Cross-Lingual Transfer Learning for Complex Word Identification

• URL: http://arxiv.org/abs/2010.01108v1
• Date: Fri, 2 Oct 2020 17:09:47 GMT
• Title: Cross-Lingual Transfer Learning for Complex Word Identification
• Authors: George-Eduard Zaharia, Dumitru-Clementin Cercel, Mihai Dascalu
• Abstract summary: Complex Word Identification (CWI) is a task centered on detecting hard-to-understand words in texts. Our approach uses zero-shot, one-shot, and few-shot learning techniques, alongside state-of-the-art solutions for Natural Language Processing (NLP) tasks. Our aim is to provide evidence that the proposed models can learn the characteristics of complex words in a multilingual environment.
• Score: 0.3437656066916039
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Complex Word Identification (CWI) is a task centered on detecting hard-to-understand words, or groups of words, in texts from different areas of expertise. The purpose of CWI is to highlight problematic structures that non-native speakers would usually find difficult to understand. Our approach uses zero-shot, one-shot, and few-shot learning techniques, alongside state-of-the-art solutions for Natural Language Processing (NLP) tasks (i.e., Transformers). Our aim is to provide evidence that the proposed models can learn the characteristics of complex words in a multilingual environment by relying on the CWI shared task 2018 dataset available for four different languages (i.e., English, German, Spanish, and also French). Our approach surpasses state-of-the-art cross-lingual results in terms of macro F1-score on English (0.774), German (0.782), and Spanish (0.734) languages, for the zero-shot learning scenario. At the same time, our model also outperforms the state-of-the-art monolingual result for German (0.795 macro F1-score).

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