Papers by Jarana Manotumruksa
Unsupervised Few-Bits Semantic Hashing with Implicit Topics Modeling (2020.findings-emnlp)
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| Challenge: | Existing methods for few-bits hashing cannot be guaranteed due to severe information loss. |
| Approach: | They propose a simple unsupervised neural generative semantic hashing method with a focus on few-bits hash. |
| Outcome: | The proposed method improves on the state-of-the-art methods in few-bits hashing. |
Improving Dialogue State Tracking with Turn-based Loss Function and Sequential Data Augmentation (2021.findings-emnlp)
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| Challenge: | Existing models rely on a traditional cross-entropy loss function during training, which may not be optimal for improving the joint goal accuracy. |
| Approach: | They propose a Turn-based Loss Function that penalises the model if it inaccurately predicts a slot value at the early turns more so than in later turns to improve joint goal accuracy. |
| Outcome: | The proposed techniques improve the state-of-the-art model by approximately 7-8% relative reduction in error and achieve a new state- of-the art joint goal accuracy with 59.50 and 54.90 on MultiWOZ2.1 and MultiWOz2.2, respectively. |