[PDF] Top 20 IEST: WASSA 2018 Implicit Emotions Shared Task
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IEST: WASSA 2018 Implicit Emotions Shared Task
... infer emotions from subtle descriptions of situations, instead of purely associ- ating emotion words with emotion ...the IEST data perform on other data sets. Bostan and Klinger (2018) showed that ... See full document
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Sentylic at IEST 2018: Gated Recurrent Neural Network and Capsule Network Based Approach for Implicit Emotion Detection
... the Implicit WASSA 2018 Implicit Emotion Shared ...The task is to pre- dict the emotion of a tweet of which the ex- plicit mentions of emotion terms have been re- ... See full document
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BrainT at IEST 2018: Fine tuning Multiclass Perceptron For Implicit Emotion Classification
... We present BrainT, a multi-class, averaged perceptron tested on implicit emotion predic- tion of tweets. We show that the dataset is linearly separable and explore ways in fine- tuning the baseline classifier. Our ... See full document
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UBC NLP at IEST 2018: Learning Implicit Emotion With an Ensemble of Language Models
... Text task Strapparava and Mihalcea (2007) and Aman and Szpakowicz (2007) are two examples that target the news and blog domains ...basic emotions of Ekman (Ekman, ... See full document
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HUMIR at IEST 2018: Lexicon Sensitive and Left Right Context Sensitive BiLSTM for Implicit Emotion Recognition
... the WASSA-2018 shared task on the implicit emotion ...this task is to predict the emotion expressed by the target word that has been ex- cluded from the given ...this task ... See full document
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NTUA SLP at IEST 2018: Ensemble of Neural Transfer Methods for Implicit Emotion Classification
... In this paper we present our approach to tackle the Implicit Emotion Shared Task (IEST) or- ganized as part of WASSA 2018 at EMNLP 2018. Given a tweet, from which a ... See full document
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IIIDYT at IEST 2018: Implicit Emotion Classification With Deep Contextualized Word Representations
... As language usually reflects the emotional state of an individual, it is natural to study human emo- tions by understanding how they are reflected in text. We see that many words indeed have af- fect as a core part of ... See full document
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HGSGNLP at IEST 2018: An Ensemble of Machine Learning and Deep Neural Architectures for Implicit Emotion Classification in Tweets
... the WASSA-2018 Implicit Emotion Shared Task ...The task is to predict the emo- tion category expressed in a tweet by remov- ing the terms angry, afraid, happy, sad, sur- prised, ... See full document
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DataSEARCH at IEST 2018: Multiple Word Embedding based Models for Implicit Emotion Classification of Tweets with Deep Learning
... Ben Eisner, Tim Rockt¨aschel, Isabelle Augenstein, Matko Boˇsnjak, and Sebastian Riedel. 2016. emoji2vec: Learning emoji representations from their description. arXiv preprint arXiv:1609.08359. Fr´ederic Godin, Baptist ... See full document
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EmotiKLUE at IEST 2018: Topic Informed Classification of Implicit Emotions
... We presented EmotiKLUE, a topic-informed deep learning system for detecting implicit emo- tion. Our experiments showed that for this task skip-gram-based word embeddings outper- form CBOW-based embeddings. ... See full document
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Disney at IEST 2018: Predicting Emotions using an Ensemble
... Typically, a system developed for implicit emo- tion prediction must understand the meaning of the entire text and not just predict using a few key- words. We propose a model which uses a CNN based architecture ... See full document
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Findings of the WMT 2018 Shared Task on Automatic Post Editing
... WMT shared task on MT Automatic Post-Editing. The task con- sists in automatically correcting the out- put of a “black-box” machine translation system by learning from human correc- ... See full document
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Report of NEWS 2018 Named Entity Transliteration Shared Task
... NEWS 2018 Shared Task would like to thank the Institute for Infocomm Research (Singapore), National University of Singapore, Artificial Intelligence Laboratory at the Ho Chi Minh City University of ... See full document
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UZH at CoNLL–SIGMORPHON 2018 Shared Task on Universal Morphological Reinflection
... 4.2 Task II: Inflection Generation in Context Our submission involves a minor change to the model described ...of Task II, we compress the immediate context into context vector g and use it in place of the ... See full document
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SB@GU at the Complex Word Identification 2018 Shared Task
... this shared task, we experimented with the 2016 CWI shared task data and trained classifiers on ...the 2018 devel- opment data, but results were inferior to training on the 2018 ... See full document
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A Neural Graph based Approach to Verbal MWE Identification
... Another way to classify MWE identification ap- proaches is based on whether the process of MWE prediction takes place before, during, or after (syn- tactic and/or semantic) parsing. The joint solu- tion is typically ... See full document
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A Report on the 2018 VUA Metaphor Detection Shared Task
... In this paper, we report on the first shared task on automatic metaphor detection. By making available an easily accessible common dataset and framework for evaluation, we hope to contribute to the ... See full document
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The SLT Interactions Parsing System at the CoNLL 2018 Shared Task
... Given the nature of the shared task, sentence and word segmentation are the two major prerequi- site tasks needed for parsing the evaluation data. For most of the languages, we rely on UDPipe for both ... See full document
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AX Semantics’ Submission to the CoNLL–SIGMORPHON 2018 Shared Task
... flected forms. Of course if the system has a better accuracy on the task, it makes fewer errors than the other system. This calculation is not very conclu- sive, but allows for some basic predicates, see Fig- ure ... See full document
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The CoNLL–SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection
... be able to make some accurate predictions on this 23% by decomposing each test bundle into indi- vidual morphological features such as FUT (fu- ture) and PL (plural), and generalizing from train- ing examples that ... See full document
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