Campus Only Senior Thesis
Bachelor of Arts
Marina Perez de Mendiola
© 2015 Elmira Tapkanova
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.
Tapkanova, Elmira, "Machine Translation and Text Simplification Evaluation" (2016). Scripps Senior Theses. 790.
This thesis is restricted to the Claremont Colleges current faculty, students, and staff.