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
Terms of Use & License Information
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.
Recommended Citation
Tapkanova, Elmira, "Machine Translation and Text Simplification Evaluation" (2016). Scripps Senior Theses. 790.
https://scholarship.claremont.edu/scripps_theses/790
This thesis is restricted to the Claremont Colleges current faculty, students, and staff.
