Most of the published approaches and resources for hate speech detection are tailored for the English language. In consequence, cross-lingual and cross-cultural perspectives lack some essential resources. The lack of diversity of the datasets in Spanish is notable. Variations throughout Spanish-speaking countries make existing datasets not enough to encompass the task in the different Spanish variants. We annotated 9834 tweets from Chile to enrich the existing Spanish resources with different words and new targets of hate that have not been considered in previous studies. We conducted several cross-dataset evaluation experiments of the models published in the literature using our Chilean dataset and two others in English and Spanish. We propose a comparative framework for quickly conducting comparative experiments using different previously published models. In addition, we set up a Codalab competition for further comparison of new models in a standard scenario, that is, data partitions and evaluation metrics. All resources can be accessed trough a centralized repository for researchers to get a complete picture of the progress on the multilingual hate speech and offensive language detection task.

The Entity Linking (EL) task identifies entity mentions in a text corpus and associates them with an unambiguous identifier in a Knowledge Base. While much work has been done on the topic, we first present the results of a survey that reveal a lack of consensus in the community regarding what forms of mentions in a text and what forms of links the EL task should consider. We argue that no one definition of the Entity Linking task fits all, and rather propose a fine-grained categorization of different types of entity mentions and links. We then re-annotate three EL benchmark datasets – ACE2004, KORE50, and VoxEL – with respect to these categories. We propose a fuzzy recall metric to address the lack of consensus and conclude with fine-grained evaluation results comparing a selection of online EL systems.