How Transliterations Improve Crosslingual Alignment

• URL: http://arxiv.org/abs/2409.17326v2
• Date: Sun, 15 Dec 2024 10:14:11 GMT
• Title: How Transliterations Improve Crosslingual Alignment
• Authors: Yihong Liu, Mingyang Wang, Amir Hossein Kargaran, Ayyoob Imani, Orgest Xhelili, Haotian Ye, Chunlan Ma, François Yvon, Hinrich Schütze,
• Abstract summary: Recent studies have shown that post-aligning multilingual pretrained language models (mPLMs) using alignment objectives can improve crosslingual alignment. This paper attempts to explicitly evaluate the crosslingual alignment and identify the key elements in transliteration-based approaches that contribute to better performance.
• Score: 48.929677368744606
• License:
• Abstract: Recent studies have shown that post-aligning multilingual pretrained language models (mPLMs) using alignment objectives on both original and transliterated data can improve crosslingual alignment. This improvement further leads to better crosslingual transfer performance. However, it remains unclear how and why a better crosslingual alignment is achieved, as this technique only involves transliterations, and does not use any parallel data. This paper attempts to explicitly evaluate the crosslingual alignment and identify the key elements in transliteration-based approaches that contribute to better performance. For this, we train multiple models under varying setups for two pairs of related languages: (1) Polish and Ukrainian and (2) Hindi and Urdu. To assess alignment, we define four types of similarities based on sentence representations. Our experimental results show that adding transliterations alone improves the overall similarities, even for random sentence pairs. With the help of auxiliary transliteration-based alignment objectives, especially the contrastive objective, the model learns to distinguish matched from random pairs, leading to better crosslingual alignment. However, we also show that better alignment does not always yield better downstream performance, suggesting that further research is needed to clarify the connection between alignment and performance. The code implementation is based on \url{https://github.com/cisnlp/Transliteration-PPA}.

Related papers

• 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)
• Exploring the Relationship between Alignment and Cross-lingual Transfer in Multilingual Transformers [0.6882042556551609]
  multilingual language models can achieve cross-lingual transfer without explicit cross-lingual training data. One common way to improve this transfer is to perform realignment steps before fine-tuning. But realignment methods were found to not always improve results across languages and tasks.
  arXiv  Detail & Related papers  (2023-06-05T11:35:40Z)
• Dual-Alignment Pre-training for Cross-lingual Sentence Embedding [79.98111074307657]
  We propose a dual-alignment pre-training (DAP) framework for cross-lingual sentence embedding. We introduce a novel representation translation learning (RTL) task, where the model learns to use one-side contextualized token representation to reconstruct its translation counterpart. Our approach can significantly improve sentence embedding.
  arXiv  Detail & Related papers  (2023-05-16T03:53:30Z)
• VECO 2.0: Cross-lingual Language Model Pre-training with Multi-granularity Contrastive Learning [56.47303426167584]
  We propose a cross-lingual pre-trained model VECO2.0 based on contrastive learning with multi-granularity alignments. Specifically, the sequence-to-sequence alignment is induced to maximize the similarity of the parallel pairs and minimize the non-parallel pairs. token-to-token alignment is integrated to bridge the gap between synonymous tokens excavated via the thesaurus dictionary from the other unpaired tokens in a bilingual instance.
  arXiv  Detail & Related papers  (2023-04-17T12:23:41Z)
• Using Optimal Transport as Alignment Objective for fine-tuning Multilingual Contextualized Embeddings [7.026476782041066]
  We propose using Optimal Transport (OT) as an alignment objective during fine-tuning to improve multilingual contextualized representations. This approach does not require word-alignment pairs prior to fine-tuning and instead learns the word alignments within context in an unsupervised manner.
  arXiv  Detail & Related papers  (2021-10-06T16:13:45Z)
• Cross-language Sentence Selection via Data Augmentation and Rationale Training [22.106577427237635]
  It uses data augmentation and negative sampling techniques on noisy parallel sentence data to learn a cross-lingual embedding-based query relevance model. Results show that this approach performs as well as or better than multiple state-of-the-art machine translation + monolingual retrieval systems trained on the same parallel data.
  arXiv  Detail & Related papers  (2021-06-04T07:08:47Z)
• Word Alignment by Fine-tuning Embeddings on Parallel Corpora [96.28608163701055]
  Word alignment over parallel corpora has a wide variety of applications, including learning translation lexicons, cross-lingual transfer of language processing tools, and automatic evaluation or analysis of translation outputs. Recently, other work has demonstrated that pre-trained contextualized word embeddings derived from multilingually trained language models (LMs) prove an attractive alternative, achieving competitive results on the word alignment task even in the absence of explicit training on parallel data. In this paper, we examine methods to marry the two approaches: leveraging pre-trained LMs but fine-tuning them on parallel text with objectives designed to improve alignment quality, and proposing
  arXiv  Detail & Related papers  (2021-01-20T17:54:47Z)
• Do Explicit Alignments Robustly Improve Multilingual Encoders? [22.954688396858085]
  multilingual encoders can effectively learn cross-lingual representation. Explicit alignment objectives based on bitexts like Europarl or MultiUN have been shown to further improve these representations. We propose a new contrastive alignment objective that can better utilize such signal.
  arXiv  Detail & Related papers  (2020-10-06T07:43:17Z)
• On the Language Neutrality of Pre-trained Multilingual Representations [70.93503607755055]
  We investigate the language-neutrality of multilingual contextual embeddings directly and with respect to lexical semantics. Our results show that contextual embeddings are more language-neutral and, in general, more informative than aligned static word-type embeddings. We show how to reach state-of-the-art accuracy on language identification and match the performance of statistical methods for word alignment of parallel sentences.
  arXiv  Detail & Related papers  (2020-04-09T19:50:32Z)

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.