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BLEU: a Method for Automatic Evaluation of Machine …

BLEU: a MethodforAutomaticEvaluationofMachineTra nslationKishore Papineni,SalimRoukos,ToddWard,andWei-Jin gZhuIBMT. J. monthstofinishandin-volve proposea methodofautomaticma-chinetranslationeval uationthatis quick,inexpensive,andlanguage-independen t,thatcorrelateshighlywithhumanevalu-ati on, presentthismethodasanauto-matedunderstud yto (MT)weighmany aspectsoftranslation,includingade-quacy, fidelity, andfluencyofthetranslation(Hovy,1999;Whi teandO Connell,1994).Acompre-hensive catalogofMTevaluationtechniquesandtheirr ichliteratureis givenbyReeder(2001).Forthemostpart,these varioushumanevaluationap-proachesarequit eexpensive (Hovy, 1999).More-over, they cantakeweeksormonthsto believe thatMTprogressstemsfromevaluationandthat thereis a logjamoffruitfulresearchideaswaitingtobe releasedfrom1 Sowecallourmethodthebilingualevaluationu nderstudy, automaticevaluationthatisquick,language- independent, machinetranslationis to a professionalhumantranslation,thebetterit judgethequalityofa machinetranslation,onemeasuresitsclosene sstooneormorereferencehumantranslationsa ccord-ingtoa ,ourMTevaluationsystemrequirestwo numerical translationcloseness corpusofgoodqualityhumanreferencetrans-l ationsWe fashionourclosenessmetricafterthehighlys uc-cessfulword errorratemetricusedbyth

tion method in this paper. 1.2 Viewpoint How does one measure translation performance? The closer a machine translation is to a professional human translation, the better it is. This is the cen-tral idea behind our proposal. To judge the quality of a machine translation, one measures its closeness to one or more reference human translations accord-

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Transformer-XL: Attentive Language Models beyond a Fixed ...

aclanthology.org

⌧+1= Transformer-Layer(q n,kn ⌧+1,v n). where the function SG(·) stands for stop-gradient, the notation [hu hv] indicates the concatenation of two hidden sequences along the length dimen-sion, and W· denotes model parameters. Com-pared to the standard Transformer, the critical dif-ference lies in that the key kn ⌧+1 and value v n ⌧+1

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Improving Multimodal Named Entity Recognition via Entity ...

aclanthology.org

with cross-modal attention mechanism to produce an image-aware word representation and a word-aware visual representation for each input word, respectively. Finally, to largely eliminate the bias of the visual context, we propose to leverage text-based entity span detection as an auxiliary task, and design a unified neural architecture based on

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aclanthology.org

Span enumeration Event propagation Coreference Auxiliary Sentence Sentence É É Sentence Figure 1: Overview of our framework: DYGIE++. Shared span representations are constructed by refin-ing contextualized word embeddings via span graph updates, then passed to scoring functions for three IE tasks. Mitchell,2016;Li and Ji,2014) and neural scor-

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CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset ...

aclanthology.org

Sentiment analysis is an important research area in Natural Language Processing (NLP). It has wide applications for other NLP tasks, such as opinion mining, dialogue generation, and user behavior analysis. Previous study (Pang et al.,2008;Liu and Zhang,2012) mainly focused on text sentiment analysis and achieved impressive results. However,

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Recurrent Attention Network on Memory for Aspect …

aclanthology.org

tic analysis (Socher et al.,2010) and sentence sen-timent analysis (Socher et al.,2013). (Dong et al., 2014;Nguyen and Shirai,2015) adopted Rec-NN for aspect sentiment classication, by converting the opinion target as the tree root and propagating the sentiment of targets depending on the context and syntactic relationships between them. How-

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A Joint Neural Model for Information Extraction with ...

aclanthology.org

Entity Extraction aims to identify entity men-tions in text and classify them into pre-defined en-tity types. A mention can be a name, nominal, or pronoun. For example, “Kashmir region” should be recognized as a location (LOC) named entity mention in Figure2. Relation Extraction is the task of assigning a

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aclanthology.org

3 Extraction Model This section describes our approach to extract events that occur in plain text. We consider event extraction as a two-stage task, which includes trig-ger extraction and argument extraction, and pro-pose a Pre-trained Language Model based Event Extractor (PLMEE). Figure3illustrates the archi-tecture of PLMEE.

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Dual Graph Convolutional Networks for Aspect-based ...

aclanthology.org

Aspect-based sentiment analysis is a fine-grained sentiment classification task. Re-cently, graph neural networks over depen-dency trees have been explored to explicitly model connections between aspects and opin-ion words. However, the improvement is lim-ited due to the inaccuracy of the dependency parsing results and the informal expressions

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Learning Implicit Sentiment in Aspect-based Sentiment ...

aclanthology.org

Aspect-based sentiment analysis aims to iden-tify the sentiment polarity of a specific aspect in product reviews. We notice that about 30% of reviews do not contain obvious opinion words, but still convey clear human-aware sen-timent orientation, which is known as implicit sentiment. However, recent neural network-

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Ruddit: Norms of Offensiveness for English Reddit Comments

aclanthology.org

sive tweets from the Twitter search API.Park et al. (2018),Wiegand et al.(2019), andDavidson et al. (2019) show that theWaseem and Hovy 2016) dataset exhibits topic bias and author bias due to the employed sampling strategy.Founta et al.(2018) boosted the representation of offensive class in their dataset by analysing the sentiment of the ...

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