Papers by Peerapon Vateekul
Rational LAMOL: A Rationale-based Lifelong Learning Framework (2021.acl-long)
Copied to clipboard
Kasidis Kanwatchara, Thanapapas Horsuwan, Piyawat Lertvittayakumjorn, Boonserm Kijsirikul, Peerapon Vateekul
| Challenge: | Existing paradigms for machine learning suffer from catastrophic forgetting when a model completely forgets what it just learned in previous tasks. |
| Approach: | They propose to exploit unsupervised rationale generation to improve the performance of a lifelong language learning model by applying critical freezing guided by human rationales. |
| Outcome: | The proposed framework outperforms vanilla LAMOL on most permutations and unsupervised rationale generation consistently improves the overall performance. |
ESRA: Explainable Scientific Research Assistant (2021.acl-demo)
Copied to clipboard
Pollawat Hongwimol, Peeranuth Kehasukcharoen, Pasit Laohawarutchai, Piyawat Lertvittayakumjorn, Aik Beng Ng, Zhangsheng Lai, Timothy Liu, Peerapon Vateekul
| Challenge: | Existing literature search systems only present metadata of papers as search results, which requires users to read the entire abstracts to understand the brief contents of the returned papers. |
| Approach: | They propose to use a knowledge graph extracted from abstracts of 23k papers on arXiv’s cs.CL category to augment search results with relevant details and explanations. |
| Outcome: | The proposed platform can accelerate the users’ search process with paper explanations and helps them better explore the landscape of the topics of interest. |
Label-Aware Automatic Verbalizer for Few-Shot Text Classification in Mid-To-Low Resource Languages (2024.acl-srw)
Copied to clipboard
| Challenge: | Prompt-based learning has shown its effectiveness in few-shot text classification. |
| Approach: | They propose a prompt-based learning verbalizer that automatically selects a word to represent each class . they use the label name along with the conjunction "and" to induce the model to generate more effective words for the verbaliser. |
| Outcome: | The proposed method outperforms existing verbalizers on four Southeast Asian languages. |