113 research outputs found
Subword Evenness (SuE) as a Predictor of Cross-lingual Transfer to Low-resource Languages
Pre-trained multilingual models, such as mBERT, XLM-R and mT5, are used to improve the performance on various tasks in low-resource languages via cross-lingual transfer. In this framework, English is usually seen as the most natural choice for a transfer language (for fine-tuning or continued training of a multilingual pre-trained model), but it has been revealed recently that this is often not the best choice. The success of cross-lingual transfer seems to depend on some properties of languages, which are currently hard to explain. Successful transfer often happens between unrelated languages and it often cannot be explained by data-dependent factors.In this study, we show that languages written in non-Latin and non-alphabetic scripts (mostly Asian languages) are the best choices for improving performance on the task of Masked Language Modelling (MLM) in a diverse set of 30 low-resource languages and that the success of the transfer is well predicted by our novel measure of Subword Evenness (SuE). Transferring language models over the languages that score low on our measure results in the lowest average perplexity over target low-resource languages. Our correlation coefficients obtained with three different pre-trained multilingual models are consistently higher than all the other predictors, including text-based measures (type-token ratio, entropy) and linguistically motivated choice (genealogical and typological proximity)
Bridging linguistic typology and multilingual machine translation with multi-view language representations
Sparse language vectors from linguistic typology databases and learned
embeddings from tasks like multilingual machine translation have been
investigated in isolation, without analysing how they could benefit from each
other's language characterisation. We propose to fuse both views using singular
vector canonical correlation analysis and study what kind of information is
induced from each source. By inferring typological features and language
phylogenies, 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 in tasks that require
information about language similarities, such as language clustering and
ranking candidates for multilingual transfer. With our method, we can easily
project and assess new languages without expensive retraining of massive
multilingual or ranking models, which are major disadvantages of related
approaches.Comment: 15 pages, 6 figure
When Is Multilinguality a Curse? Language Modeling for 250 High- and Low-Resource Languages
Multilingual language models are widely used to extend NLP systems to
low-resource languages. However, concrete evidence for the effects of
multilinguality on language modeling performance in individual languages
remains scarce. Here, we pre-train over 10,000 monolingual and multilingual
language models for over 250 languages, including multiple language families
that are under-studied in NLP. We assess how language modeling performance in
each language varies as a function of (1) monolingual dataset size, (2) added
multilingual dataset size, (3) linguistic similarity of the added languages,
and (4) model size (up to 45M parameters). We find that in moderation, adding
multilingual data improves low-resource language modeling performance, similar
to increasing low-resource dataset sizes by up to 33%. Improvements depend on
the syntactic similarity of the added multilingual data, with marginal
additional effects of vocabulary overlap. However, high-resource languages
consistently perform worse in multilingual pre-training scenarios. As dataset
sizes increase, adding multilingual data begins to hurt performance for both
low-resource and high-resource languages, likely due to limited model capacity
(the "curse of multilinguality"). These results suggest that massively
multilingual pre-training may not be optimal for any languages involved, but
that more targeted models can significantly improve performance
On the relation between linguistic typology and (limitations of) multilingual language modeling
A key challenge in cross-lingual NLP is developing general language-independent architectures that are equally applicable to any language. However, this ambition is largely hampered by the variation in structural and semantic properties, i.e. the typological profiles of the world's languages. In this work, we analyse the implications of this variation on the language modeling (LM) task. We present a large-scale study of state-of-the art n-gram based and neural language models on 50 typologically diverse languages covering a wide variety of morphological systems. Operating in the full vocabulary LM setup focused on word-level prediction, we demonstrate that a coarse typology of morphological systems is predictive of absolute LM performance. Moreover, fine-grained typological features such as exponence, flexivity, fusion, and inflectional synthesis are borne out to be responsible for the proliferation of low-frequency phenomena which are organically difficult to model by statistical architectures, or for the meaning ambiguity of character n-grams. Our study strongly suggests that these features have to be taken into consideration during the construction of next-level language-agnostic LM architectures, capable of handling morphologically complex languages such as Tamil or Korean.ERC grant Lexica
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