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Sentiment Analysis of Arabic Documents
In: Natural Language Processing for Global and Local Business ; https://hal.archives-ouvertes.fr/hal-03124729 ; Fatih Pinarbasi; M. Nurdan Taskiran. Natural Language Processing for Global and Local Business, pp.307-331, 2021, 9781799842408. ⟨10.4018/978-1-7998-4240-8.ch013⟩ ; https://www.igi-global.com/ (2021)
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2
Evaluating Multiway Multilingual NMT in the Turkic Languages ...
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3
Findings of the WMT 2021 Shared Task on Quality Estimation ...
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4
Pushing the Right Buttons: Adversarial Evaluation of Quality Estimation ...
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5
Multilingual Domain Adaptation for NMT: Decoupling Language and Domain Information with Adapters ...
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6
Robust Open-Vocabulary Translation from Visual Text Representations ...
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7
Contrastive Learning for Context-aware Neural Machine Translation Using Coreference Information ...
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8
To Ship or Not to Ship: An Extensive Evaluation of Automatic Metrics for Machine Translation ...
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9
Identifying the Importance of Content Overlap for Better Cross-lingual Embedding Mappings ...
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10
Simultaneous Neural Machine Translation with Constituent Label Prediction ...
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11
Bridging the Gap Between Ontology and Lexicon via Class-Specific Association Rules Mined from a Loosely-Parallel Text-Data Corpus ...
Ell, Basil; Elahi, Mohammad Fazleh; Cimiano, Philipp. - : Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021
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12
Just Ask! Evaluating Machine Translation by Asking and Answering Questions ...
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13
Findings of the WMT Shared Task on Machine Translation Using Terminologies ...
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14
Translation Transformers Rediscover Inherent Data Domains ...
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15
Phrase-level Active Learning for Neural Machine Translation ...
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16
A Fine-Grained Analysis of BERTScore ...
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17
Improving Sentiment Polarity Detection through Target Identification
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18
Portuguese Comparative Sentences: A Collection of Labeled Sentences on Twitter and Buscapé ...
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19
SENTIMENT ANALYSIS FOR ARABIC TWEETS DATASETS: LEXICON-BASED AND MACHINE LEARNING APPROACHES ...
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SENTIMENT ANALYSIS FOR ARABIC TWEETS DATASETS: LEXICON-BASED AND MACHINE LEARNING APPROACHES ...
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