Categorial Grammar Induction with Stochastic Category Selection (2024.lrec-main)
Copied to clipboard
| Challenge: | categorial grammar inducers have been used to learn from raw data, but they use shortcuts to ensure branching behavior. |
| Approach: | They propose a grammar inducer that learns from raw data and does not rely on bias terms . they show a recall-homogeneity of 0.48 on a corpus of English child-directed speech . |
| Outcome: | The proposed model achieves a recall-homogeneity of 0.48 on a corpus of English child-directed speech . |
Similar Papers
Categorial grammar induction from raw data (2023.findings-acl)
Copied to clipboard
| Challenge: | a new model for categorial grammar induction is based on raw data without part-of-speech information. |
| Approach: | They propose a grammar induction model that learns from raw data without part-of-speech information. |
| Outcome: | a new model for inducing a basic categorial grammar is developed . the model attains a recall-homogeneity of 0.33 on average, and a bias toward forward function application is added . |
Compound Probabilistic Context-Free Grammars for Grammar Induction (P19-1)
Copied to clipboard
| Challenge: | Existing approaches to grammar induction have resorted to manually-engineered features and auxiliary objectives to induce the desired structures. |
| Approach: | They propose a formalization of the grammar induction problem that models sentences as being generated by a compound probabilistic context free grammar. |
| Outcome: | Experiments on English and Chinese show that the proposed approach is more efficient than other methods. |
Leveraging Grammar Induction for Language Understanding and Generation (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing grammar induction methods do not provide sufficient performance in downstream tasks. |
| Approach: | They propose an unsupervised grammar induction method for language understanding and generation using a grammar parser and a syntactic mask. |
| Outcome: | The proposed method performs better on from-scratch and pre-trained scenarios. |
Character-based PCFG Induction for Modeling the Syntactic Acquisition of Morphologically Rich Languages (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models for syntactic acquisition are word-based and do not inspect functional affixes. |
| Approach: | They propose a computer-based induction model that allows a clean ablation of the influence of subword information in grammar induction. |
| Outcome: | The proposed model is more accurate in morphologically richer languages with subword information than word-based models. |
Cross-lingual CCG Induction (N19-1)
Copied to clipboard
| Challenge: | Combinatory categorial grammars are linguistically motivated and useful for semantic parsing, but costly to acquire in a supervised way and difficult to acquire unsupervised. |
| Approach: | They propose an alternative using a source-language parser and a parallel corpus to induce a grammar and parsing model for a target language. |
| Outcome: | The proposed model outperforms POS tags on 3 out of 8 languages and unsupervised CCG induction on 6 out of 8. |
Grammar Induction with Neural Language Models: An Unusual Replication (D18-1)
Copied to clipboard
| Challenge: | Recent work on latent tree learning attempts to develop models with parse-valued latent variables and train them on non-parsing tasks. |
| Approach: | They propose a model with parse-valued latent variables and a strong latent tree learning result on constituency parsing. |
| Outcome: | The proposed model outperforms all baselines and performs competitively with symbolic grammar induction systems. |
Finding Dataset Shortcuts with Grammar Induction (2022.emnlp-main)
Copied to clipboard
| Challenge: | Prior work on shortcut detection focused on enumerating features like unigrams or bigrams . prior work relied on post-hoc models that reveal qualitative patterns without a clear statistical interpretation . |
| Approach: | They propose to use probabilistic grammars to characterize and discover shortcuts in NLP datasets using context-free grammars and synchronous context- free grammars. |
| Outcome: | The proposed grammars reveal interesting shortcut features in a number of datasets, including simple and high-level features, and automatically identify groups of test examples on which conventional classifiers fail. |
End-to-End Reinforcement Learning for Automatic Taxonomy Induction (P18-1)
Copied to clipboard
| Challenge: | Existing methods for automating taxonomy induction often divide the problem into two subtasks . a novel end-to-end reinforcement learning approach is proposed to improve the accuracy of such methods. |
| Approach: | They propose an end-to-end reinforcement learning approach to automatic taxonomy induction from a set of terms. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on two public datasets of different domains. |
Creating Grammar Teaching Material for Endangered Languages with Hybrid Grammar Induction (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for creating grammar lessons are labor-intensive and often fall to teachers who lack formal training in grammar. |
| Approach: | They propose a hybrid grammar-induction method that uses typological priors, Bayesian inference, constrained LLM reasoning and retrieval from sparse corpora to generate topic-specific grammar lessons. |
| Outcome: | The proposed method can produce coherent and useful lessons with better quality when modest explanatory evidence is available. |
A Generative Model for Lambek Categorial Sequents (2024.lrec-main)
Copied to clipboard
| Challenge: | generative models such as PLC+ generate grammatical sentences with a high probability of being grammatized. |
| Approach: | They propose a generative model, PLC+, for generating Lambek Categorial Grammar(LCG) sequents. |
| Outcome: | The proposed model generates Lambek Categorial Grammar(LCG) sequents and is more robust to probabilistic context-free grammars. |