Enhancing deep neural networks with morphological information

TL;DR

This paper investigates how adding morphological features to LSTM and BERT models affects NLP tasks in less-resourced, morphologically rich languages, revealing mixed results depending on feature quality and task.

Contribution

It provides a comparative analysis of the impact of morphological features on neural models across multiple languages and tasks, highlighting conditions for effectiveness.

Findings

Morphological features improve LSTM performance on NER and DP.

High-quality features enhance BERT's DP performance, but predicted features do not.

Effects vary by task, feature quality, and model type.

Abstract

Deep learning approaches are superior in NLP due to their ability to extract informative features and patterns from languages. The two most successful neural architectures are LSTM and transformers, used in large pretrained language models such as BERT. While cross-lingual approaches are on the rise, most current NLP techniques are designed and applied to English, and less-resourced languages are lagging behind. In morphologically rich languages, information is conveyed through morphology, e.g., through affixes modifying stems of words. Existing neural approaches do not explicitly use the information on word morphology. We analyse the effect of adding morphological features to LSTM and BERT models. As a testbed, we use three tasks available in many less-resourced languages: named entity recognition (NER), dependency parsing (DP), and comment filtering (CF). We construct baselines involving LSTM and BERT models, which we adjust by adding additional input in the form of part of speech (POS) tags and universal features. We compare models across several languages from different language families. Our results suggest that adding morphological features has mixed effects depending on the quality of features and the task. The features improve the performance of LSTM-based models on the NER and DP tasks, while they do not benefit the performance on the CF task. For BERT-based models, the morphological features only improve the performance on DP when they are of high quality while not showing practical improvement when they are predicted. Even for high-quality features, the improvements are less pronounced in language-specific BERT variants compared to massively multilingual BERT models. As in NER and CF datasets manually checked features are not available, we only experiment with predicted features and find that they do not cause any practical improvement in performance.