Abstract
With the tremendous success of deep learning models on computer vision tasks, there are various emerging works on the Natural Language Processing (NLP) task of Text Classification using parametric models. However, it constrains the expressability limit of the function and demands enormous empirical efforts to come up with a robust model architecture. Also, the huge parameters involved in the model causes over-fitting when dealing with small datasets. Deep Gaussian Processes (DGP) offer a Bayesian non-parametric modelling framework with strong function compositionality, and helps in overcoming these limitations. In this paper, we propose DGP models for the task of Text Classification and an empirical comparison of the performance of shallow and Deep Gaussian Process models is made. Extensive experimentation is performed on the benchmark Text Classification datasets such as TREC (Text REtrieval Conference), SST (Stanford Sentiment Treebank), MR (Movie Reviews), R8 (Reuters-8), which demonstrate the effectiveness of DGP models.- Anthology ID:
- 2020.lrec-1.185
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 1485–1491
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.185
- DOI:
- Cite (ACL):
- P. Jayashree and P. K. Srijith. 2020. Evaluation of Deep Gaussian Processes for Text Classification. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 1485–1491, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Evaluation of Deep Gaussian Processes for Text Classification (Jayashree & Srijith, LREC 2020)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2020.lrec-1.185.pdf