SubmissionNumber#=%=#124
FinalPaperTitle#=%=#TueSents at SemEval-2024 Task 8: Predicting the Shift from Human Authorship to Machine-generated Output in a Mixed Text
ShortPaperTitle#=%=#
NumberOfPages#=%=#4
CopyrightSigned#=%=#Valentin Pickard
JobTitle#==#
Organization#==#Seminar für Sprachwissenschaft, Eberhard Karls Universität Tübingen, Germany
Abstract#==#This paper describes our approach and results
for the SemEval 2024 task of identifying the
token index in a mixed text where a switch
from human authorship to machine-generated
text occurs. We explore two BiLSTMs, one
over sentence feature vectors to predict the
index of the sentence containing such a change
and another over character embeddings of the
text. As sentence features, we compute token
count, mean token length, standard deviation
of token length, counts for punctuation and
space characters, various readability scores,
word frequency class and word part-of-speech
class counts for each sentence. class counts.
The evaluation is performed on mean absolute
error (MAE) between predicted and actual
boundary token index. While our competition
results were notably below the baseline, there
may still be useful aspects to our approach.
Author{1}{Firstname}#=%=#Valentin
Author{1}{Lastname}#=%=#Pickard
Author{1}{Username}#=%=#pival
Author{1}{Email}#=%=#pival89@gmail.com
Author{1}{Affiliation}#=%=#University of Tuebingen
Author{2}{Firstname}#=%=#Hoa
Author{2}{Lastname}#=%=#Do
Author{2}{Username}#=%=#hoado
Author{2}{Email}#=%=#hoa.do@student.uni-tuebingen.de
Author{2}{Affiliation}#=%=#University of Tübingen

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