Beyond Sentential Semantic Parsing: Tackling the Math SAT with a Cascade of Tree Transducers
Mark Hopkins, Cristian Petrescu-Prahova, Roie Levin, Ronan Le Bras, Alvaro Herrasti, Vidur Joshi
Abstract
We present an approach for answering questions that span multiple sentences and exhibit sophisticated cross-sentence anaphoric phenomena, evaluating on a rich source of such questions – the math portion of the Scholastic Aptitude Test (SAT). By using a tree transducer cascade as its basic architecture, our system propagates uncertainty from multiple sources (e.g. coreference resolution or verb interpretation) until it can be confidently resolved. Experiments show the first-ever results 43% recall and 91% precision) on SAT algebra word problems. We also apply our system to the public Dolphin algebra question set, and improve the state-of-the-art F1-score from 73.9% to 77.0%.- Anthology ID:
- D17-1083
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 795–804
- Language:
- URL:
- https://aclanthology.org/D17-1083
- DOI:
- 10.18653/v1/D17-1083
- Cite (ACL):
- Mark Hopkins, Cristian Petrescu-Prahova, Roie Levin, Ronan Le Bras, Alvaro Herrasti, and Vidur Joshi. 2017. Beyond Sentential Semantic Parsing: Tackling the Math SAT with a Cascade of Tree Transducers. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 795–804, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Beyond Sentential Semantic Parsing: Tackling the Math SAT with a Cascade of Tree Transducers (Hopkins et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/D17-1083.pdf
- Data
- ALG514