Leveraging Grammar Induction for Language Understanding and Generation

• URL: http://arxiv.org/abs/2410.04878v1
• Date: Mon, 7 Oct 2024 09:57:59 GMT
• Title: Leveraging Grammar Induction for Language Understanding and Generation
• Authors: Jushi Kai, Shengyuan Hou, Yusheng Huang, Zhouhan Lin,
• Abstract summary: We introduce an unsupervised grammar induction method for language understanding and generation. We construct a grammar to induce constituency structures and dependency relations, which is simultaneously trained on downstream tasks. We evaluate and apply our method to multiple machine translation tasks natural language understanding tasks.
• Score: 7.459693992079273
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Grammar induction has made significant progress in recent years. However, it is not clear how the application of induced grammar could enhance practical performance in downstream tasks. In this work, we introduce an unsupervised grammar induction method for language understanding and generation. We construct a grammar parser to induce constituency structures and dependency relations, which is simultaneously trained on downstream tasks without additional syntax annotations. The induced grammar features are subsequently incorporated into Transformer as a syntactic mask to guide self-attention. We evaluate and apply our method to multiple machine translation tasks and natural language understanding tasks. Our method demonstrates superior performance compared to the original Transformer and other models enhanced with external parsers. Experimental results indicate that our method is effective in both from-scratch and pre-trained scenarios. Additionally, our research highlights the contribution of explicitly modeling the grammatical structure of texts to neural network models.

Related papers

• On Eliciting Syntax from Language Models via Hashing [19.872554909401316]
  Unsupervised parsing aims to infer syntactic structure from raw text. In this paper, we explore the possibility of leveraging this capability to deduce parsing trees from raw text. We show that our method is effective and efficient enough to acquire high-quality parsing trees from pre-trained language models at a low cost.
  arXiv  Detail & Related papers  (2024-10-05T08:06:19Z)
• 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)
• Sentence Representation Learning with Generative Objective rather than Contrastive Objective [86.01683892956144]
  We propose a novel generative self-supervised learning objective based on phrase reconstruction. Our generative learning achieves powerful enough performance improvement and outperforms the current state-of-the-art contrastive methods.
  arXiv  Detail & Related papers  (2022-10-16T07:47:46Z)
• 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)
• 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)
• 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)
• Guiding Symbolic Natural Language Grammar Induction via Transformer-Based Sequence Probabilities [0.0]
  A novel approach to automated learning of syntactic rules governing natural languages is proposed. This method exploits the learned linguistic knowledge in transformers, without any reference to their inner representations. We show a proof-of-concept example of our proposed technique, using it to guide unsupervised symbolic link-grammar induction methods.
  arXiv  Detail & Related papers  (2020-05-26T06:18:47Z)
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer [64.22926988297685]
  Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP) In this paper, we explore the landscape of introducing transfer learning techniques for NLP by a unified framework that converts all text-based language problems into a text-to-text format.
  arXiv  Detail & Related papers  (2019-10-23T17:37:36Z)

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.