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1
A Quantitative and Qualitative Analysis of Schizophrenia Language ...
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Towards Responsible Natural Language Annotation for the Varieties of Arabic ...
Bergman, A. Stevie; Diab, Mona T.. - : arXiv, 2022
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3
Gender Bias Amplification During Speed-Quality Optimization in Neural Machine Translation ...
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4
Few-shot Learning with Multilingual Language Models ...
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5
AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization ...
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6
Green NLP panel ...
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7
Detecting Hallucinated Content in Conditional Neural Sequence Generation ...
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8
Gender bias amplification during Speed-Quality optimization in Neural Machine Translation ...
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9
Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data ...
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10
Discrete Cosine Transform as Universal Sentence Encoder ...
Almarwani, Nada; Diab, Mona. - : arXiv, 2021
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11
Discrete Cosine Transform as Universal Sentence Encoder ...
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12
Detecting Urgency Status of Crisis Tweets: A Transfer Learning Approach for Low Resource Languages ...
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13
DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking ...
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14
Mutlitask Learning for Cross-Lingual Transfer of Semantic Dependencies ...
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15
Overview for the Second Shared Task on Language Identification in Code-Switched Data ...
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16
WASA: A Web Application for Sequence Annotation ...
AlGhamdi, Fahad; Diab, Mona. - : arXiv, 2019
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17
Creating a Large Multi-Layered Representational Repository of Linguistic Code Switched Arabic Data ...
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18
Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues ...
Levi, Or; Hosseini, Pedram; Diab, Mona. - : arXiv, 2019
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19
Part of speech tagging for code switched data ...
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20
Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task ...
Abstract: In the third shared task of the Computational Approaches to Linguistic Code-Switching (CALCS) workshop, we focus on Named Entity Recognition (NER) on code-switched social-media data. We divide the shared task into two competitions based on the English-Spanish (ENG-SPA) and Modern Standard Arabic-Egyptian (MSA-EGY) language pairs. We use Twitter data and 9 entity types to establish a new dataset for code-switched NER benchmarks. In addition to the CS phenomenon, the diversity of the entities and the social media challenges make the task considerably hard to process. As a result, the best scores of the competitions are 63.76% and 71.61% for ENG-SPA and MSA-EGY, respectively. We present the scores of 9 participants and discuss the most common challenges among submissions. ... : ACL 2018 (CALCS) ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/1906.04138
https://dx.doi.org/10.48550/arxiv.1906.04138
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