In-depth Question classification using Convolutional Neural Networks

Dachapally, Prudhvi Raj; Srikanth, Ramanam,

doi:10.48550/arxiv.1804.00968

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“…In particular, different solutions regarding the CNN architecture have been tested, and, according to literature advices, the best settings have been searched in the proper ranges, in order to maximize the classification power for the particular case of Italian questions dataset. In [31], an extended CNN architecture is proposed, able to first classify a question into a broader category, and, successively, based on the prior knowledge, assign to it a more specific category. This solution was tested on an English questions dataset with pre-trained word embeddings, showing results on par or improved with respect to other classical methods.…”

Section: Related Workmentioning

confidence: 99%

Best Practices of Convolutional Neural Networks for Question Classification

et al. 2020

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Question Classification (QC) is of primary importance in question answering systems, since it enables extraction of the correct answer type. State-of-the-art solutions for short text classification obtained remarkable results by Convolutional Neural Networks (CNNs). However, implementing such models requires choices, usually based on subjective experience, or on rare works comparing different settings for general text classification, while peculiar solutions should be individuated for QC task, depending on language and on dataset size. Therefore, this work aims at suggesting best practices for QC using CNNs. Different datasets were employed: (i) A multilingual set of labelled questions to evaluate the dependence of optimal settings on language; (ii) a large, widely used dataset for validation and comparison. Numerous experiments were executed, to perform a multivariate analysis, for evaluating statistical significance and influence on QC performance of all the factors (regarding text representation, architectural characteristics, and learning hyperparameters) and some of their interactions, and for finding the most appropriate strategies for QC. Results show the influence of CNN settings on performance. Optimal settings were found depending on language. Tests on different data validated the optimization performed, and confirmed the transferability of the best settings. Comparisons to configurations suggested by previous works highlight the best classification accuracy by those optimized here. These findings can suggest the best choices to configure a CNN for QC.

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Section: Related Workmentioning

confidence: 99%