LINSPECTOR WEB: A multilingual probing suite for word representations
Eichler M, Şahin GG, Gurevych I (2019)
Publication Type: Conference contribution
Publication year: 2019
Publisher: Association for Computational Linguistics (ACL)
Pages Range: 127-132
Conference Proceedings Title: EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Proceedings of System Demonstrations
Event location: Hong Kong, CHN
ISBN: 9781950737925
DOI: 10.18653/v1/D19-3022
Abstract
We present LINSPECTOR WEB, an open source multilingual inspector to analyze word representations. Our system provides researchers working in low-resource settings with an easily accessible web based probing tool to gain quick insights into their word embeddings especially outside of the English language. To do this we employ 16 simple linguistic probing tasks such as gender, case marking, and tense for a diverse set of 28 languages. We support probing of static word embeddings along with pretrained AllenNLP models that are commonly used for NLP downstream tasks such as named entity recognition, natural language inference and dependency parsing. The results are visualized in a polar chart and also provided as a table. LINSPECTOR WEB is available as an offline tool or at https://linspector.ukp.informatik.tu-darmstadt.de.
Involved external institutions
Germany (DE)How to cite
APA:
Eichler, M., Şahin, G.G., & Gurevych, I. (2019). LINSPECTOR WEB: A multilingual probing suite for word representations. In EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Proceedings of System Demonstrations (pp. 127-132). Hong Kong, CHN: Association for Computational Linguistics (ACL).
MLA:
Eichler, Max, Gözde Gül Şahin, and Iryna Gurevych. "LINSPECTOR WEB: A multilingual probing suite for word representations." Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Proceedings of System Demonstration, EMNLP-IJCNLP 2019, Hong Kong, CHN Association for Computational Linguistics (ACL), 2019. 127-132.
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