Combining Textual and Speech Features in the NLI Task Using State-of-the-Art Machine Learning Techniques

Pavel Ircing, Jan Švec, Zbyněk Zajíc, Barbora Hladká, Martin Holub


Abstract
We summarize the involvement of our CEMI team in the ”NLI Shared Task 2017”, which deals with both textual and speech input data. We submitted the results achieved by using three different system architectures; each of them combines multiple supervised learning models trained on various feature sets. As expected, better results are achieved with the systems that use both the textual data and the spoken responses. Combining the input data of two different modalities led to a rather dramatic improvement in classification performance. Our best performing method is based on a set of feed-forward neural networks whose hidden-layer outputs are combined together using a softmax layer. We achieved a macro-averaged F1 score of 0.9257 on the evaluation (unseen) test set and our team placed first in the main task together with other three teams.
Anthology ID:
W17-5021
Volume:
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venues:
BEA | WS
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
198–209
Language:
URL:
https://www.aclweb.org/anthology/W17-5021
DOI:
10.18653/v1/W17-5021
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-5021.pdf