Work

Cross-lingual Character-Level Neural Morphological Tagging

Year: 2017

Type: conference-paper

Abstract: Even for common NLP tasks, sufficient supervision is not available in many languages-morphological tagging is no exception.In the work presented here, we explore a transfer learning scheme, whereby we train character-level recurrent neural taggers to predict morphological taggings for high-resource languages and low-resource languages together.Learning joint character representations among multiple related languages successfully enables knowledge transfer from the high-resource languages to the low-resource ones, improving accuracy by up to 30%.

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.18653/v1/d17-1078

Authors: Ryan Cotterell, Georg Heigold

Institutions: Johns Hopkins University, German Research Centre for Artificial Intelligence

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Defense Science and Engineering Graduate, Leibniz-Gemeinschaft

OpenAlex ID: W2753398138