2024 COLING COLING 2024

Two Sequence Labeling Approaches to Sentence Segmentation and Punctuation Prediction for Classic Chinese Texts

Abstract

AbstractThis paper describes our system for the EvaHan2024 shared task. We design and experiment with two sequence labeling approaches, i.e., one-stage and two-stage approaches. The one-stage approach directly predicts a label for each character, and the label may contain multiple punctuation marks. The two-stage approach divides punctuation marks into two classes, i.e., pause and non-pause, and separately handles them via two sequence labeling processes. The labels contain at most one punctuation marks. We use pre-trained SikuRoBERTa as a key component of the encoder and employ a conditional random field (CRF) layer on the top. According to the evaluation metrics adopted by the organizers, the two-stage approach is superior to the one-stage approach, and our system achieves the second place among all participant systems.

๐ŸŒ‰ Interdisciplinary Bridge โ€” Machine Learning and Natural Language Processing
๐Ÿ Cross-Pollinator โ€” Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio