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1
Towards Zero-shot Language Modeling ...
Abstract: Can we construct a neural model that is inductively biased towards learning human languages? Motivated by this question, we aim at constructing an informative prior over neural weights, in order to adapt quickly to held-out languages in the task of character-level language modeling. We infer this distribution from a sample of typologically diverse training languages via Laplace approximation. The use of such a prior outperforms baseline models with an uninformative prior (so-called "fine-tuning") in both zero-shot and few-shot settings. This shows that the prior is imbued with universal phonological knowledge. Moreover, we harness additional language-specific side information as distant supervision for held-out languages. Specifically, we condition language models on features from typological databases, by concatenating them to hidden states or generating weights with hyper-networks. These features appear beneficial in the few-shot setting, but not in the zero-shot setting. Since the paucity of digital texts ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/2108.03334
https://dx.doi.org/10.48550/arxiv.2108.03334
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2
Multilingual and Cross-Lingual Intent Detection from Spoken Data ...
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3
Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems ...
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4
Modelling Latent Translations for Cross-Lingual Transfer ...
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5
Prix-LM: Pretraining for Multilingual Knowledge Base Construction ...
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6
Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking ...
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7
xGQA: Cross-Lingual Visual Question Answering ...
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8
On Cross-Lingual Retrieval with Multilingual Text Encoders ...
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9
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models ...
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10
Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval ...
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11
AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples ...
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12
Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ...
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