Abstract
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.- Anthology ID:
- 2020.coling-main.439
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Donia Scott, Nuria Bel, Chengqing Zong
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 5004–5012
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.439
- DOI:
- 10.18653/v1/2020.coling-main.439
- Cite (ACL):
- Matthijs Westera, Jacopo Amidei, and Laia Mayol. 2020. Similarity or deeper understanding? Analyzing the TED-Q dataset of evoked questions. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5004–5012, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- Similarity or deeper understanding? Analyzing the TED-Q dataset of evoked questions (Westera et al., COLING 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.coling-main.439.pdf
- Code
- amore-upf/ted-q_eval
- Data
- BookCorpus, QuAC