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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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3
"Wikily" Supervised Neural Translation Tailored to Cross-Lingual Tasks ...
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4
Is "my favorite new movie" my favorite movie? Probing the Understanding of Recursive Noun Phrases ...
Lyu, Qing; Zheng, Hua; Li, Daoxin. - : arXiv, 2021
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Goal-Oriented Script Construction ...
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Visual Goal-Step Inference using wikiHow ...
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Wikily Supervised Neural Translation Tailored to Cross-Lingual Tasks ...
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8
BUSINESS MEETING ...
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9
BiSECT: Learning to Split and Rephrase Sentences with Bitexts ...
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10
Resolving pronouns in Twitter streams: context can help!
Wijaya, Derry Tanti; Andy, Anietie; Callison-Burch, Chris. - : Association for Computational Linguistics, 2020
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11
Intent Detection with WikiHow ...
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12
Resolving Pronouns in Twitter Streams: Context can Help! ...
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13
Artificial Intelligence in mental health and the biases of language based models
In: PLoS One (2020)
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14
Winter is here: summarizing Twitter streams related to pre-scheduled events
Andy, Anietie; Wijaya, Derry Tanti; Callison-Burch, Chris. - : Association for Computational Linguistics, 2019
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15
Bilingual is At Least Monolingual (BALM): A Novel Translation Algorithm that Encodes Monolingual Priors ...
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16
Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims ...
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17
Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification ...
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18
Comparison of Diverse Decoding Methods from Conditional Language Models ...
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19
Paraphrase-Sense-Tagged Sentences
In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 714-728 (2019) (2019)
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20
Learning translations via images with a massively multilingual image dataset
Callison-Burch, Chris; Wijaya, Derry; Kriz, Reno; Ippolito, Daphne; Callahan, Brendan; Hewitt, John. - : Association for Computational Linguistics, 2018
Abstract: We conduct the most comprehensive study to date into translating words via images. To facilitate research on the task, we introduce a large-scale multilingual corpus of images, each labeled with the word it represents. Past datasets have been limited to only a few high-resource languages and unrealistically easy translation settings. In contrast, we have collected by far the largest available dataset for this task, with images for approximately 10,000 words in each of 100 languages. We run experiments on a dozen high resource languages and 20 low resources languages, demonstrating the effect of word concreteness and part-of-speech on translation quality. %We find that while image features work best for concrete nouns, they are sometimes effective on other parts of speech. To improve image-based translation, we introduce a novel method of predicting word concreteness from images, which improves on a previous state-of-the-art unsupervised technique. This allows us to predict when image-based translation may be effective, enabling consistent improvements to a state-of-the-art text-based word translation system. Our code and the Massively Multilingual Image Dataset (MMID) are available at http://multilingual-images.org/. ; Published version
Keyword: Computational linguistics; Linguistics; Massively Multilingual Image Dataset; Translations
URL: https://www.aclweb.org/portal/
https://doi.org/10.18653/v1/P18-1239
https://hdl.handle.net/2144/38467
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