Text Simplification for Comprehension-based Question-Answering

• URL: http://arxiv.org/abs/2109.13984v1
• Date: Tue, 28 Sep 2021 18:48:00 GMT
• Title: Text Simplification for Comprehension-based Question-Answering
• Authors: Tanvi Dadu, Kartikey Pant, Seema Nagar, Ferdous Ahmed Barbhuiya, Kuntal Dey
• Abstract summary: We release Simple-SQuAD, a simplified version of the widely-used SQuAD dataset. We benchmark the newly created corpus and perform an ablation study for examining the effect of the simplification process in the SQuAD-based question answering task.
• Score: 7.144235435987265
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text simplification is the process of splitting and rephrasing a sentence to a sequence of sentences making it easier to read and understand while preserving the content and approximating the original meaning. Text simplification has been exploited in NLP applications like machine translation, summarization, semantic role labeling, and information extraction, opening a broad avenue for its exploitation in comprehension-based question-answering downstream tasks. In this work, we investigate the effect of text simplification in the task of question-answering using a comprehension context. We release Simple-SQuAD, a simplified version of the widely-used SQuAD dataset. Firstly, we outline each step in the dataset creation pipeline, including style transfer, thresholding of sentences showing correct transfer, and offset finding for each answer. Secondly, we verify the quality of the transferred sentences through various methodologies involving both automated and human evaluation. Thirdly, we benchmark the newly created corpus and perform an ablation study for examining the effect of the simplification process in the SQuAD-based question answering task. Our experiments show that simplification leads to up to 2.04% and 1.74% increase in Exact Match and F1, respectively. Finally, we conclude with an analysis of the transfer process, investigating the types of edits made by the model, and the effect of sentence length on the transfer model.

Related papers

• 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)
• Syntactic Complexity Identification, Measurement, and Reduction Through Controlled Syntactic Simplification [0.0]
  We present a classical syntactic dependency-based approach to split and rephrase a compound and complex sentence into a set of simplified sentences. The paper also introduces an algorithm to identify and measure a sentence's syntactic complexity. This work is accepted and presented in International workshop on Learning with Knowledge Graphs (IWLKG) at WSDM-2023 Conference.
  arXiv  Detail & Related papers  (2023-04-16T13:13:58Z)
• Exploiting Summarization Data to Help Text Simplification [50.0624778757462]
  We analyzed the similarity between text summarization and text simplification and exploited summarization data to help simplify. We named these pairs Sum4Simp (S4S) and conducted human evaluations to show that S4S is high-quality.
  arXiv  Detail & Related papers  (2023-02-14T15:32:04Z)
• NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization [46.772517928718216]
  We propose a summarize-then-simplify two-stage strategy, which we call NapSS. NapSS identifies the relevant content to simplify while ensuring that the original narrative flow is preserved. Our model achieves significantly better than the seq2seq baseline on an English medical corpus.
  arXiv  Detail & Related papers  (2023-02-11T02:20:25Z)
• Text Revision by On-the-Fly Representation Optimization [76.11035270753757]
  Current state-of-the-art methods formulate these tasks as sequence-to-sequence learning problems. We present an iterative in-place editing approach for text revision, which requires no parallel data. It achieves competitive and even better performance than state-of-the-art supervised methods on text simplification.
  arXiv  Detail & Related papers  (2022-04-15T07:38:08Z)
• Document-Level Text Simplification: Dataset, Criteria and Baseline [75.58761130635824]
  We define and investigate a new task of document-level text simplification. Based on Wikipedia dumps, we first construct a large-scale dataset named D-Wikipedia. We propose a new automatic evaluation metric called D-SARI that is more suitable for the document-level simplification task.
  arXiv  Detail & Related papers  (2021-10-11T08:15:31Z)
• 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)
• Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification [13.447565774887215]
  Text simplification reduces the language complexity of professional content for accessibility purposes. End-to-end neural network models have been widely adopted to directly generate the simplified version of input text. We show that text simplification can be decomposed into a compact pipeline of tasks to ensure the transparency and explainability of the process.
  arXiv  Detail & Related papers  (2020-07-31T03:33:37Z)
• ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations [97.27005783856285]
  This paper introduces ASSET, a new dataset for assessing sentence simplification in English. We show that simplifications in ASSET are better at capturing characteristics of simplicity when compared to other standard evaluation datasets for the task.
  arXiv  Detail & Related papers  (2020-05-01T16:44:54Z)

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.