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240 resources
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Esin Durmus, He He, Mona Diab|Jul 1st, 2020|conferencePaperEsin Durmus, He He, Mona DiabJul 1st, 2020
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Shikib Mehri, Maxine Eskenazi|Jul 1st, 2020|conferencePaperShikib Mehri, Maxine EskenaziJul 1st, 2020
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Thibault Sellam, Dipanjan Das, Ankur Par...|Jul 1st, 2020|conferencePaperThibault Sellam, Dipanjan Das, Ankur Par...Jul 1st, 2020
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V Vijayaraghavan, Jack Brian Cooper, oth...|Jul 1st, 2020|journalArticleV Vijayaraghavan, Jack Brian Cooper, oth...Jul 1st, 2020
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Samuel Holmes, Anne Moorhead, Raymond Bo...|Sep 10th, 2019|conferencePaperSamuel Holmes, Anne Moorhead, Raymond Bo...Sep 10th, 2019
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Elizabeth Clark, Asli Celikyilmaz, Noah ...|Jul 1st, 2019|conferencePaperElizabeth Clark, Asli Celikyilmaz, Noah ...Jul 1st, 2019
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Chris Van Der Lee, Albert Gatt, Emiel Va...|Jul 1st, 2019|conferencePaperChris Van Der Lee, Albert Gatt, Emiel Va...Jul 1st, 2019
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Kavita Ganesan|Mar 5th, 2018|preprintKavita GanesanMar 5th, 2018
Evaluation of summarization tasks is extremely crucial to determining the quality of machine generated summaries. Over the last decade, ROUGE has become the standard automatic evaluation measure for evaluating summarization tasks. While ROUGE has been shown to be effective in capturing n-gram overlap between system and human composed summaries, there are several limitations with the existing ROUGE measures in terms of capturing synonymous concepts and coverage of topics. Thus, often times...
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Ryan Lowe, Michael Noseworthy, Iulian Vl...|Jul 1st, 2017|conferencePaperRyan Lowe, Michael Noseworthy, Iulian Vl...Jul 1st, 2017
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Ramakrishna Vedantam, C. Lawrence Zitnic...|Jun 2nd, 2015|preprintRamakrishna Vedantam, C. Lawrence Zitnic...Jun 2nd, 2015
Automatically describing an image with a sentence is a long-standing challenge in computer vision and natural language processing. Due to recent progress in object detection, attribute classification, action recognition, etc., there is renewed interest in this area. However, evaluating the quality of descriptions has proven to be challenging. We propose a novel paradigm for evaluating image descriptions that uses human consensus. This paradigm consists of three main parts: a new...
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Sami Virpioja, Stig-Arne Grönroos|Jul 1st, 2015|conferencePaperSami Virpioja, Stig-Arne GrönroosJul 1st, 2015
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David Hutchison, Takeo Kanade, Josef Kit...|Jul 1st, 2013|bookSectionDavid Hutchison, Takeo Kanade, Josef Kit...Jul 1st, 2013
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Lise Getoor, Ashwin Machanavajjhala|Aug 1st, 2012|journalArticleLise Getoor, Ashwin MachanavajjhalaAug 1st, 2012
This tutorial brings together perspectives on ER from a variety of fields, including databases, machine learning, natural language processing and information retrieval, to provide, in one setting, a survey of a large body of work. We discuss both the practical aspects and theoretical underpinnings of ER. We describe existing solutions, current challenges, and open research problems.
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R.S.J.d Baker, B. McGaw, P. Peterson|Jul 1st, 2010|bookSectionR.S.J.d Baker, B. McGaw, P. PetersonJul 1st, 2010
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Ehud Reiter, Anja Belz|Dec 1st, 2009|journalArticleEhud Reiter, Anja BelzDec 1st, 2009
There is growing interest in using automatically computed corpus-based evaluation metrics to evaluate Natural Language Generation (NLG) systems, because these are often considerably cheaper than the human-based evaluations which have traditionally been used in NLG. We review previous work on NLG evaluation and on validation of automatic metrics in NLP, and then present the results of two studies of how well some metrics which are popular in other areas of NLP (notably BLEU and ROUGE)...
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Joseph P. Turian, Luke Shen, I. Dan Mela...|Jan 1st, 2006|conferencePaperJoseph P. Turian, Luke Shen, I. Dan Mela...Jan 1st, 2006
Evaluation of MT evaluation measures is limited by inconsistent human judgment data. Nonetheless, machine translation can be evaluated using the well-known measures precision, recall, and their average, the F-measure. The unigram-based F-measure has significantly higher correlation with human judgments than recently proposed alternatives. More importantly, this standard measure has an intuitive graphical interpretation, which can facilitate insight into how MT systems might be improved. The...
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David Hutchison, Takeo Kanade, Josef Kit...|Jul 1st, 2004|bookSectionDavid Hutchison, Takeo Kanade, Josef Kit...Jul 1st, 2004
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Chin-Yew Lin, Franz Josef Och|Jul 1st, 2004|conferencePaperChin-Yew Lin, Franz Josef OchJul 1st, 2004
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George Doddington|Jul 1st, 2002|conferencePaperGeorge DoddingtonJul 1st, 2002
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Kishore Papineni, Salim Roukos, Todd War...|Jul 1st, 2001|conferencePaperKishore Papineni, Salim Roukos, Todd War...Jul 1st, 2001