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Found 12 papers, 4 papers with code
Modeling Incremental Language Comprehension in the Brain with Combinatory Categorial Grammar
Hierarchical sentence structure plays a role in word-by-word human sentence comprehension, but it remains unclear how best to characterize this structure and unknown how exactly it would be recognized in a step-by-step process model.
A Root of a Problem: Optimizing Single-Root Dependency Parsing
We describe two approaches to single-root dependency parsing that yield significant speed ups in such parsing.
Computing All Quantifier Scopes with CCG
We present a method for computing all quantifer scopes that can be extracted from a single CCG derivation.
Formal Basis of a Language Universal
Abstract Steedman (2020) proposes as a formal universal of natural language grammar that grammatical permutations of the kind that have given rise to transformational rules are limited to a class known to mathematicians and computer scientists as the “separable” permutations.
Multipath parsing in the brain
Humans understand sentences word-by-word, in the order that they hear them.
SynJax: Structured Probability Distributions for JAX
The development of deep learning software libraries enabled significant progress in the field by allowing users to focus on modeling, while letting the library to take care of the tedious and time-consuming task of optimizing execution for modern hardware accelerators.
Modeling structure-building in the brain with CCG parsing and large language models
These effects are spatially distinct from bilateral superior temporal effects that are unique to predictability.
Unbiased and Efficient Sampling of Dependency Trees
However, the majority of dependency treebanks require that every valid dependency tree has a single edge coming out of the ROOT node, a constraint that is not part of the definition of spanning trees.
Transformer Grammars: Augmenting Transformer Language Models with Syntactic Inductive Biases at Scale
We find that TGs outperform various strong baselines on sentence-level language modeling perplexity, as well as on multiple syntax-sensitive language modeling evaluation metrics.
Modality and Negation in Event Extraction
Language provides speakers with a rich system of modality for expressing thoughts about events, without being committed to their actual occurrence.
Removing Biases from Trainable MT Metrics by Using Self-Training
Our solution is more general: (1) it does not address only the recall bias but also all other biases that might be present in the data and (2) it does not require any knowledge of the types of features used which is useful in cases when manual tuning of metric's weights is not possible.
