Relation extraction represents a fundamental component in the process of creating knowledge graphs, among other applications. Large language models (LLMs) have been adopted as a promising tool for relation extraction, both in supervised and in-context learning settings. However, in this work we show that their performance still lags behind much smaller architectures when the linguistic graph underlying a text has great complexity. To demonstrate this, we evaluate four LLMs against a graph-based parser on six relation extraction datasets with sentence graphs of varying sizes and complexities. Our results show that the graph-based parser increasingly outperforms the LLMs, as the number of relations in the input documents increases. This makes the much lighter graph-based parser a superior choice in the presence of complex linguistic graphs.

Most work on named entity recognition (NER) focuses solely on English. Through the use of training data augmentation via machine translation (MT), multilingual NER can become a powerful tool for information extraction in multilingual contexts. In this paper, we augment NER data from culinary recipe ingredient lists, by means of MT and word alignment (WA), following two approaches: (i) translating each entity separately, while taking into account the full context of the list and (ii) translating the whole list of ingredients and then aligning entities using three types of WA models: Giza++, Fast Align, and BERT, fine-tuned using a novel entity-shuffling approach. We depart from English data and produce Italian versions via MT, span-annotated with the entities projected from English. Then, we use the data produced by the two approaches to train mono- and multilingual NER BERT models. We test the performance of the WA and NER models on an annotated dataset of ingredient lists, partially out-of-domain compared to the training data. The results show that shuffling entities leads to better BERT aligner models. The higher quality NER data created by these models enables NER models to achieve better results, with multilingual models reaching performances equal to or greater than their monolingual counterparts.

Spotting hate speech in social media posts is crucial to increase the civility of the Web and has been thoroughly explored in the NLP community. For the first time, we introduce a multilingual corpus for the analysis and identification of hate speech in the domain of inceldom, built from incel Web forums in English and Italian, including expert annotation at the post level for two kinds of hate speech: misogyny and racism. This resource paves the way for the development of mono- and cross-lingual models for (a) the identification of hateful (misogynous and racist) posts and (b) the forecasting of the amount of hateful responses that a post is likely to trigger. Our experiments aim at improving the performance of Transformer-based models using masked language modeling pre-training and dataset merging. The results show that these strategies boost the models’ performance in all settings (binary classification, multi-label classification and forecasting), especially in the cross-lingual scenarios.