0

MFAQ: a Multilingual FAQ Dataset

A multilingual FAQ dataset and testing reveal that XLM-RoBERTa outperforms language-specific models except for English, showing collaboration across languages.

Year
2021
Venue
EMNLP (MRQA) 2021 11
Authors
4
Hosting
Abstract onlyARXIV-DEFAULT

Cite

Notes

Only stored in your browser.

Attribution

Abstract & full text
arxiv.org/abs/2109.12870v2ARXIV-DEFAULT
TL;DR
Semantic Scholar
Attribution policy →

Abstract

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.

Authors

4