We gratefully acknowledge support from
the Simons Foundation and member institutions.
Full-text links:


Current browse context:

new | recent | 1902

Change to browse by:

References & Citations


BibSonomy logo Mendeley logo Reddit logo ScienceWISE logo

Computer Science > Computation and Language

Title:How Large a Vocabulary Does Text Classification Need? A Variational Approach to Vocabulary Selection

Abstract: With the rapid development in deep learning, deep neural networks have been widely adopted in many real-life natural language applications. Under deep neural networks, a pre-defined vocabulary is required to vectorize text inputs. The canonical approach to select pre-defined vocabulary is based on the word frequency, where a threshold is selected to cut off the long tail distribution. However, we observed that such simple approach could easily lead to under-sized vocabulary or over-sized vocabulary issues. Therefore, we are interested in understanding how the end-task classification accuracy is related to the vocabulary size and what is the minimum required vocabulary size to achieve a specific performance. In this paper, we provide a more sophisticated variational vocabulary dropout (VVD) based on variational dropout to perform vocabulary selection, which can intelligently select the subset of the vocabulary to achieve the required performance. To evaluate different algorithms on the newly proposed vocabulary selection problem, we propose two new metrics: Area Under Accuracy-Vocab Curve and Vocab Size under X\% Accuracy Drop. Through extensive experiments on various NLP classification tasks, our variational framework is shown to significantly outperform the frequency-based and other selection baselines on these metrics.
Comments: Accepted to NAACL 2019, 11 pages, 7 figures, 3 tables
Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:1902.10339 [cs.CL]
  (or arXiv:1902.10339v4 [cs.CL] for this version)

Submission history

From: Wenhu Chen [view email]
[v1] Wed, 27 Feb 2019 05:57:13 UTC (1,310 KB)
[v2] Mon, 11 Mar 2019 19:42:22 UTC (1,317 KB)
[v3] Fri, 15 Mar 2019 19:59:53 UTC (1,319 KB)
[v4] Wed, 3 Apr 2019 20:07:26 UTC (1,323 KB)