@inproceedings{edf919d180af42fe908b68e0c7e2de82,

title = "Math Multiple Choice Question Solving and Distractor Generation with Attentional GRU Networks",

abstract = "We investigate encoder-decoder GRU networks with attention mechanism for solving a diverse array of elementary math problems with mathematical symbolic structures. We quantitatively measure performances of recurrent models on a given question type using a test set of unseen problems with a binary scoring and partial credit system. From our findings, we propose the use of encoder-decoder recurrent neural networks for the generation of mathematical multiple-choice question distractors. We introduce a computationally inexpensive decoding schema called character offsetting, which qualitatively and quantitatively shows promise for doing so for several question types. Character offsetting involves freezing the hidden state and top k probabilities of a decoder{\textquoteright}s initial probability outputs given the input of an encoder, then performing k basic greedy decodings given each of the frozen outputs as the initialization for decoded sequence.",

author = "Neisarg Dave and Riley Bakes and Barton Pursel and Giles, {C. Lee}",

note = "Publisher Copyright: {\textcopyright} EDM 2021.All rights reserved.; 14th International Conference on Educational Data Mining, EDM 2023 ; Conference date: 29-06-2021 Through 02-07-2021",

year = "2021",

language = "English (US)",

series = "Proceedings of the 14th International Conference on Educational Data Mining, EDM 2021",

publisher = "International Educational Data Mining Society",

pages = "422--430",

editor = "I-Han Hsiao and Shaghayegh Sahebi and Francois Bouchet and Jill-Jenn Vie",

booktitle = "Proceedings of the 14th International Conference on Educational Data Mining, EDM 2021",

}