Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence
Kelvin Lo, Yuan Jin, Weicong Tan, Ming Liu, Lan Du, Wray Buntine
Abstract
This paper proposes a transformer over transformer framework, called Transformerˆ2, to perform neural text segmentation. It consists of two components: bottom-level sentence encoders using pre-trained transformers, and an upper-level transformer-based segmentation model based on the sentence embeddings. The bottom-level component transfers the pre-trained knowledge learnt from large external corpora under both single and pair-wise supervised NLP tasks to model the sentence embeddings for the documents. Given the sentence embeddings, the upper-level transformer is trained to recover the segmentation boundaries as well as the topic labels of each sentence. Equipped with a multi-task loss and the pre-trained knowledge, Transformerˆ2 can better capture the semantic coherence within the same segments. Our experiments show that (1) Transformerˆ2$manages to surpass state-of-the-art text segmentation models in terms of a commonly-used semantic coherence measure; (2) in most cases, both single and pair-wise pre-trained knowledge contribute to the model performance; (3) bottom-level sentence encoders pre-trained on specific languages yield better performance than those pre-trained on specific domains.- Anthology ID:
- 2021.findings-emnlp.283
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3334–3340
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.283
- DOI:
- 10.18653/v1/2021.findings-emnlp.283
- Cite (ACL):
- Kelvin Lo, Yuan Jin, Weicong Tan, Ming Liu, Lan Du, and Wray Buntine. 2021. Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3334–3340, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence (Lo et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2021.findings-emnlp.283.pdf
- Data
- WikiSection