Open AccessIDENTIFYING SPEECH ACTS IN E‐MAILS: TOWARD AUTOMATED SCORING OF THE TOEIC ® E‐MAIL TASK
Author(s) -
Felice Rachele De,
Deane Paul
Publication year - 2012
Publication title -
ets research report series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.235
H-Index - 5
ISSN - 2330-8516
DOI - 10.1002/j.2333-8504.2012.tb02298.x
Subject(s) - toeic , rubric , computer science , task (project management) , focus (optics) , identification (biology) , natural language processing , speech recognition , artificial intelligence , psychology , reading (process) , linguistics , mathematics education , philosophy , physics , botany , optics , biology , management , economics
This study proposes an approach to automatically score the TOEIC ® Writing e‐mail task. We focus on one component of the scoring rubric, which notes whether the test‐takers have used particular speech acts such as requests, orders, or commitments. We developed a computational model for automated speech act identification and tested it on a corpus of TOEIC responses, achieving up to 79.28% accuracy. This model represents a positive first step toward the development of a more comprehensive scoring model. We also created a corpus of speech act‐annotated native English workplace e‐mails. Comparisons between these and the TOEIC data allow us to assess whether English learners are approximating native models and whether differences between native and non‐native data can have negative consequences in the global workplace.