Graduation Year

2016

Document Type

Campus Only Senior Thesis

Degree Name

Bachelor of Arts

Department

Computer Science

Reader 1

David Kauchak

Reader 2

Marina Perez de Mendiola

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Terms of Use for work posted in Scholarship@Claremont.

Rights Information

© 2015 Elmira Tapkanova

Abstract

Machine translation translates a text from one language to another, while text simplification converts a text from its original form to a simpler one, usually in the same language. This survey paper discusses the evaluation (manual and automatic) of both fields, providing an overview of existing metrics along with their strengths and weaknesses. The first chapter takes an in-depth look at machine translation evaluation metrics, namely BLEU, NIST, AMBER, LEPOR, MP4IBM1, TER, MMS, METEOR, TESLA, RTE, and HTER. The second chapter focuses more generally on text simplification, starting with a discussion of the theoretical underpinnings of the field (i.e what ``simple'' means). Then, an overview of automatic evaluation metrics, namely BLEU and Flesch-Kincaid, is given, along with common approaches to text simplification. The paper concludes with a discussion of the future trajectory of both fields.

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