arXiv Machine Learning
Jul 10
Holographic Neural PCFG for Unsupervised Parsing
arXiv:2607. 08063v1 Announce Type: cross Abstract: Unsupervised constituency parsing aims to accurately induce latent tree structures from raw text alone.
Holographic Neural PCFG for Unsupervised Parsing
Unsupervised constituency parsing aims to accurately induce latent tree structures from raw text alone. Recent neural parameterizations of PCFGs achieve strong performance in both supervised and unsupervised parsing, yet rely on high-capacity black-box networks for rule scoring -- as exemplified by the Neural PCFG family -- leaving rule probabilities without an interpretable mathematical form.
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arXiv Computation and Language
Aug 28
Representing and Parsing Korean Constituency Structure at Different Levels of Granularity
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arXiv Computation and Language
Sep 2
CWoMP: Morpheme Representation Learning for Interlinear Glossing
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arXiv Machine Learning
Jun 25
Weave of Formal Thought
arXiv:2606. 25987v1 Announce Type: cross Abstract: Large language models (LLMs) attain remarkable surface fluency on code, yet they neither formally guarantee the syntactic validity of their output nor leverage the hierarchical structure defining the target language.
arXiv AI
Aug 18
Structural Generalization on SLOG without Hand-Written Rules
arXiv:2604. 26157v4 Announce Type: replace-cross Abstract: Structural generalization in semantic parsing requires systems to apply learned compositional rules to novel structural combinations.
arXiv Computation and Language
Aug 25
How Useful are LLMs for Grammar Engineering? Cantonese ParGram Resources and Controlled Experimental Evaluation with English Baselines
arXiv:2608.23448v1 Announce Type: new Abstract: This paper presents new Cantonese ParGram resources and evaluates LLMs for knowledge-driven grammar engineering within a controlled experimental paradi...
arXiv Computation and Language
Sep 18
Dictionary-Constrained Grapheme-to-Phoneme for Unsegmented Languages from LLM-Annotated Data
The paper introduces a context‑aware neural grapheme‑to‑phoneme (G2P) system for unsegmented languages like Japanese, using a discriminative conditional random field over a word lattice built from dictionaries. It addresses data scarcity by generating over two million sentences with large language models. Experiments show the method surpasses traditional morphological analyzers and neural sequence models, achieving 99.62% target word reading accuracy and very low phoneme error rates on the Joyo‑Kanji‑Yomi benchmark.
arXiv AI
Jul 7
RLIE: Rule Generation with Logistic Regression, Iterative Refinement, and Evaluation for Large Language Models
arXiv:2510. 19698v3 Announce Type: replace Abstract: Large Language Models (LLMs) can propose rules in natural language, sidestepping the need for a predefined predicate space in traditional rule learning.
Hugging Face Trending Papers
Sep 17
Dictionary-Constrained Grapheme-to-Phoneme for Unsegmented Languages from LLM-Annotated Data
The paper introduces a context‑aware neural grapheme‑to‑phoneme (G2P) system for unsegmented languages like Japanese, combining a discriminative conditional random field over a dictionary‑based word lattice with large language model‑generated training data. By generating over two million synthetic sentences, the method addresses data scarcity and achieves superior performance compared to traditional morphological analyzers and neural sequence models. On the Joyo‑Kanji‑Yomi benchmark, it attains 99.62% target word reading accuracy, 0.32% target word phoneme error rate, and 0.14% sentence phoneme error rate.
arXiv Computation and Language
Sep 24
ThaiTrees: Thai Syntactic Dependency Trees Across Domains
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arXiv AI
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Auto-Formalizing Neuro-Symbolic Predictors
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arXiv AI
Sep 25
Multi-Task Learning by using Contextualized Word Representations for Syntactic Parsing of a Morphologically Rich Language
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