Text Simplification for Comprehension-based Question-Answering (2021)

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.

## introduction text simplification is the task of modifying the structure of a text to make it easier to read and comprehend while preserving the content and approximating the original meaning. linguistically, simple sentences are defined as having only one subject and one verb or predicate. therefore, a complex sentence can be rewritten into multiple simpler sentences while retaining the same meaning. when simplifying texts, a myriad of rewriting transformations have been explored, ranging from replacing complex words or phrases for simpler synonyms, to changing the syntactic structure of the sentence. moreover, modern automated text simplification approaches are data-driven, attempting to simplify sentences using parallel corpora of aligned complex-simplified sentences. text simplification allows humans to read texts more efficiently and faster, empowering them to take part in society and become more active in their daily actions and healthcare @xcite . another vital application of text simplification is reading assistance it provides, especially for people with reading disabilities @xcite , low-literacy readers @xcite , or non-native speakers @xcite . there has been considerable work done where nlp applications exploit the benefits of text simplification. long sentences with complex syntax or those laden with long-distance dependencies often pose difficulties for various downstream tasks. text simplification has been used to improve the performance for these tasks including machine translation @xcite , summarization @xcite , semantic role labeling @xcite , and information extraction @xcite . this opens up the avenue for exploring simplification for question-answering. to the best of our knowledge, text simplification for comprehension-based question-answering has not been explored yet. in this work, we make the following contributions:- ## benchmarking experiments this section highlights the models and experiments performed for benchmarking the simple-squad. we compare the results obtained for simple-squad against the squad dataset. ## conclusion in this work, we study the effect of text simplification in the comprehension based questionanswering downstream task using the squad dataset. for simple-squad corpus creation, we use a transformers based style transfer model to transfer complex sentences to sequences of simple sentences while retaining the original meaning. we further use post-editing techniques to reduce noise from the dataset, followed by the use of heuristics to find required offsets for an answer in each question-answer pair. we prove the efficacy of our model using automated evaluation as well as human evaluation. we then benchmark simple-squad using two different variants of roberta and perform an ablation study to investigate the effects of text simplification using four different variations of input. we prove that text simplification in the question-answering downstream task increases the predictive performance of the models. we further conduct edit-type analysis and sentencelength analysis to give insights about the transfer process. future work may include improving style transfer performance using a more extensive corpus for text simplification and exploring effects of text simplification for other downstream tasks like text summarization, sentiment analysis.