Multi-lingual Mathematical Word Problem Generation using Long Short Term Memory Networks with Enhanced Input Features

Vijini Liyanage, Surangika Ranathunga


Abstract
A Mathematical Word Problem (MWP) differs from a general textual representation due to the fact that it is comprised of numerical quantities and units, in addition to text. Therefore, MWP generation should be carefully handled. When it comes to multi-lingual MWP generation, language specific morphological and syntactic features become additional constraints. Standard template-based MWP generation techniques are incapable of identifying these language specific constraints, particularly in morphologically rich yet low resource languages such as Sinhala and Tamil. This paper presents the use of a Long Short Term Memory (LSTM) network that is capable of generating elementary level MWPs, while satisfying the aforementioned constraints. Our approach feeds a combination of character embeddings, word embeddings, and Part of Speech (POS) tag embeddings to the LSTM, in which attention is provided for numerical values and units. We trained our model for three languages, English, Sinhala and Tamil using separate MWP datasets. Irrespective of the language and the type of the MWP, our model could generate accurate single sentenced and multi sentenced problems. Accuracy reported in terms of average BLEU score for English, Sinhala and Tamil languages were 22.97%, 24.49% and 20.74%, respectively.
Anthology ID:
2020.lrec-1.579
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
4709–4716
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.579
DOI:
Bibkey:
Cite (ACL):
Vijini Liyanage and Surangika Ranathunga. 2020. Multi-lingual Mathematical Word Problem Generation using Long Short Term Memory Networks with Enhanced Input Features. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 4709–4716, Marseille, France. European Language Resources Association.
Cite (Informal):
Multi-lingual Mathematical Word Problem Generation using Long Short Term Memory Networks with Enhanced Input Features (Liyanage & Ranathunga, LREC 2020)
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PDF:
https://aclanthology.org/2020.lrec-1.579.pdf