Inter-sentence features and thresholded minimum error rate training: NAIST at CLEF 2013 QA4MRE

Philip Arthur, Graham Neubig, Sakriani Sakti, Tomoki Toda, Satoshi Nakamura

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)

Abstract

This paper describes the Nara Institute of Science and Technology's system for the main task of CLEF 2013 QA4MRE. The core of the system is a log linear scoring model that couples both intra and intersentence features. Each of the features receives an input of a candidate answer, question, and document, and uses these to assign a score according to some criterion. We use minimum error rate training (MERT) to train the weights of the model and also propose a novel method for MERT with the addition of a threshold that defines the certainty with which we must answer questions. The system received a score of 28% c@1 on main questions and 33% c@1 when considering auxiliary questions on the CLEF 2013 evaluation.

Original languageEnglish
Title of host publication2013 Cross Language Evaluation Forum Conference
Volume1179
Publication statusPublished - 2013
Externally publishedYes
EventCross Language Evaluation Forum Conference 2013 - Valencia, Spain
Duration: 23 Sept 201326 Sept 2013
Conference number: 4th
http://clef2013.clef-initiative.eu/index.php

Conference

ConferenceCross Language Evaluation Forum Conference 2013
Abbreviated titleCLEF 2013
Country/TerritorySpain
CityValencia
Period23/09/1326/09/13
Internet address

Keywords

  • Discriminative learning
  • Inter-sentence features
  • Linear feature model
  • Machine reading
  • Minimum error rate training
  • Question answering

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