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        Enriching training data with syntactic knowledge and the effect on performance of a neural network on natural language processing tasks

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        Publication date
        2021
        Author
        Teerlink, Y.K.
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        Summary
        Compared to neural networks (NN), humans can learn new concepts using only very little data. The ability to learn so efficiently might be due to the use of ab- stractions. To find similarities between human and machine learning this research will analyze if NN benefits from syntactic information during training. We will aim to answer the following question: How does enriching training data with syntactic knowledge affect the performance of a NN on natural language processing tasks? This research examines the results of Long Short Term Memory models (LSTM) trained on two different types of datasets; one without Part of Speech tags (a form of abstract knowledge) and a dataset that is supplemented with POS-tags. The results show that an LSTM trained on a relatively small dataset supplemented with POS- tags outperforms an LSTM trained on a regular dataset. The increase in performance might suggest that neural networks benefit from abstract information, which in turn might show some similarities in the way humans and machines learn.
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        https://studenttheses.uu.nl/handle/20.500.12932/39213
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