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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).

tor and a standard (poor) machine translation system using 4 reference translations for each of 127 source sentences. The average precision results are shown in Figure 1. Figure 1: Distinguishing Human from Machine ˘ ˇ ˆ The strong signal differentiating human (high pre-cision) from machine (low precision) is striking.

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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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Entity, Relation, and Event Extraction with Contextualized ...

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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Exploring Pre-trained Language Models for Event Extraction ...

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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Lecture Notes in MACHINE LEARNING

news.vidyaacademy.ac.in

portant algorithms in machine learning. In nearly all cases, whenever a new concept is introduced it has been illustrated with “toy examples” and also with examples from real life situations. In the case of algorithms, wherever possible, the working of the algorithm has been illustrated with con-crete numerical examples.

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A arXiv:1412.6572v3 [stat.ML] 20 Mar 2015

arxiv.org

Szegedy et al. (2014b) made an intriguing discovery: several machine learning models, including state-of-the-art neural networks, are vulnerable to adversarial examples. That is, these machine learning models misclassify examples that are only slightly different from correctly classified exam-ples drawn from the data distribution.

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A Review Paper on Machine Learning Based …

ijedr.org

The precision is the percentage of movies classified as higher that are higher, and recall is the percentage of higher items that were classified as higher. In addition to this, the F-measure is also used. The F-measure was proposed as means of intuitively representing the two measures and overcoming the inverse proportion of precision and recall.

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DEEP LEARNING B.TECH-IT VIII SEM QUESTION BANK …

ccsuniversity.ac.in

the backend is complicated and takes care of the precision factor but seems to be a simple application of ML at the front end. • Similar Pins: Machine learning is the core element of Computer Vision, which is a technique to extract useful information from images and videos. Pinterest uses computer vision

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Pattern Recognition and Machine Learning by Bishop

tommyodland.com

The multidimensional Gaussian can be diagonalized by diagonalizing the precision matrix = T1, then exp(xT x) ˘=exp(y Dy), where D= diag(d 1;:::;d D). One limitation is the unimodal nature of the Gaussian, i.e. it has a single peak. Partitioned Gaussians. Let x˘N(xj ; ), where = 1 and x= x a x b = a b = aa ab ba bb: : { Conditional ...

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Worked Examples from Introductory Physics (Algebra–Based ...

www2.tntech.edu

to express large and small numbers conveniently (and also express the precision of those numbers). We will need the basic algebra operations of powers and roots and we will solve equations to find the “unknowns”. Usually the algebra will be very simple. But if we are ever faced with an equation that looks like ax2 +bx+c = 0 (1.1)

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Machine Learning Modelling in R : : CHEAT SHEET

raw.githubusercontent.com

Classification: Accuracy, precision, Simple models with ew parameters areeasier to compute but lead to poorer fits (high bias). Complex models more fits but over-fit the data 'high variance) Cross validation compares the test performances of different model realisations with different sets or values of parameters

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