RSTGen: Imbuing Fine-Grained Interpretable Control into Long-FormText Generators

• URL: http://arxiv.org/abs/2205.12590v1
• Date: Wed, 25 May 2022 09:06:04 GMT
• Title: RSTGen: Imbuing Fine-Grained Interpretable Control into Long-FormText Generators
• Authors: Rilwan A. Adewoyin, Ritabrata Dutta, Yulan He
• Abstract summary: RSTGen is a framework that controls the discourse structure, semantics and topics of generated text. We demonstrate our model's ability to control structural discourse and semantic features of generated text in open generation evaluation.
• Score: 26.27412809287025
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper, we study the task of improving the cohesion and coherence of long-form text generated by language models. To this end, we propose RSTGen, a framework that utilises Rhetorical Structure Theory (RST), a classical language theory, to control the discourse structure, semantics and topics of generated text. Firstly, we demonstrate our model's ability to control structural discourse and semantic features of generated text in open generation evaluation. Then we experiment on the two challenging long-form text tasks of argument generation and story generation. Evaluation using automated metrics and a metric with high correlation to human evaluation, shows that our model performs competitively against existing models, while offering significantly more controls over generated text than alternative methods.

Related papers

• Personalized Text Generation with Fine-Grained Linguistic Control [9.668216418094316]
  We focus on controlling fine-grained attributes spanning multiple linguistic dimensions. We introduce a novel benchmark to train generative models and evaluate their ability to generate personalized text.
  arXiv  Detail & Related papers  (2024-02-07T14:41:08Z)
• Instruct-SCTG: Guiding Sequential Controlled Text Generation through Instructions [42.67608830386934]
  Instruct-SCTG is a sequential framework that harnesses instruction-tuned language models to generate structurally coherent text. Our framework generates articles in a section-by-section manner, aligned with the desired human structure using natural language instructions.
  arXiv  Detail & Related papers  (2023-12-19T16:20:49Z)
• Sequentially Controlled Text Generation [97.22539956688443]
  GPT-2 generates sentences that are remarkably human-like, longer documents can ramble and do not follow human-like writing structure. We study the problem of imposing structure on long-range text. We develop a sequential controlled text generation pipeline with generation and editing.
  arXiv  Detail & Related papers  (2023-01-05T21:23:51Z)
• Model Criticism for Long-Form Text Generation [113.13900836015122]
  We apply a statistical tool, model criticism in latent space, to evaluate the high-level structure of generated text. We perform experiments on three representative aspects of high-level discourse -- coherence, coreference, and topicality. We find that transformer-based language models are able to capture topical structures but have a harder time maintaining structural coherence or modeling coreference.
  arXiv  Detail & Related papers  (2022-10-16T04:35:58Z)
• Data-to-text Generation with Variational Sequential Planning [74.3955521225497]
  We consider the task of data-to-text generation, which aims to create textual output from non-linguistic input. We propose a neural model enhanced with a planning component responsible for organizing high-level information in a coherent and meaningful way. We infer latent plans sequentially with a structured variational model, while interleaving the steps of planning and generation.
  arXiv  Detail & Related papers  (2022-02-28T13:17:59Z)
• How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN [63.79300884115027]
  Current language models can generate high-quality text. Are they simply copying text they have seen before, or have they learned generalizable linguistic abstractions? We introduce RAVEN, a suite of analyses for assessing the novelty of generated text.
  arXiv  Detail & Related papers  (2021-11-18T04:07:09Z)
• Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence [59.51720326054546]
  We propose a long text generation model, which can represent the prefix sentences at sentence level and discourse level in the decoding process. Our model can generate more coherent texts than state-of-the-art baselines.
  arXiv  Detail & Related papers  (2021-05-19T07:29:08Z)
• Outline to Story: Fine-grained Controllable Story Generation from Cascaded Events [39.577220559911055]
  We propose a new task named "Outline to Story" (O2S) as a test bed for fine-grained controllable generation of long text. We then create datasets for future benchmarks, built by state-of-the-art keyword extraction techniques.
  arXiv  Detail & Related papers  (2021-01-04T08:16:21Z)
• Unsupervised Text Generation by Learning from Search [86.51619839836331]
  TGLS is a novel framework to unsupervised Text Generation by Learning. We demonstrate the effectiveness of TGLS on two real-world natural language generation tasks, paraphrase generation and text formalization.
  arXiv  Detail & Related papers  (2020-07-09T04:34:48Z)
• Syntax-driven Iterative Expansion Language Models for Controllable Text Generation [2.578242050187029]
  We propose a new paradigm for introducing a syntactic inductive bias into neural text generation. Our experiments show that this paradigm is effective at text generation, with quality between LSTMs and Transformers, and comparable diversity.
  arXiv  Detail & Related papers  (2020-04-05T14:29:40Z)

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.