Papers with CKY
A Probabilistic Toolkit for Multi-grained Word Segmentation in Chinese (2025.coling-demos)
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| Challenge: | Existing tools for word segmentation are based on different linguistic theories or target different scenarios. |
| Approach: | They propose a probabilistic toolkit for multi-grained word segmentation in Chinese . they adopt semi-Markov CRF for single-grain word segmenting (SWS) . |
| Outcome: | The proposed approach can produce marginal probabilities of words during inference and significantly improve performance in the cross-domain scenario. |
Span-based Semantic Parsing for Compositional Generalization (2021.acl-long)
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| Challenge: | despite success of sequence-to-sequence models, they fail in compositional generalization . a span-based parser that predicts a utterance over spans improves performance . |
| Approach: | They propose a span-based parser that predicts a utterance over a given span tree . they propose to use CKY to encode how partial programs compose over spans . |
| Outcome: | The proposed model performs better on random splits than baselines that require compositional generalization. |
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders (N19-1)
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| Challenge: | Using the deep inside-outside recursive autoencoder, we can extract both shallow parses and full syntactic trees from any domain or language automatically. |
| Approach: | They propose a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents within the induced tree. |
| Outcome: | The proposed method outperforms previous methods on the WSJ dataset. |
Efficient Semiring-Weighted Earley Parsing (2023.acl-long)
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| Challenge: | Using Earley's context-free parsing algorithm, we show that the speed-ups are effective in practice. |
| Approach: | They propose a context-free parsing algorithm with various known and new speed-ups that improve Earley's (1970) O(N3|G||R|) They also propose 'a binarized version' that achieves runtime of O(M| |G| when the grammar is represented compactly as a single finite-state automaton M. |
| Outcome: | The proposed algorithm can be used to reduce the complexity of CKY on a binarized version of the grammar G. |
Efficient Constituency Parsing by Pointing (2020.acl-main)
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| Challenge: | Constituency parsing is a core task in natural language processing (NLP) Existing methods for constituency paring are greedy transition-based or globally optimized. |
| Approach: | They propose a constituency parsing model that casts the problem into a series of pointing tasks. |
| Outcome: | The proposed model achieves 92.78 F1 without pre-trained models, which is faster than existing models. |
Decomposed Local Models for Coordinate Structure Parsing (N19-1)
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| Challenge: | Existing methods for coordination boundary identification are inefficient, even for humans. |
| Approach: | They propose a simple and accurate model for coordination boundary identification . they combine syntactic parsers and neural networks to compute similarity and replaceability features of conjuncts . |
| Outcome: | The proposed model outperforms similarity-based approaches but cannot handle more than two conjuncts in a coordination and multiple coordinations at once. |
To be Continuous, or to be Discrete, Those are Bits of Questions (2024.acl-long)
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| Challenge: | Existing studies have shown that binary representations preserve information on the output side, but they are not available for continuous representations. |
| Approach: | They propose to extend the contrastive hashing method to output binary labels instead of continuous vectors. |
| Outcome: | The proposed representation preserves structural information along with label information . it achieves competitive performance on various structured prediction tasks . |
Approximating CKY with Transformers (2023.findings-emnlp)
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| Challenge: | CKY algorithm is a cubic dependence on sentence length, but transformers can be used to approximate it. |
| Approach: | They propose a transformer-based approach that approximates the CKY algorithm by directly predicting a sentence's parse and avoiding its cubic dependence on sentence length. |
| Outcome: | The proposed approach achieves better performance than comparable parsers that make use of CKY, while being faster. |