Papers with neural-network architectures
State-of-the-art Chinese Word Segmentation with Bi-LSTMs (D18-1)
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| Challenge: | A wide variety of neural-network architectures have been proposed for the task of Chinese word segmentation. |
| Approach: | They propose a bidirectional LSTM model with standard deep learning techniques and best practices for the task of Chinese word segmentation. |
| Outcome: | The proposed model outperforms models based on standard deep learning techniques and best practices on Chinese word segmentation datasets. |
Meta-Learning Neural Mechanisms rather than Bayesian Priors (2025.acl-long)
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| Challenge: | a meta-learning approach has been proposed to integrate human-like learning biases into neural networks . a recent study suggests that meta-training on a single formal language can improve a model . |
| Approach: | They propose to integrate human-like learning biases into neural-network architectures . they use symbolic models to capture aspects of humans' basic generalisations from small data . |
| Outcome: | The proposed model can learn from a single language as much as 5000 different languages . the model can be scaled to a larger model and training datasets . |