BERT-Assisted Semantic Annotation Correction for Emotion-Related Questions
Abstract
Annotated data have traditionally been used to provide the input for training a supervised machine learning (ML) model. However, current pre-trained ML models for natural language processing (NLP) contain embedded linguistic information that can be used to inform the annotation process. We use the BERT neural language model to feed information back into an annotation task that involves semantic labelling of dialog behavior in a question-asking game called Emotion Twenty Questions (EMO20Q). First we describe the background of BERT, the EMO20Q data, and assisted annotation tasks. Then we describe the methods for fine-tuning BERT for the purpose of checking the annotated labels. To do this, we use the paraphrase task as a way to check that all utterances with the same annotation label are classified as paraphrases of each other. We show this method to be an effective way to assess and revise annotations of textual user data with complex, utterance-level semantic labels.
Cite
@article{arxiv.2204.00916,
title = {BERT-Assisted Semantic Annotation Correction for Emotion-Related Questions},
author = {Abe Kazemzadeh},
journal= {arXiv preprint arXiv:2204.00916},
year = {2022}
}
Comments
Presented at 6th International Workshop on Annotation of useR Data for UbiquitOUs Systems (ARDUOUS) Workshop of IEEE Pervasive Computing (Percom) conference, Mar. 25, 2022. To replicate this experiment please see https://colab.research.google.com/drive/1sXqTT2DoJ1hFBR02iM8W4GKZe5OxCYJy and https://github.com/abecode/emo20q