Integrating Question Classification and Deep Learning for improved Answer Selection

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

Colleges, School and Institutes

Abstract

We present a system for Answer Selection that integrates fine-grained Question Classification with a Deep Learning model designed for Answer Selection. We detail the necessary changes to the Question Classification taxonomy and system, the creation of a new Entity Identification system and methods of highlighting entities to achieve this objective. Our experiments show that Question Classes are a strong signal to Deep Learning models for Answer Selection, and enable us to outperform the current state of the art in all variations of our experiments except one. In the best configuration, our MRR and MAP scores outperform the current state of the art by between 3 and 5 points on both versions of the TREC Answer Selection test set, a standard dataset for this task.

Details

Original languageEnglish
Title of host publicationProceedings of the 27th International Conference on Computational Linguistics (COLING 2018)
Publication statusPublished - 21 Aug 2018
EventThe 27th International Conference on Computational Linguistics - Santa Fe Community Convention Center , Santa Fe, New-Mexico, United States
Duration: 20 Aug 201826 Aug 2018

Conference

ConferenceThe 27th International Conference on Computational Linguistics
Abbreviated titleCOLING 2018
CountryUnited States
CitySanta Fe, New-Mexico
Period20/08/1826/08/18