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Applying the Transformer to Character-level Transduction ...
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Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction ...
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Applying the Transformer to Character-level Transduction
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In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (2021)
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Do Explicit Alignments Robustly Improve Multilingual Encoders? ...
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SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ...
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The Paradigm Discovery Problem
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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020)
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Emerging Cross-lingual Structure in Pretrained Language Models ...
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Abstract:
We study the problem of multilingual masked language modeling, i.e. the training of a single model on concatenated text from multiple languages, and present a detailed study of several factors that influence why these models are so effective for cross-lingual transfer. We show, contrary to what was previously hypothesized, that transfer is possible even when there is no shared vocabulary across the monolingual corpora and also when the text comes from very different domains. The only requirement is that there are some shared parameters in the top layers of the multi-lingual encoder. To better understand this result, we also show that representations from independently trained models in different languages can be aligned post-hoc quite effectively, strongly suggesting that, much like for non-contextual word embeddings, there are universal latent symmetries in the learned embedding spaces. For multilingual masked language modeling, these symmetries seem to be automatically discovered and aligned during the ... : ACL 2020 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.1911.01464 https://arxiv.org/abs/1911.01464
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The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection ...
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