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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 9 of 9 1 Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages ... Ruiter, Dana; Klakow, Dietrich; van Genabith, Josef. - : arXiv, 2021 BASE Show details 2 Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification ... Pylypenko, Daria; Amponsah-Kaakyire, Kwabena; Chowdhury, Koel Dutta. - : arXiv, 2021 BASE Show details 3 The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe ... Rehm, Georg; Marheinecke, Katrin; Hegele, Stefanie. - : arXiv, 2020 BASE Show details 4 Linguistically inspired morphological inflection with a sequence to sequence model ... Metheniti, Eleni; Neumann, Guenter; van Genabith, Josef. - : arXiv, 2020 BASE Show details 5 Probing Word Translations in the Transformer and Trading Decoder for Encoder Layers ... Xu, Hongfei; van Genabith, Josef; Liu, Qiuhui. - : arXiv, 2020 BASE Show details 6 INFODENS: An Open-source Framework for Learning Text Representations ... Taie, Ahmad; Rubino, Raphael; van Genabith, Josef. - : arXiv, 2018 Abstract: The advent of representation learning methods enabled large performance gains on various language tasks, alleviating the need for manual feature engineering. While engineered representations are usually based on some linguistic understanding and are therefore more interpretable, learned representations are harder to interpret. Empirically studying the complementarity of both approaches can provide more linguistic insights that would help reach a better compromise between interpretability and performance. We present INFODENS, a framework for studying learned and engineered representations of text in the context of text classification tasks. It is designed to simplify the tasks of feature engineering as well as provide the groundwork for extracting learned features and combining both approaches. INFODENS is flexible, extensible, with a short learning curve, and is easy to integrate with many of the available and widely used natural language processing tools. ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.1810.07091 https://arxiv.org/abs/1810.07091 BASE Hide details 7 Massively Multilingual Neural Grapheme-to-Phoneme Conversion ... Peters, Ben; Dehdari, Jon; van Genabith, Josef. - : arXiv, 2017 BASE Show details 8 An Empirical Analysis of NMT-Derived Interlingual Embeddings and their Use in Parallel Sentence Identification ... España-Bonet, Cristina; Varga, Ádám Csaba; Barrón-Cedeño, Alberto. - : arXiv, 2017 BASE Show details 9 Predicting the Law Area and Decisions of French Supreme Court Cases ... Sulea, Octavia-Maria; Zampieri, Marcos; Vela, Mihaela. - : arXiv, 2017 BASE Show details

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