scholarly journals Unsupervised Cross-Lingual Part-of-Speech Tagging for Truly Low-Resource Scenarios

2020 ◽  
Author(s):  
Ramy Eskander ◽  
Smaranda Muresan ◽  
Michael Collins
Keyword(s):  
Low Resource ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Cross Lingual ◽  
Speech Tagging

scholarly journals Multilingual Projection for Parsing Truly Low-Resource Languages

2016 ◽  
Vol 4 ◽  
pp. 301-312 ◽  
Author(s):  
Željko Agić ◽  
Anders Johannsen ◽  
Barbara Plank ◽  
Héctor Martínez Alonso ◽  
Natalie Schluter ◽  
...  
Keyword(s):  
Empirical Evaluation ◽  
Upper Bounds ◽  
Low Resource ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Novel Approach ◽  
Cross Lingual ◽  
Test Languages ◽  
Speech Tagging ◽  
Parallel Texts

We propose a novel approach to cross-lingual part-of-speech tagging and dependency parsing for truly low-resource languages. Our annotation projection-based approach yields tagging and parsing models for over 100 languages. All that is needed are freely available parallel texts, and taggers and parsers for resource-rich languages. The empirical evaluation across 30 test languages shows that our method consistently provides top-level accuracies, close to established upper bounds, and outperforms several competitive baselines.

scholarly journals Sequence Mixup for Zero-Shot Cross-Lingual Part-Of-Speech Tagging

2021 ◽  
Author(s):  
Megh Thakkar ◽  
Vishwa Shah ◽  
Ramit Sawhney ◽  
Debdoot Mukherjee
Keyword(s):  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Cross Lingual ◽  
Speech Tagging

scholarly journals Cross-Lingual Part-of-Speech Tagging through Ambiguous Learning

2014 ◽  
Author(s):  
Guillaume Wisniewski ◽  
Nicolas Pécheux ◽  
Souhir Gahbiche-Braham ◽  
François Yvon
Keyword(s):  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Cross Lingual ◽  
Speech Tagging

scholarly journals To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLP

2021 ◽  
pp. 1-38
Author(s):  
Gözde Gül Şahin
Keyword(s):  
Language Models ◽  
Semantic Role ◽  
Semantic Role Labeling ◽  
Dependency Parsing ◽  
Low Resource ◽  
Part Of Speech Tagging ◽  
High Resource ◽  
Part Of Speech ◽  
Augmentation Techniques ◽  
Speech Tagging

Abstract Data-hungry deep neural networks have established themselves as the defacto standard for many NLP tasks including the traditional sequence tagging ones. Despite their state-of-the-art performance on high-resource languages, they still fall behind of their statistical counter-parts in low-resource scenarios. One methodology to counter attack this problem is text augmentation, i.e., generating new synthetic training data points from existing data. Although NLP has recently witnessed a load of textual augmentation techniques, the field still lacks a systematic performance analysis on a diverse set of languages and sequence tagging tasks. To fill this gap, we investigate three categories of text augmentation methodologies which perform changes on the syntax (e.g., cropping sub-sentences), token (e.g., random word insertion) and character (e.g., character swapping) levels.We systematically compare the methods on part-of-speech tagging, dependency parsing and semantic role labeling for a diverse set of language families using various models including the architectures that rely on pretrained multilingual contextualized language models such as mBERT. Augmentation most significantly improves dependency parsing, followed by part-of-speech tagging and semantic role labeling. We find the experimented techniques to be effective on morphologically rich languages in general rather than analytic languages such as Vietnamese. Our results suggest that the augmentation techniques can further improve over strong baselines based on mBERT, especially for dependency parsing. We identify the character-level methods as the most consistent performers, while synonym replacement and syntactic augmenters provide inconsistent improvements. Finally, we discuss that the results most heavily depend on the task, language pair (e.g., syntactic-level techniques mostly benefit higher-level tasks and morphologically richer languages), and the model type (e.g., token-level augmentation provide significant improvements for BPE, while character-level ones give generally higher scores for char and mBERT based models).

scholarly journals Token and Type Constraints for Cross-Lingual Part-of-Speech Tagging

2013 ◽  
Vol 1 ◽  
pp. 1-12 ◽  
Author(s):  
Oscar Täckström ◽  
Dipanjan Das ◽  
Slav Petrov ◽  
Ryan McDonald ◽  
Joakim Nivre
Keyword(s):  
Conditional Random Field ◽  
Target Language ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
European Languages ◽  
Partially Observed ◽  
Resource Poor ◽  
Conditional Random Field Model ◽  
Cross Lingual ◽  
Speech Tagging

We consider the construction of part-of-speech taggers for resource-poor languages. Recently, manually constructed tag dictionaries from Wiktionary and dictionaries projected via bitext have been used as type constraints to overcome the scarcity of annotated data in this setting. In this paper, we show that additional token constraints can be projected from a resource-rich source language to a resource-poor target language via word-aligned bitext. We present several models to this end; in particular a partially observed conditional random field model, where coupled token and type constraints provide a partial signal for training. Averaged across eight previously studied Indo-European languages, our model achieves a 25% relative error reduction over the prior state of the art. We further present successful results on seven additional languages from different families, empirically demonstrating the applicability of coupled token and type constraints across a diverse set of languages.

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