive and sparse architecture for multilingual modeling. Our model outperforms strong baselines in terms of translation quality without increasing the inference cost.
  arXiv  Detail & Related papers  (2021-04-15T10:31:07Z)
• On Negative Interference in Multilingual Models: Findings and A Meta-Learning Treatment [59.995385574274785]
  We show that, contrary to previous belief, negative interference also impacts low-resource languages. We present a meta-learning algorithm that obtains better cross-lingual transferability and alleviates negative interference.
  arXiv  Detail & Related papers  (2020-10-06T20:48:58Z)
• Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer [101.58431011820755]
  We study gender bias in multilingual embeddings and how it affects transfer learning for NLP applications. We create a multilingual dataset for bias analysis and propose several ways for quantifying bias in multilingual representations.
  arXiv  Detail & Related papers  (2020-05-02T04:34:37Z)
• Bridging Linguistic Typology and Multilingual Machine Translation with Multi-View Language Representations [83.27475281544868]
  We use singular vector canonical correlation analysis to study what kind of information is induced from each source. We observe that our representations embed typology and strengthen correlations with language relationships. We then take advantage of our multi-view language vector space for multilingual machine translation, where we achieve competitive overall translation accuracy.
  arXiv  Detail & Related papers  (2020-04-30T16:25:39Z)
• 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.