Impact of Subword Pooling Strategy on Cross-lingual Event Detection
This work addresses a specific bottleneck in cross-lingual information extraction for NLP researchers, but it is incremental as it focuses on optimizing an existing component rather than introducing a new paradigm.
The paper tackles the problem of subword pooling strategies in zero-shot cross-lingual event detection, showing that the choice of pooling strategy can impact target language performance by up to 16 absolute F1 points, with attention pooling being robust across languages and datasets.
Pre-trained multilingual language models (e.g., mBERT, XLM-RoBERTa) have significantly advanced the state-of-the-art for zero-shot cross-lingual information extraction. These language models ubiquitously rely on word segmentation techniques that break a word into smaller constituent subwords. Therefore, all word labeling tasks (e.g. named entity recognition, event detection, etc.), necessitate a pooling strategy that takes the subword representations as input and outputs a representation for the entire word. Taking the task of cross-lingual event detection as a motivating example, we show that the choice of pooling strategy can have a significant impact on the target language performance. For example, the performance varies by up to 16 absolute $f_{1}$ points depending on the pooling strategy when training in English and testing in Arabic on the ACE task. We carry out our analysis with five different pooling strategies across nine languages in diverse multi-lingual datasets. Across configurations, we find that the canonical strategy of taking just the first subword to represent the entire word is usually sub-optimal. On the other hand, we show that attention pooling is robust to language and dataset variations by being either the best or close to the optimal strategy. For reproducibility, we make our code available at https://github.com/isi-boston/ed-pooling.