Question Answering over Linked Data (QALD-4)

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

MLQA: Evaluating Cross-lingual Extractive Question Answering (2019, methodology): “Cross-lingual QA Modelling Cross-lingual QA as a discipline has been explored in QA for RDF data for a number of years, such as the QALD-3 and 5 tracks (Cimiano et al., 2013; Unger et al., 2015), with more recent work from Zimina et al. (2018).”
Don't just listen, use your imagination: Leveraging visual common sense for non-visual tasks (2015, neutral): “Today’s artiﬁcially intelligent agents are good at answering factual questions about our world [9, 15, 41].”
Explanations for CommonsenseQA: New Dataset and Models (2021, neutral): “Bulk of the recent literature on automated QA is focused on either (i) proposing a new kind of dataset (Unger et al., 2014; Rajpurkar et al., 2016; Ling et al., 2017; Joshi et al., 2017; Trivedi et al., 2017; Welbl et al., 2017; Yang et al., 2018; Kwiatkowski et al., 2019; Talmor et al., 2019; Miao…”
More Accurate Question Answering on Freebase (2015, unknown): “See [22] for the latest report.”
TempQuestions: A Benchmark for Temporal Question Answering (2018, background): “QALD [29, 31] is a series of evaluation campaigns on QA over linked data, and releases datasets every year to evaluate KB-QA systems.”
Core techniques of question answering systems over knowledge bases: a survey (2017, unknown): “We included the QALD tasks querying DBpedia and QALD-4 task 2 which addresses the problem of QA over interlinked KBs.”