Clustering Pseudo Language Family in Multilingual Translation Models with Fisher Information Matrix

• URL: http://arxiv.org/abs/2312.02820v1
• Date: Tue, 5 Dec 2023 15:03:27 GMT
• Title: Clustering Pseudo Language Family in Multilingual Translation Models with Fisher Information Matrix
• Authors: Xinyu Ma and Xuebo Liu and Min Zhang
• Abstract summary: Clustering languages based solely on their ancestral families can yield suboptimal results. We propose an innovative method that leverages the fisher information matrix (FIM) to cluster language families. We provide an in-depth discussion regarding the inception and application of these pseudo language families.
• Score: 22.891944602891428
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In multilingual translation research, the comprehension and utilization of language families are of paramount importance. Nevertheless, clustering languages based solely on their ancestral families can yield suboptimal results due to variations in the datasets employed during the model's training phase. To mitigate this challenge, we introduce an innovative method that leverages the fisher information matrix (FIM) to cluster language families, anchored on the multilingual translation model's characteristics. We hypothesize that language pairs with similar effects on model parameters exhibit a considerable degree of linguistic congruence and should thus be grouped cohesively. This concept has led us to define pseudo language families. We provide an in-depth discussion regarding the inception and application of these pseudo language families. Empirical evaluations reveal that employing these pseudo language families enhances performance over conventional language families in adapting a multilingual translation model to unfamiliar language pairs. The proposed methodology may also be extended to scenarios requiring language similarity measurements. The source code and associated scripts can be accessed at https://github.com/ecoli-hit/PseudoFamily.

Related papers

• From Isolates to Families: Using Neural Networks for Automated Language Affiliation [9.182884165239996]
  In historical linguistics, the affiliation of languages to a common language family is traditionally carried out using a complex workflow. Large-scale standardized collections of multilingual wordlists and grammatical language structures might help to improve this and open new avenues for developing automated language affiliation. We present neural network models that use lexical and grammatical data from a worldwide sample of more than 1,000 languages with known affiliations to classify individual languages into families.
  arXiv  Detail & Related papers  (2025-02-17T11:25:32Z)
• Scaling Laws for Multilingual Language Models [41.6318470003173]
  A primary challenge in studying multilingual scaling is the difficulty of analyzing individual language performance due to cross-lingual transfer. We introduce and validate a hypothesis that the test cross-entropy loss for each language family is determined solely by its own sampling ratio. We derive a power-law relationship that links performance with dataset size, model size and sampling ratios.
  arXiv  Detail & Related papers  (2024-10-15T20:29:38Z)
• Phylogeny-Inspired Adaptation of Multilingual Models to New Languages [43.62238334380897]
  We show how we can use language phylogenetic information to improve cross-lingual transfer leveraging closely related languages. We perform adapter-based training on languages from diverse language families (Germanic, Uralic, Tupian, Uto-Aztecan) and evaluate on both syntactic and semantic tasks.
  arXiv  Detail & Related papers  (2022-05-19T15:49:19Z)
• A Massively Multilingual Analysis of Cross-linguality in Shared Embedding Space [61.18554842370824]
  In cross-lingual language models, representations for many different languages live in the same space. We compute a task-based measure of cross-lingual alignment in the form of bitext retrieval performance. We examine a range of linguistic, quasi-linguistic, and training-related features as potential predictors of these alignment metrics.
  arXiv  Detail & Related papers  (2021-09-13T21:05:37Z)
• 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)
• Probing Multilingual Language Models for Discourse [0.0]
  We find that the XLM-RoBERTa family of models consistently show the best performance. Our results also indicate that model distillation may hurt the ability of cross-lingual transfer of sentence representations.
  arXiv  Detail & Related papers  (2021-06-09T06:34:21Z)
• Automatically Identifying Language Family from Acoustic Examples in Low Resource Scenarios [48.57072884674938]
  We propose a method to analyze language similarity using deep learning. Namely, we train a model on the Wilderness dataset and investigate how its latent space compares with classical language family findings.
  arXiv  Detail & Related papers  (2020-12-01T22:44:42Z)
• XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning [68.57658225995966]
  Cross-lingual Choice of Plausible Alternatives (XCOPA) is a typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages. We evaluate a range of state-of-the-art models on this novel dataset, revealing that the performance of current methods falls short compared to translation-based transfer.
  arXiv  Detail & Related papers  (2020-05-01T12:22:33Z)
• 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)
• On the Importance of Word Order Information in Cross-lingual Sequence Labeling [80.65425412067464]
  Cross-lingual models that fit into the word order of the source language might fail to handle target languages. We investigate whether making models insensitive to the word order of the source language can improve the adaptation performance in target languages.
  arXiv  Detail & Related papers  (2020-01-30T03:35:44Z)

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.