Papers by Piyawat Lertvittayakumjorn
Knowledge-Driven Slot Constraints for Goal-Oriented Dialogue Systems (2021.naacl-main)
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| Challenge: | Traditional goal-oriented dialogue systems allow execution of validation rules as a post-processing step after slots have been filled which can lead to error accumulation. |
| Approach: | They propose a task of constraint violation detection based on knowledge-driven slot constraints . they propose methods to integrate external knowledge into the system and compare it to traditional rule-based pipeline approach . |
| Outcome: | The proposed task compares to the existing system and a rule-based pipeline. |
GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns (2022.lrec-1)
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| Challenge: | a Python library is available for extracting patterns from textual data. |
| Approach: | They propose a Python library for extracting patterns from textual data . it integrates a public implementation of the existing GrASP algorithm . |
| Outcome: | The proposed library integrates a public implementation of the existing GrASP algorithm. |
Supporting Complaints Investigation for Nursing and Midwifery Regulatory Agencies (2021.acl-demo)
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Piyawat Lertvittayakumjorn, Ivan Petej, Yang Gao, Yamuna Krishnamurthy, Anna Van Der Gaag, Robert Jago, Kostas Stathis
| Challenge: | Fig. 1 illustrates the major components and workflow of our proposed system to improve the efficiency of complaints investigation for nursing and midwifery regulators. |
| Approach: | They propose a decision support system that uses machine learning and natural language processing techniques to process complaints and predict their risk level. |
| Outcome: | The proposed system uses state-of-the-art machine learning and natural language processing techniques to process complaints and predict risk levels. |
Explanation-Based Human Debugging of NLP Models: A Survey (2021.tacl-1)
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| Challenge: | In this paper, we review literature that exploits explanations to enable humans to fix bugs in NLP models. |
| Approach: | They review literature that exploits explanations to enable humans to fix bugs in NLP models. |
| Outcome: | The proposed approach is described in detail in this paper and is based on three dimensions of the problem explanation-based human debugging (EBHD). |
Rational LAMOL: A Rationale-based Lifelong Learning Framework (2021.acl-long)
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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)
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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)
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| 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. |
FIND: Human-in-the-Loop Debugging Deep Text Classifiers (2020.emnlp-main)
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| Challenge: | Existing models are limited in the number of available datasets and lack the necessary tools to improve them. |
| Approach: | They propose a framework which enables humans to debug deep learning text classifiers by disabling irrelevant hidden features. |
| Outcome: | Experiments show that using FIND, humans can improve CNN text classifiers trained on different types of imperfect datasets. |
Human-grounded Evaluations of Explanation Methods for Text Classification (D19-1)
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| Challenge: | Explainable Artificial Intelligence (XAI) is aimed at providing explanations for decisions made by AI systems. |
| Approach: | They propose to use model-agnostic and model-specific explanation methods for CNNs for text classification to provide human-grounded evaluations. |
| Outcome: | The proposed methods could be used to explain models' results and improve AIs and humans in many cases. |
Towards Geo-Culturally Grounded LLM Generations (2025.acl-short)
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| Challenge: | Contemporary large language models (LLMs) are pretrained on huge corpora of natural language text and fine-tuned using human feedback to improve their quality. |
| Approach: | They compare the performance of standard LLMs, LLM augmented with retrievals from a bespoke knowledge base and LLM with retrieval from . a web search on multiple cultural awareness benchmarks. |
| Outcome: | The retrieval augmented generation and search grounding techniques improve LLMs' ability to display familiarity with various national cultures on cultural awareness benchmarks. |
Integrating Semantic Knowledge to Tackle Zero-shot Text Classification (N19-1)
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| Challenge: | Existing approaches to classify text documents of emerging classes are ineffective because of insufficient or even unavailable training data. |
| Approach: | They propose a two-phase framework with data augmentation and feature augmentation to deal with unseen classes effectively using four kinds of semantic knowledge. |
| Outcome: | The proposed framework achieves the best overall accuracy compared with baselines and recent approaches in classifying real-world texts under the zero-shot scenario. |