On embedding Lambek calculus into commutative categorial grammars
- URL: http://arxiv.org/abs/2005.10058v4
- Date: Fri, 19 Nov 2021 12:04:31 GMT
- Title: On embedding Lambek calculus into commutative categorial grammars
- Authors: Sergey Slavnov
- Abstract summary: We consider tensor grammars, which are an example of commutative" grammars, based on the classical (rather than intuitionistic) linear logic.
The basic ingredient are tensor terms, which can be seen as encoding and generalizing proof-nets.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We consider tensor grammars, which are an example of \commutative" grammars,
based on the classical (rather than intuitionistic) linear logic. They can be
seen as a surface representation of abstract categorial grammars ACG in the
sense that derivations of ACG translate to derivations of tensor grammars and
this translation is isomorphic on the level of string languages. The basic
ingredient are tensor terms, which can be seen as encoding and generalizing
proof-nets. Using tensor terms makes the syntax extremely simple and a direct
geometric meaning becomes transparent. Then we address the problem of encoding
noncommutative operations in our setting. This turns out possible after
enriching the system with new unary operators. The resulting system allows
representing both ACG and Lambek grammars as conservative fragments, while the
formalism remains, as it seems to us, rather simple and intuitive.
Related papers
- Training Neural Networks as Recognizers of Formal Languages [87.06906286950438]
Formal language theory pertains specifically to recognizers.
It is common to instead use proxy tasks that are similar in only an informal sense.
We correct this mismatch by training and evaluating neural networks directly as binary classifiers of strings.
arXiv Detail & Related papers (2024-11-11T16:33:25Z) - An introduction to graphical tensor notation for mechanistic
interpretability [0.0]
It's often easy to get confused about which operations are happening between tensors.
The first half of this document introduces the notation and applies it to some decompositions.
The second half applies it to some existing some foundational approaches for mechanistically understanding language models.
arXiv Detail & Related papers (2024-02-02T02:56:01Z) - The Tensor as an Informational Resource [1.3044677039636754]
A tensor is a multidimensional array of numbers that can be used to store data, encode a computational relation and represent quantum entanglement.
We propose a family of information-theoretically constructed preorders on tensors, which can be used to compare tensors with each other and to assess the existence of transformations between them.
arXiv Detail & Related papers (2023-11-03T18:47:39Z) - Backpack Language Models [108.65930795825416]
We present Backpacks, a new neural architecture that marries strong modeling performance with an interface for interpretability and control.
We find that, after training, sense vectors specialize, each encoding a different aspect of a word.
We present simple algorithms that intervene on sense vectors to perform controllable text generation and debiasing.
arXiv Detail & Related papers (2023-05-26T09:26:23Z) - Lexinvariant Language Models [84.2829117441298]
Token embeddings, a mapping from discrete lexical symbols to continuous vectors, are at the heart of any language model (LM)
We study textitlexinvariantlanguage models that are invariant to lexical symbols and therefore do not need fixed token embeddings in practice.
We show that a lexinvariant LM can attain perplexity comparable to that of a standard language model, given a sufficiently long context.
arXiv Detail & Related papers (2023-05-24T19:10:46Z) - Making first order linear logic a generating grammar [0.0]
It is known that different categorial grammars have surface representation in a fragment of first order multiplicative linear logic (MLL1)
We show that the fragment of interest is equivalent to the recently introduced extended type calculus (ETTC)
arXiv Detail & Related papers (2022-06-17T18:11:34Z) - Multilingual Extraction and Categorization of Lexical Collocations with
Graph-aware Transformers [86.64972552583941]
We put forward a sequence tagging BERT-based model enhanced with a graph-aware transformer architecture, which we evaluate on the task of collocation recognition in context.
Our results suggest that explicitly encoding syntactic dependencies in the model architecture is helpful, and provide insights on differences in collocation typification in English, Spanish and French.
arXiv Detail & Related papers (2022-05-23T16:47:37Z) - Geometry-Aware Supertagging with Heterogeneous Dynamic Convolutions [0.7868449549351486]
We revisit constructive supertagging from a graph-theoretic perspective.
We propose a framework based on heterogeneous dynamic graph convolutions.
We test our approach on a number of categorial grammar datasets spanning different languages.
arXiv Detail & Related papers (2022-03-23T07:07:11Z) - VLGrammar: Grounded Grammar Induction of Vision and Language [86.88273769411428]
We study grounded grammar induction of vision and language in a joint learning framework.
We present VLGrammar, a method that uses compound probabilistic context-free grammars (compound PCFGs) to induce the language grammar and the image grammar simultaneously.
arXiv Detail & Related papers (2021-03-24T04:05:08Z) - Unsupervised Distillation of Syntactic Information from Contextualized
Word Representations [62.230491683411536]
We tackle the task of unsupervised disentanglement between semantics and structure in neural language representations.
To this end, we automatically generate groups of sentences which are structurally similar but semantically different.
We demonstrate that our transformation clusters vectors in space by structural properties, rather than by lexical semantics.
arXiv Detail & Related papers (2020-10-11T15:13:18Z) - Traduction des Grammaires Cat\'egorielles de Lambek dans les Grammaires
Cat\'egorielles Abstraites [0.0]
This internship report is to demonstrate that every Lambek Grammar can be, not entirely but efficiently, expressed in Abstract Categorial Grammars (ACG)
The main idea is to transform the type rewriting system of LGs into that of Context-Free Grammars (CFG) by erasing introduction and elimination rules and generating enough axioms so that the cut rule suffices.
Although the underlying algorithm was not fully implemented, this proof provides another argument in favour of the relevance of ACGs in Natural Language Processing.
arXiv Detail & Related papers (2020-01-23T18:23:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.