Papers with symbolic models
NeuSTIP: A Neuro-Symbolic Model for Link and Time Prediction in Temporal Knowledge Graphs (2023.emnlp-main)
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| Challenge: | Temporal Knowledge Graphs (KGs) are factual information repositories where a fact is associated with a time interval. |
| Approach: | They propose a temporal NS model for knowledge graph completion that performs link prediction and time interval prediction in a TKG. |
| Outcome: | The proposed model shows competitive performance on link prediction and time prediction. |
Neural Grammatical Error Correction with Finite State Transducers (N19-1)
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| Challenge: | Language model based GEC (LM-GEC) is a promising alternative to SMT and neural sequence-to-sequence models. |
| Approach: | They propose to use finite state transducers to improve LM-GEC by rescoring with neural language models. |
| Outcome: | The proposed model outperforms the best published results on the CoNLL-2014 test set and achieves far better relative improvements over the baselines. |
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 . |
LLM-Guided Semantic Bootstrapping for Interpretable Text Classification with Tsetlin Machines (2026.findings-acl)
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| Challenge: | Pretrained language models (PLMs) provide strong semantic representations but are costly and opaque. |
| Approach: | They propose a framework that transfers pretrained language models into symbolic form and integrates them into symbolic models. |
| Outcome: | The proposed framework improves interpretability and accuracy across multiple text classification tasks while remaining fully symbolic and efficient. |