Papers by Canasai Kruengkrai
Teaching Text Agents to Learn Sequential Decision Making from Failure (2025.acl-long)
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| Challenge: | Existing methods to improve text-based reinforcement-learning agents' performance contain failed actions that reinforce incorrect behaviors and reduce task success rates. |
| Approach: | They propose a failed action-aware objective that suppresses negative impact of failed actions . they propose 'failed action-based' perturbation method that leverages unsuccessful trajectories to construct new successful ones . |
| Outcome: | The proposed method outperforms baselines and generalizes across environments. |
Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling (2020.acl-main)
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| Challenge: | Existing models for named entity recognition (NER) use sentence-level labels, which are expensive to obtain, to improve NER. |
| Approach: | They propose a sentence-level named entity recognition model that uses sentence-based labels that are easy to obtain. |
| Outcome: | The proposed model produces 3.78%, 4.20%, 2.08% improvements in F1 over the baseline on e-commerce product titles in Vietnamese, Thai, and Indonesian, respectively. |
A Multi-Level Attention Model for Evidence-Based Fact Checking (2021.findings-acl)
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| Challenge: | Recent state-of-the-art approaches have developed increasingly sophisticated models based on graph structures. |
| Approach: | They propose a simple model that can be trained on sequence structures and can benefit from joint training. |
| Outcome: | The proposed model outperforms the graph-based models on a large-scale dataset for Fact Extraction and VERification. |
Revisiting Pathologies of Neural Models under Input Reduction (2023.findings-acl)
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| Challenge: | Recent studies have shown that modern neural models tend to be miscalibrated. |
| Approach: | They examine why models produce high-confidence predictions on inputs that appear nonsensical to humans . previous work suggested that models fail to assign low probabilities due to model overconfidence . |
| Outcome: | The proposed methods can be extended to reduce the number of examples but with the cost of miscalibration. |
DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks (2020.emnlp-main)
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Bosheng Ding, Linlin Liu, Lidong Bing, Canasai Kruengkrai, Thien Hai Nguyen, Shafiq Joty, Luo Si, Chunyan Miao
| Challenge: | Data augmentation techniques are widely used to improve machine learning performance . however, due to the complexity of language, it is difficult to generalize such rules for languages. |
| Approach: | They propose a method to generate high quality synthetic data for low-resource tagging tasks . they use unlabeled data only and unlabelled data plus a knowledge base . |
| Outcome: | The proposed method outperforms baselines on NER, part of speech and target based sentiment analysis tasks. |
Better Exploiting Latent Variables in Text Modeling (P19-1)
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| Challenge: | Consistent gains in performance on two datasets, Penn Treebank and Yahoo, indicate the generalizability of our method. |
| Approach: | They propose a method to exploit latent variables through hidden state averaging by sampling latent variable multiple times at a gradient step. |
| Outcome: | The proposed method shows consistent gains on two datasets showing that it is generalizable. |
Mitigating the Diminishing Effect of Elastic Weight Consolidation (2022.coling-1)
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| Challenge: | Existing work addresses catastrophic forgetting in sequential training by fine-tuning pre-trained language models on different datasets. |
| Approach: | They propose to rescale the components of EWC to mitigate catastrophic forgetting by mixing new and old training data and retraining the model from scratch. |
| Outcome: | The proposed method requires smaller values for the trade-off parameters to achieve comparable results to EWC on natural language inference and fact-checking tasks. |
Learning to Flip the Sentiment of Reviews from Non-Parallel Corpora (D19-1)
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| Challenge: | Existing methods for flipping sentiment are costly and require parallel data. |
| Approach: | They propose a method for acquiring imperfectly aligned sentences from non-parallel corpora and propose 'sensational' model that learns to minimize sentiment and content losses in a fully end-to-end manner. |
| Outcome: | The proposed model offers well-balanced results across Yelp restaurant and Amazon product reviews. |
Outlier-Aware Training for Improving Group Accuracy Disparities (2022.aacl-srw)
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| Challenge: | Methods addressing spurious correlations such as Just Train Twice involve reweighting a subset of the training set to maximize the worst-group accuracy. |
| Approach: | They propose to reweight a subset of a training set to maximize the worst-group accuracy by detecting outliers and removing them before reweighing. |
| Outcome: | The proposed method achieves competitive or better accuracy compared with JTT and can detect and remove annotation errors in the subset being reweighted in JTT. |
Bridging Textual and Tabular Worlds for Fact Verification: A Lightweight, Attention-Based Model (2024.lrec-main)
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| Challenge: | Existing fact-checking systems rely on extensive preprocessing and rule-based transformations, leading to potential context loss or misleading encodings. |
| Approach: | They propose a simple yet powerful model that nullifies the need for modality conversion, thereby preserving the original evidence’s context. |
| Outcome: | The proposed model nullifies the need for modality conversion, preserving the original evidence’s context. |