ARTIST: ARTificial Intelligence for Simplified Text

• URL: http://arxiv.org/abs/2308.13458v1
• Date: Fri, 25 Aug 2023 16:06:06 GMT
• Title: ARTIST: ARTificial Intelligence for Simplified Text
• Authors: Lorenzo Corti and Jie Yang
• Abstract summary: Text Simplification is a key Natural Language Processing task that aims for reducing the linguistic complexity of a text. Recent advances in Generative Artificial Intelligence (AI) have enabled automatic text simplification both on the lexical and syntactical levels.
• Score: 5.095775294664102
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Complex text is a major barrier for many citizens when accessing public information and knowledge. While often done manually, Text Simplification is a key Natural Language Processing task that aims for reducing the linguistic complexity of a text while preserving the original meaning. Recent advances in Generative Artificial Intelligence (AI) have enabled automatic text simplification both on the lexical and syntactical levels. However, as applications often focus on English, little is understood about the effectiveness of Generative AI techniques on low-resource languages such as Dutch. For this reason, we carry out empirical studies to understand the benefits and limitations of applying generative technologies for text simplification and provide the following outcomes: 1) the design and implementation for a configurable text simplification pipeline that orchestrates state-of-the-art generative text simplification models, domain and reader adaptation, and visualisation modules; 2) insights and lessons learned, showing the strengths of automatic text simplification while exposing the challenges in handling cultural and commonsense knowledge. These outcomes represent a first step in the exploration of Dutch text simplification and shed light on future endeavours both for research and practice.

Related papers

• Exploring Large Language Models to generate Easy to Read content [1.474723404975345]
  Easy to Read and Plain Language guidelines aim to simplify complex texts. Standardizing these guidelines remains challenging and often involves manual processes. This work presents an exploratory investigation into leveraging Artificial Intelligence (AI) and Natural Language Processing (NLP) approaches to systematically simplify Spanish texts into Easy to Read formats.
  arXiv  Detail & Related papers  (2024-07-29T14:30:39Z)
• ARTiST: Automated Text Simplification for Task Guidance in Augmented Reality [11.23591724305816]
  ARTiST is an automatic text simplification system that uses a few-shot prompt and GPT-3 models to optimize the text length and semantic content for augmented reality. Results from a 16-user empirical study showed that ARTiST lightens the cognitive load and improves performance significantly over both unmodified text and text modified via traditional methods.
  arXiv  Detail & Related papers  (2024-02-29T01:58:49Z)
• Text2Data: Low-Resource Data Generation with Textual Control [104.38011760992637]
  Natural language serves as a common and straightforward control signal for humans to interact seamlessly with machines. We propose Text2Data, a novel approach that utilizes unlabeled data to understand the underlying data distribution through an unsupervised diffusion model. It undergoes controllable finetuning via a novel constraint optimization-based learning objective that ensures controllability and effectively counteracts catastrophic forgetting.
  arXiv  Detail & Related papers  (2024-02-08T03:41:39Z)
• Automatic and Human-AI Interactive Text Generation [27.05024520190722]
  This tutorial aims to provide an overview of the state-of-the-art natural language generation research. Text-to-text generation tasks are more constrained in terms of semantic consistency and targeted language styles.
  arXiv  Detail & Related papers  (2023-10-05T20:26:15Z)
• Multilingual Simplification of Medical Texts [49.469685530201716]
  We introduce MultiCochrane, the first sentence-aligned multilingual text simplification dataset for the medical domain in four languages. We evaluate fine-tuned and zero-shot models across these languages, with extensive human assessments and analyses. Although models can now generate viable simplified texts, we identify outstanding challenges that this dataset might be used to address.
  arXiv  Detail & Related papers  (2023-05-21T18:25:07Z)
• Elaborative Simplification as Implicit Questions Under Discussion [51.17933943734872]
  This paper proposes to view elaborative simplification through the lens of the Question Under Discussion (QUD) framework. We show that explicitly modeling QUD provides essential understanding of elaborative simplification and how the elaborations connect with the rest of the discourse.
  arXiv  Detail & Related papers  (2023-05-17T17:26:16Z)
• Automatic Lexical Simplification for Turkish [0.0]
  We present the first automatic lexical simplification system for the Turkish language. Recent text simplification efforts rely on manually crafted simplified corpora and comprehensive NLP tools. We present a new text simplification pipeline based on pretrained representation model BERT together with morphological features to generate grammatically correct and semantically appropriate word-level simplifications.
  arXiv  Detail & Related papers  (2022-01-15T15:58:44Z)
• Elaborative Simplification: Content Addition and Explanation Generation in Text Simplification [33.08519864889526]
  We present the first data-driven study of content addition in text simplification. We analyze how entities, ideas, and concepts are elaborated through the lens of contextual specificity. Our results illustrate the complexities of elaborative simplification, suggesting many interesting directions for future work.
  arXiv  Detail & Related papers  (2020-10-20T05:06:23Z)
• A Survey of Knowledge-Enhanced Text Generation [81.24633231919137]
  The goal of text generation is to make machines express in human language. Various neural encoder-decoder models have been proposed to achieve the goal by learning to map input text to output text. To address this issue, researchers have considered incorporating various forms of knowledge beyond the input text into the generation models.
  arXiv  Detail & Related papers  (2020-10-09T06:46:46Z)
• Improving Disentangled Text Representation Learning with Information-Theoretic Guidance [99.68851329919858]
  discrete nature of natural language makes disentangling of textual representations more challenging. Inspired by information theory, we propose a novel method that effectively manifests disentangled representations of text. Experiments on both conditional text generation and text-style transfer demonstrate the high quality of our disentangled representation.
  arXiv  Detail & Related papers  (2020-06-01T03:36:01Z)
• TRIE: End-to-End Text Reading and Information Extraction for Document Understanding [56.1416883796342]
  We propose a unified end-to-end text reading and information extraction network. multimodal visual and textual features of text reading are fused for information extraction. Our proposed method significantly outperforms the state-of-the-art methods in both efficiency and accuracy.
  arXiv  Detail & Related papers  (2020-05-27T01:47:26Z)

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.