@inproceedings{586b6920226a414c884269d3e38016b4,
title = "Enhancing Seq2seq Math Word Problem Solver with Entity Information and Math Knowledge",
abstract = "Devising an automatic Math Word Problem (MWP) solver has emerged as an important task in recent years. Various applications such as online education and intelligent assistants are expecting better MWP solvers to process complex user queries that involve numerical reasoning. Current seq2seq MWP solvers encounter two critical challenges: ordinal indices without semantics and insufficient training data. In this work, we propose Entity Random Indexing to equip indices with semantics and design diverse representations of math expressions to augment training data. Experimental results show that our approach effectively enhances the seq2seq MWP solver, which outperforms strong baselines.",
keywords = "Entity information, Math knowledge, Math word problem, Seq2seq model",
author = "Lei Li and Dongxiang Zhang and Chengyu Wang and Cheqing Jin and Ming Gao and Aoying Zhou",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 23rd International Conference on Web Information Systems Engineering, WISE 2021 ; Conference date: 01-11-2022 Through 03-11-2022",
year = "2022",
doi = "10.1007/978-3-031-20891-1\_26",
language = "英语",
isbn = "9783031208904",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "370--385",
editor = "Richard Chbeir and Helen Huang and Fabrizio Silvestri and Yannis Manolopoulos and Yanchun Zhang and Yanchun Zhang",
booktitle = "Web Information Systems Engineering – WISE 2022 - 23rd International Conference, Proceedings",
address = "德国",
}