Subword Pooling Makes a Difference

TL;DR

This paper investigates how different subword pooling strategies impact the performance of multilingual models on linguistic tasks, revealing that attention-based pooling and small LSTMs outperform common methods across diverse languages.

Contribution

It systematically compares subword pooling methods in multilingual models, demonstrating the effectiveness of attention and LSTM pooling over traditional strategies for various NLP tasks.

Findings

Attention pooling improves morphological task performance.

LSTM pooling outperforms simple strategies for POS tagging.

mBERT outperforms XLM-RoBERTa across all languages.

Abstract

Contextual word-representations became a standard in modern natural language processing systems. These models use subword tokenization to handle large vocabularies and unknown words. Word-level usage of such systems requires a way of pooling multiple subwords that correspond to a single word. In this paper we investigate how the choice of subword pooling affects the downstream performance on three tasks: morphological probing, POS tagging and NER, in 9 typologically diverse languages. We compare these in two massively multilingual models, mBERT and XLM-RoBERTa. For morphological tasks, the widely used `choose the first subword' is the worst strategy and the best results are obtained by using attention over the subwords. For POS tagging both of these strategies perform poorly and the best choice is to use a small LSTM over the subwords. The same strategy works best for NER and we show that mBERT is better than XLM-RoBERTa in all 9 languages. We publicly release all code, data and the full result tables at \url{https://github.com/juditacs/subword-choice}.