MFAQ: a Multilingual FAQ Dataset
This provides a new dataset for multilingual FAQ retrieval, addressing a gap in resources, though it is incremental as it builds on existing retrieval methods.
The authors tackled the lack of a multilingual FAQ dataset by creating MFAQ, which includes 6M FAQ pairs across 21 languages, and found that a multilingual XLM-RoBERTa model achieved the best results except for English, with lower-resource languages benefiting from cross-lingual learning.
In this paper, we present the first multilingual FAQ dataset publicly available. We collected around 6M FAQ pairs from the web, in 21 different languages. Although this is significantly larger than existing FAQ retrieval datasets, it comes with its own challenges: duplication of content and uneven distribution of topics. We adopt a similar setup as Dense Passage Retrieval (DPR) and test various bi-encoders on this dataset. Our experiments reveal that a multilingual model based on XLM-RoBERTa achieves the best results, except for English. Lower resources languages seem to learn from one another as a multilingual model achieves a higher MRR than language-specific ones. Our qualitative analysis reveals the brittleness of the model on simple word changes. We publicly release our dataset, model and training script.