International Committee on Computational Linguistics
We take a close look at a recent dataset of TED-talks annotated with the questions they implicitly evoke, TED-Q (Westera et al., 2020). We test to what extent the relation between a discourse and the questions it evokes is merely one of similarity or association, as opposed to deeper semantic/pragmatic interpretation. We do so by turning the TED-Q dataset into a binary classification task, constructing an analogous task from explicit questions we extract from the BookCorpus (Zhu et al., 2015), and fitting a BERT-based classifier alongside models based on different notions of similarity. The BERT-based classifier, achieving close to human performance, outperforms all similarity-based models, suggesting that there is more to identifying true evoked questions than plain similarity.
Westera, Matthijs; Amidei, Jacopo; Mayol, Laia. Similarity or deeper understanding? Analyzing the TED-Q dataset of evoked questions. In: Scott D, Bel N, Zong C. Proceedings of the 28th International Conference on Computational Linguistics (COLING'2020). 1 ed. International Committee on Computational Linguistics; 2020. p. 5004-5012.