Vinayak Athavale
2019
Predicting Algorithm Classes for Programming Word Problems
Vinayak Athavale

Aayush Naik

Rajas Vanjape

Manish Shrivastava
Proceedings of the 5th Workshop on Noisy Usergenerated Text (WNUT 2019)
We introduce the task of algorithm class prediction for programming word problems. A programming word problem is a problem written in natural language, which can be solved using an algorithm or a program. We define classes of various programming word problems which correspond to the class of algorithms required to solve the problem. We present four new datasets for this task, two multiclass datasets with 550 and 1159 problems each and two multilabel datasets having 3737 and 3960 problems each. We pose the problem as a text classification problem and train neural network and nonneural network based models on this task. Our best performing classifier gets an accuracy of 62.7 percent for the multiclass case on the five class classification dataset, Codeforces Multiclass5 (CFMC5). We also do some humanlevel analysis and compare human performance with that of our text classification models. Our best classifier has an accuracy only 9 percent lower than that of a human on this task. To the best of our knowledge, these are the first reported results on such a task. We make our code and datasets publicly available.
2017
Deep Neural Network based system for solving Arithmetic Word problems
Purvanshi Mehta

Pruthwik Mishra

Vinayak Athavale

Manish Shrivastava

Dipti Sharma
Proceedings of the IJCNLP 2017, System Demonstrations
This paper presents DILTON a system which solves simple arithmetic word problems. DILTON uses a Deep Neural based model to solve math word problems. DILTON divides the question into two parts  worldstate and query. The worldstate and the query are processed separately in two different networks and finally, the networks are merged to predict the final operation. We report the first deep learning approach for the prediction of operation between two numbers. DILTON learns to predict operations with 88.81% accuracy in a corpus of primary school questions.
2016
Towards Deep Learning in Hindi NER: An approach to tackle the Labelled Data Sparsity
Vinayak Athavale

Shreenivas Bharadwaj

Monik Pamecha

Ameya Prabhu

Manish Shrivastava
Proceedings of the 13th International Conference on Natural Language Processing
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Coauthors
 Manish Shrivastava 3
 Shreenivas Bharadwaj 1
 Monik Pamecha 1
 Ameya Prabhu 1
 Purvanshi Mehta 1
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