PointWise HSIC: A linear-time kernelized co-occurrence norm for sparse linguistic expressions

Sho Yokoi, Sosuke Kobayashi, Kenji Fukumizu, Jun Suzuki, Kentaro Inui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a new kernel-based co-occurrence measure that can be applied to sparse linguistic expressions (e.g., sentences) with a very short learning time, as an alternative to pointwise mutual information (PMI). As well as deriving PMI from mutual information, we derive this new measure from the Hilbert-Schmidt independence criterion (HSIC); thus, we call the new measure the pointwise HSIC (PHSIC). PHSIC can be interpreted as a smoothed variant of PMI that allows various similarity metrics (e.g., sentence embeddings) to be plugged in as kernels. Moreover, PHSIC can be estimated by simple and fast (linear in the size of the data) matrix calculations regardless of whether we use linear or nonlinear kernels. Empirically, in a dialogue response selection task, PHSIC is learned thousands of times faster than an RNN-based PMI while outperforming PMI in accuracy. In addition, we also demonstrate that PHSIC is beneficial as a criterion of a data selection task for machine translation owing to its ability to give high (low) scores to a consistent (inconsistent) pair with other pairs.

Original languageEnglish
Title of host publicationProceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018
EditorsEllen Riloff, David Chiang, Julia Hockenmaier, Jun'ichi Tsujii
PublisherAssociation for Computational Linguistics
Pages1763-1775
Number of pages13
ISBN (Electronic)9781948087841
Publication statusPublished - 2020 Jan 1
Event2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 - Brussels, Belgium
Duration: 2018 Oct 312018 Nov 4

Publication series

NameProceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018

Conference

Conference2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018
CountryBelgium
CityBrussels
Period18/10/3118/11/4

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

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