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A Feasibility Study of Answer-Agnostic Question Generation for Education ...
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BiSECT: Learning to Split and Rephrase Sentences with Bitexts ...
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"Wikily" Supervised Neural Translation Tailored to Cross-Lingual Tasks ...
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Is "my favorite new movie" my favorite movie? Probing the Understanding of Recursive Noun Phrases ...
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Wikily Supervised Neural Translation Tailored to Cross-Lingual Tasks ...
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BiSECT: Learning to Split and Rephrase Sentences with Bitexts ...
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Resolving Pronouns in Twitter Streams: Context can Help! ...
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Artificial Intelligence in mental health and the biases of language based models
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In: PLoS One (2020)
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Winter is here: summarizing Twitter streams related to pre-scheduled events
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Bilingual is At Least Monolingual (BALM): A Novel Translation Algorithm that Encodes Monolingual Priors ...
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Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims ...
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Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification ...
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Comparison of Diverse Decoding Methods from Conditional Language Models ...
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Paraphrase-Sense-Tagged Sentences
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In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 714-728 (2019) (2019)
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Abstract:
Many natural language processing tasks require discriminating the particular meaning of a word in context, but building corpora for developing sense-aware models can be a challenge. We present a large resource of example usages for words having a particular meaning, called Paraphrase-Sense-Tagged Sentences (PSTS). Built on the premise that a word’s paraphrases instantiate its fine-grained meanings (i.e., bug has different meanings corresponding to its paraphrases fly and microbe) the resource contains up to 10,000 sentences for each of 3 million target-paraphrase pairs where the target word takes on the meaning of the paraphrase. We describe an automatic method based on bilingual pivoting used to enumerate sentences for PSTS, and present two models for ranking PSTS sentences based on their quality. Finally, we demonstrate the utility of PSTS by using it to build a dataset for the task of hypernym prediction in context. Training a model on this automatically generated dataset produces accuracy that is competitive with a model trained on smaller datasets crafted with some manual effort.
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Keyword:
Computational linguistics. Natural language processing; P98-98.5
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URL: https://doaj.org/article/35fe5b0819ac4ef68cad89ddd12368bf https://doi.org/10.1162/tacl_a_00295
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Learning translations via images with a massively multilingual image dataset
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