A logical word embedding for learning grammar

• URL: http://arxiv.org/abs/2304.14590v2
• Date: Tue, 6 Jun 2023 00:46:49 GMT
• Title: A logical word embedding for learning grammar
• Authors: Sean Deyo, Veit Elser
• Abstract summary: We introduce the logical grammar emdebbing (LGE) to enable unsupervised inference of lexical categories and syntactic rules from a corpus of text. LGE produces comprehensible output summarizing its inferences, has a completely transparent process for producing novel sentences, and can learn from as few as a hundred sentences.
• Score: 4.111899441919164
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce the logical grammar emdebbing (LGE), a model inspired by pregroup grammars and categorial grammars to enable unsupervised inference of lexical categories and syntactic rules from a corpus of text. LGE produces comprehensible output summarizing its inferences, has a completely transparent process for producing novel sentences, and can learn from as few as a hundred sentences.

Related papers

• Grammar Induction from Visual, Speech and Text [91.98797120799227]
  This work introduces a novel visual-audio-text grammar induction task (textbfVAT-GI) Inspired by the fact that language grammar exists beyond the texts, we argue that the text has not to be the predominant modality in grammar induction. We propose a visual-audio-text inside-outside autoencoder (textbfVaTiora) framework, which leverages rich modal-specific and complementary features for effective grammar parsing.
  arXiv  Detail & Related papers  (2024-10-01T02:24:18Z)
• Detecting and explaining (in)equivalence of context-free grammars [0.6282171844772422]
  We propose a scalable framework for deciding, proving, and explaining (in)equivalence of context-free grammars. We present an implementation of the framework and evaluate it on large data sets collected within educational support systems.
  arXiv  Detail & Related papers  (2024-07-25T17:36:18Z)
• Ungrammatical-syntax-based In-context Example Selection for Grammatical Error Correction [8.655807096424732]
  In this paper, we propose a novel ungrammatical-syntax-based in-context example selection strategy for grammatical error correction. Specifically, we measure similarity of sentences based on their syntactic structures with diverse algorithms, and identify optimal ICL examples sharing the most similar ill-formed syntax to the test input.
  arXiv  Detail & Related papers  (2024-03-28T10:05:57Z)
• Explicit Syntactic Guidance for Neural Text Generation [45.60838824233036]
  Generative Grammar suggests that humans generate natural language texts by learning language grammar. We propose a syntax-guided generation schema, which generates the sequence guided by a constituency parse tree in a top-down direction. Experiments on paraphrase generation and machine translation show that the proposed method outperforms autoregressive baselines.
  arXiv  Detail & Related papers  (2023-06-20T12:16:31Z)
• Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistency [71.42261918225773]
  Conceptually, LOCCO can be viewed as a form of self-learning where the semantic being trained is used to generate annotations for unlabeled text. As an added bonus, the annotations produced by LOCCO can be trivially repurposed to train a neural text generation model.
  arXiv  Detail & Related papers  (2023-05-31T16:47:20Z)
• The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding [65.34601470417967]
  We describe a hybrid architecture for dialogue response generation that combines the strengths of neural language modeling and rule-based generation. Our experiments show that this system outperforms both rule-based and learned approaches in human evaluations of fluency, relevance, and truthfulness.
  arXiv  Detail & Related papers  (2022-09-16T09:00:49Z)
• Learning grammar with a divide-and-concur neural network [4.111899441919164]
  We implement a divide-and-concur iterative projection approach to context-free grammar inference. Our method requires a relatively small number of discrete parameters, making the inferred grammar directly interpretable.
  arXiv  Detail & Related papers  (2022-01-18T22:42:43Z)
• Dependency Induction Through the Lens of Visual Perception [81.91502968815746]
  We propose an unsupervised grammar induction model that leverages word concreteness and a structural vision-based to jointly learn constituency-structure and dependency-structure grammars. Our experiments show that the proposed extension outperforms the current state-of-the-art visually grounded models in constituency parsing even with a smaller grammar size.
  arXiv  Detail & Related papers  (2021-09-20T18:40:37Z)
• 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)
• SLM: Learning a Discourse Language Representation with Sentence Unshuffling [53.42814722621715]
  We introduce Sentence-level Language Modeling, a new pre-training objective for learning a discourse language representation. We show that this feature of our model improves the performance of the original BERT by large margins.
  arXiv  Detail & Related papers  (2020-10-30T13:33:41Z)

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.