Extracting Settings from Multilingual Recipes with Various Sequence Tagging Models: an Experimental Study

In this paper we formulate an information retrieval problem as a sequence tagging task and develop and analyze critically various solutions. The specific context is that of extracting settings (such as cooking times, temperatures and cooking function) from online cooking recipes in multiple languages. We lack annotated data, therefore we start with developing a regex-based automatic tagger for three languages - English, German and French, which has proven to produce good enough annotations for learning. The final goal is to obtain a single extensible multilingual solution, and explore proven cross-lingual capabilities of pretrained multilingual language models to easily accommodate new languages. In the experimental evaluations performed we compare the performance of several monolingual and multilingual solutions, evaluate the impact of specific linguistic features on the performance of the models and assess zeroshot capabilities of the best multilingual solutions. The results indicate that a Conditional Random Field (CRF) layer added on top of Bidirectional Long-Short Term Memory (BiLSTM) models produces the best results in both mono-and multi-lingual settings, reaching Fl-scores of over 96% for all classes, in both learning setups.