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Semi-Supervised Learning on Meta Structure: Multi-Task Tagging and Parsing in Low-Resource Scenarios
In: Conference of the Association for the Advancement of Artificial Intelligence ; https://hal.archives-ouvertes.fr/hal-02895835 ; Conference of the Association for the Advancement of Artificial Intelligence, Association for the Advancement of Artificial Intelligence, Feb 2020, New York, United States ; https://aaai.org/Conferences/AAAI-20/ (2020)
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StructSum: Summarization via Structured Representations ...
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Harnessing Code Switching to Transcend the Linguistic Barrier ...
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Improving Candidate Generation for Low-resource Cross-lingual Entity Linking
In: Transactions of the Association for Computational Linguistics, Vol 8, Pp 109-124 (2020) (2020)
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Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework ...
Wang, Zirui; Xie, Jiateng; Xu, Ruochen. - : arXiv, 2019
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Learning Rhyming Constraints using Structured Adversaries ...
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Domain Adaptation of Neural Machine Translation by Lexicon Induction ...
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Language Technologies for Humanitarian Aid ...
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Language Technologies for Humanitarian Aid ...
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Active Semi-Supervised Learning for Improving Word Alignment ...
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Active Semi-Supervised Learning for Improving Word Alignment ...
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Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations ...
Abstract: Much work in Natural Language Processing (NLP) has been for resource-rich languages, making generalization to new, less-resourced languages challenging. We present two approaches for improving generalization to low-resourced languages by adapting continuous word representations using linguistically motivated subword units: phonemes, morphemes and graphemes. Our method requires neither parallel corpora nor bilingual dictionaries and provides a significant gain in performance over previous methods relying on these resources. We demonstrate the effectiveness of our approaches on Named Entity Recognition for four languages, namely Uyghur, Turkish, Bengali and Hindi, of which Uyghur and Bengali are low resource languages, and also perform experiments on Machine Translation. Exploiting subwords with transfer learning gives us a boost of +15.2 NER F1 for Uyghur and +9.7 F1 for Bengali. We also show improvements in the monolingual setting where we achieve (avg.) +3 F1 and (avg.) +1.35 BLEU. ... : Accepted at EMNLP 2018 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/1808.09500
https://dx.doi.org/10.48550/arxiv.1808.09500
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13
An Efficient Interlingua Translation System for Multi-lingual Document Production ...
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ParaMor: Finding Paradigms across Morphology ...
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ParaMor: Minimally-Supervised Induction of Paradigm Structure and Morphological Analysis ...
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ParaMor: Finding Paradigms across Morphology ...
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ParaMor: Finding Paradigms across Morphology ...
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ParaMor: Finding Paradigms across Morphology ...
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Neural Cross-Lingual Named Entity Recognition with Minimal Resources ...
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Zero-shot Neural Transfer for Cross-lingual Entity Linking ...
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