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    <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%.</abstract>
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%0 Conference Proceedings
%T Beyond Sentential Semantic Parsing: Tackling the Math SAT with a Cascade of Tree Transducers
%A Hopkins, Mark
%A Petrescu-Prahova, Cristian
%A Levin, Roie
%A Le Bras, Ronan
%A Herrasti, Alvaro
%A Joshi, Vidur
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F hopkins-etal-2017-beyond
%X 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%.
%R 10.18653/v1/D17-1083
%U https://aclanthology.org/D17-1083
%U https://doi.org/10.18653/v1/D17-1083
%P 795-804

Markdown (Informal)

[Beyond Sentential Semantic Parsing: Tackling the Math SAT with a Cascade of Tree Transducers](https://aclanthology.org/D17-1083) (Hopkins et al., EMNLP 2017)

• Beyond Sentential Semantic Parsing: Tackling the Math SAT with a Cascade of Tree Transducers (Hopkins et al., EMNLP 2017)

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.