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All Languages Are NOT Created (Tokenized) Equal

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Language Disparity in Natural Language Processing This digital divide in natural language processing (NLP) is an active area of research. 70% of research papers published in a computational linguistics conference only evaluated English.[ Association for Computational Linguistics.

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The State of Transfer Learning in NLP

Sebastian Ruder

2013 ) learned a single representation for every word independent of its context. Major themes Several major themes can be observed in how this paradigm has been applied: From words to words-in-context  Over time, representations incorporate more context. Early approaches such as word2vec ( Mikolov et al.,

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Parsing English in 500 Lines of Python

Explosion

I wrote this blog post in 2013, describing an exciting advance in natural language understanding technology. Natural languages introduce many unexpected ambiguities, which our world-knowledge immediately filters out. The derivation for the transition system we’re using, Arc Hybrid, is in Goldberg and Nivre (2013).

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Multi-domain Multilingual Question Answering

Sebastian Ruder

RC Olympics: The many domains of reading comprehension Datasets in the Fiction domain typically require processing narratives in books such as NarrativeQA ( Kočiský et al., 2013 ), MCScript ( Modi et al., The MLQA alignment and annotation process ( Lewis et al., 2018 ), Children's Book Test ( Hill et al.,

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Major trends in NLP: a review of 20 years of ACL research

NLP People

The 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) is starting this week in Florence, Italy. The universal linguistic principle behind word embeddings is distributional similarity: a word can be characterized by the contexts in which it occurs. Toutanova (2018). Goldberg and G. Hirst (2017).

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