%0 Conference Proceedings %T Search-based Neural Structured Learning for Sequential Question Answering %A Iyyer, Mohit %A Yih, Wen-tau %A Chang, Ming-Wei %Y Barzilay, Regina %Y Kan, Min-Yen %S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) %D 2017 %8 July %I Association for Computational Linguistics %C Vancouver, Canada %F iyyer-etal-2017-search %X Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions. We collect a dataset of 6,066 question sequences that inquire about semi-structured tables from Wikipedia, with 17,553 question-answer pairs in total. To solve this sequential question answering task, we propose a novel dynamic neural semantic parsing framework trained using a weakly supervised reward-guided search. Our model effectively leverages the sequential context to outperform state-of-the-art QA systems that are designed to answer highly complex questions. %R 10.18653/v1/P17-1167 %U https://aclanthology.org/P17-1167 %U https://doi.org/10.18653/v1/P17-1167 %P 1821-1831