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Register a new userWhat Makes a Concept Complex? Measuring Conceptual Complexity as a Precursor for Text Simplification
ما الذي يجعل مجمع مفهوم؟قياس التعقيد المفاهيمي كسلائف تبسيط النص
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كانت التطورات في مجال تبسيط النص (TS) في المقام الأول ضمن تبسيط النحوية أو المعجمية.ومع ذلك، فقد تم تحديد التبسيط المفاهيمي سابقا كحقل آخر من TS له القدرة على تحسين فهم القراءة بشكل كبير.الخطوة الأولى للقياس التبسيط المفاهيمي هو تصنيف المفاهيم كمعقد أو بسيط.تقترح ورقة البحث في البحث هذه تعريفا جديدا للتعقيد المفاهيمي إلى جانب نهج بسيط لتعلم الآلات التي تنفذ مهمة تصنيف ثنائية للتمييز بين المفاهيم البسيطة والمعقدة.يقترح أن تكون هذه الخطوة الأولى عند تطوير نماذج تبسيط نص جديدة تعمل على مستوى مفاهيمي.
Advancements within the field of text simplification (TS) have primarily been within syntactic or lexical simplification. However, conceptual simplification has previously been identified as another field of TS that has the potential to significantly improve reading comprehension. A first step to measuring conceptual simplification is the classification of concepts as either complex or simple. This research-in-progress paper proposes a new definition of conceptual complexity alongside a simple machine-learning approach that performs a binary classification task to distinguish between simple and complex concepts. It is proposed that this be a first step when developing new text simplification models that operate on a conceptual level.
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Sentence-level text simplification is currently evaluated using both automated metrics and human evaluation. For automatic evaluation, a combination of metrics is usually employed to evaluate different aspects of the simplification. Flesch-Kincaid Gr
ade Level (FKGL) is one metric that has been regularly used to measure the readability of system output. In this paper, we argue that FKGL should not be used to evaluate text simplification systems. We provide experimental analyses on recent system output showing that the FKGL score can easily be manipulated to improve the score dramatically with only minor impact on other automated metrics (BLEU and SARI). Instead of using FKGL, we suggest that the component statistics, along with others, be used for posthoc analysis to understand system behavior.
The quality of fully automated text simplification systems is not good enough for use in real-world settings; instead, human simplifications are used. In this paper, we examine how to improve the cost and quality of human simplifications by leveragin
g crowdsourcing. We introduce a graph-based sentence fusion approach to augment human simplifications and a reranking approach to both select high quality simplifications and to allow for targeting simplifications with varying levels of simplicity. Using the Newsela dataset (Xu et al., 2015) we show consistent improvements over experts at varying simplification levels and find that the additional sentence fusion simplifications allow for simpler output than the human simplifications alone.
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ultiple sentences. Based on Wikipedia dumps, we first construct a large-scale dataset named D-Wikipedia and perform analysis and human evaluation on it to show that the dataset is reliable. Then, we propose a new automatic evaluation metric called D-SARI that is more suitable for the document-level simplification task. Finally, we select several representative models as baseline models for this task and perform automatic evaluation and human evaluation. We analyze the results and point out the shortcomings of the baseline models.
The complexity loss paradox, which posits that individuals suffering from disease exhibit surprisingly predictable behavioral dynamics, has been observed in a variety of both human and animal physiological systems. The recent advent of online text-ba
sed therapy presents a new opportunity to analyze the complexity loss paradox in a novel operationalization: linguistic complexity loss in text-based therapy conversations. In this paper, we analyze linguistic complexity correlates of mental health in the online therapy messages sent between therapists and 7,170 clients who provided 30,437 corresponding survey responses on their anxiety. We found that when clients reported more anxiety, they showed reduced lexical diversity as estimated by the moving average type-token ratio. Therapists, on the other hand, used language of higher reading difficulty, syntactic complexity, and age of acquisition when clients were more anxious. Finally, we found that clients, and to an even greater extent, therapists, exhibited consistent levels of many linguistic complexity measures. These results demonstrate how linguistic analysis of text-based communication can be leveraged as a marker for anxiety, an exciting prospect in a time of both increased online communication and increased mental health issues.
Recently, a large pre-trained language model called T5 (A Unified Text-to-Text Transfer Transformer) has achieved state-of-the-art performance in many NLP tasks. However, no study has been found using this pre-trained model on Text Simplification. Th
erefore in this paper, we explore the use of T5 fine-tuning on Text Simplification combining with a controllable mechanism to regulate the system outputs that can help generate adapted text for different target audiences. Our experiments show that our model achieves remarkable results with gains of between +0.69 and +1.41 over the current state-of-the-art (BART+ACCESS). We argue that using a pre-trained model such as T5, trained on several tasks with large amounts of data, can help improve Text Simplification.
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