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Experimental Evaluation of Math Word Problem Solver

Our contributions:

  • Creation of a new English corpus of arithmetic word problems dealing with {+, -, *, /} and linear variables only. We named this as Dolphin300 which is a subset of publicly available Dolphin18k.
  • Creation of equation templates and normalizing equations in par with Math23K dataset [1].
  • Experimental evaluation of T-RNN and retrieval baselines on Math23K, Dolphin300 and Dolphin1500.

Sample data processing and cleaning:

  • What is the value of five times the sum of twice of three-fourths and nine? ==> What is the value of 5 times the sum of 2 of 3/4 and 9?
  • please help with math! What percentage of 306,800 equals eight thousands? ==> please help with math! What percentage of 306800 equals 8000?
  • help!!!!!!!(please) i cant figure this out!? what is the sum of 4 2/5 and 17 3/7 ? ==> help!!!!!!!(please) i cant figure this out!? what is the sum of 22/5 and 122/7 ?
  • math homework help? question: 15 is 25% of what number? ==> math homework help? question: 15 is 25/100 of what number?

list of folders:

  • Web scraping: it contains the code (OriginalDataExtractor.py) to scrap the math word problems from yahoo answers. A basic data cleaning has also been carried out (CleanVersionExtractor.py) to get the questions in the desired format.
  • Data_Cleaning: it contains the code for the data cleaning of Dolphin DataSet, MWP_DataCleaning.py file has all the rule based and filtering logic for transforming the candidate dolphin datasets to cleaned templates. Inside cleaned_data_examples folder, uncleaned_dolphin_data.csv contains the raw data from dolphin dataset and filtered_cleaned_dolphin_data.json contains the filtered out cleaned template json from the csv.
  • T-RNN and baselines: contain T-RNN code and baseline models, output folder within contains runs of retrieval model (named as pred_.txt) and runs of TRNN (named as run_.txt)

Implementation:

  • Implemented in >=py3.6 environment with pytorch
  • We used part of T-RNN code [1] and added some more implementations for Math23K
  • We used replicate.py and MWP_DataCleaning.ipynb to replicate data and process raw noisy Dolphin18k data.
  • Finally we obtain Dolphin300 and Dolphin1500 after running replicate.py on Dolphin300.
  • Run T-RNN code as : $ python T-RNN/src/main.py (Please see the details in the code to change input files)

T-RNN for Math23K

  • In the template prediction module, we use a pre-trained word embedding with 128 units, a two-layer Bi-LSTM with 256 hidden units as encoder, a two-layer LSTM with 512 hidden units as decoder. As to the optimizer, we use Adam with learning rate set to 1e−3, β1 = 0.9 and β2 = 0.99. In the answer generation module, we use a embedding layer with 100 units, a two-layer Bi-LSTM with 160 hidden units. SGD with learning rate 0.01 and momentum factor 0.9 is used to optimize this module. In both components, the number of epochs, mini-batch size and dropout rate are set 100, 32 and 0.5 respectively.

T-RNN for Dolphin1500 & Dolphin300

  • Template prediction module: 128 units, two-layer Bi-LSTM with 256 hidden units as encoder, a two-layer LSTM with 512 hidden units as decoder. ADAM optimizer with default parameters. Answer generation module - embedding layer with 100 units, a two-layer Bi-LSTM with 160 hidden units, RNN classes = 4.SGD with learning rate 0.01 and momentum factor 0.9 is used to optimize this module. the number of epochs, mini-batch size and dropout rate are set 50, 32 and 0.5 respectively.

References:

[1] Lei Wang, Dongxiang Zhang, Jipeng Zhang, Xing Xu, Lianli Gao, Bingtian Dai, and Heng Tao Shen. Template-based math word problem solvers with recursive neural networks. 2019.

[2] Yan Wang, Xiaojiang Liu, and Shuming Shi. Deep neural solver for math word problems. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 845–854, 2017.