Papers with symbolic approach
Realizing Universal Dependencies Structures (D19-63)
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| Challenge: | UD-SURFR is a symbolic surface realizer for Universal Dependencies structures . it is based on a tree of constituents that is then converted into an English sentence . |
| Approach: | They propose a symbolic system that transforms a dependency tree into a tree of constituents that is transformed into an English sentence by an existing realizer. |
| Outcome: | The proposed system is quite fast and showed competitive results for English sentences using automatic and manual evaluation measures. |
Similar, but why? A Toolkit for Explaining Text Similarity (2026.eacl-demo)
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| Challenge: | XPLAINSIM is a Python package that explains textual similarity in an easy-to-use way. |
| Approach: | They propose a Python package that unifies three approaches to explain text similarity . they demonstrate the value of the package through intuitive examples and empirical research . |
| Outcome: | XPLAINSIM is a Python package that unifies three approaches to explain text similarity . the authors show that the package is useful for explaining text similarities in a simple way . |
Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation (2024.acl-long)
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| Challenge: | Logic-based approaches to reasoning have lost popularity due to limited scalability and coverage. |
| Approach: | They present a dataset of 28K sentence-level NL-FOL pairs from GPT4 and a LogicLLaMA2-7B/13B fine-tuned on MALLS for NL translation. |
| Outcome: | The proposed model can be used standalone or to correct previously generated rules by GPT3.5. |
Top-Down Structurally-Constrained Neural Response Generation with Lexicalized Probabilistic Context-Free Grammar (N19-1)
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| Challenge: | Neural encoder-decoder architectures have shown promise for natural language generation. |
| Approach: | They propose to generate words according to order of first appearance in lexicalized PCFG parse tree . they also combine neural model with symbolic approach to generate syntactic structure . |
| Outcome: | The proposed method improves over sequence-to-sequence baseline in diversity and relevance. |
A Zero-Shot Neuro-Symbolic Approach for Complex Knowledge Graph Question Answering (2025.findings-emnlp)
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| Challenge: | Existing low-resource Knowledge Graph Question Answering (KGQA) methods rely heavily on Large Language Models (LLMs) KGQA methods based on LLMs are limited in their ability to model KG structure without additional data. |
| Approach: | They propose a KGQA framework that can operate in a zero-shot setting . they propose NS-KGQA to use neural KG embeddings to model KG structure . |
| Outcome: | The proposed framework outperforms existing LLM-based zero-shot baselines by 26%. |