Papers with Syntax
FastKASSIM: A Fast Tree Kernel-Based Syntactic Similarity Metric (2023.eacl-main)
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| Challenge: | Existing syntactic similarity metrics are computationally expensive and inconsistent when faced with syntaktically dissimilar documents. |
| Approach: | They propose a metric which pairs and averages the most similar constituency parse trees between a pair of documents based on tree kernels. |
| Outcome: | The proposed metric is more robust to syntactic dissimilarities and runs up to 5.32 times faster than its predecessor over documents in the r/ChangeMyView corpus. |
Mimic and Conquer: Heterogeneous Tree Structure Distillation for Syntactic NLP (2020.findings-emnlp)
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| Challenge: | Existing work on integrating syntactic information into neural networks uses a single tree, such as a constituency or a dependency tree. |
| Approach: | They propose a method to integrate heterogeneous structure knowledge into a unified sequential LSTM encoder. |
| Outcome: | The proposed method outperforms tree encoders on four syntax-dependent tasks and is efficient and accurate. |
Transition-based Neural RST Parsing with Implicit Syntax Features (C18-1)
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| Challenge: | Syntax has been a useful source of information for statistical RST discourse parsing. |
| Approach: | They propose an implicit syntax feature extraction approach using hidden-layer vectors extracted from a neural syntax parser. |
| Outcome: | The proposed model with dynamic oracle is competitive with existing models. |
Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations (N19-1)
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| Challenge: | Syntax integration has been demonstrated highly effective in neural machine translation (NMT). |
| Approach: | They propose a method to integrate source-side syntax implicitly for neural machine translation . they use hidden representations of a well-trained end-to-end dependency parser to concatenate them with ordinary word embeddings to enhance basic NMT models. |
| Outcome: | The proposed method outperforms existing methods on two translation tasks . it can be easily integrated into the widely-used sequence-to-sequence (Seq2Sequen) framework . |
Explicitly Modeling Syntax in Language Models with Incremental Parsing and a Dynamic Oracle (2021.naacl-main)
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| Challenge: | Failing to capture the structure of input language could lead to generalization problems and over-parametrization. |
| Approach: | They propose a new syntax-aware language model that explicitly models the structure with an incremental parser and maintains the conditional probability setting of a standard language model. |
| Outcome: | The proposed model can achieve strong results in language modeling, parsing, and syntactic generalization tests while using fewer parameters than other models. |
Syntactic Substitutability as Unsupervised Dependency Syntax (2023.emnlp-main)
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| Challenge: | Syntax is a latent hierarchical structure which underpins the robust and compositional nature of human language. |
| Approach: | They propose a method to induce syntactic dependencies theory-agnostically by substituting words from the same category for words at either end of a dependency. |
| Outcome: | The proposed method achieves 79.5% recall on long-distance subject-verb agreement constructions compared to 8.9% using a previous method. |