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AutoMeTS: The Autocomplete for Medical Text Simplification. ...
Abstract: The goal of text simplification (TS) is to transform difficult text into a version that is easier to understand and more broadly accessible to a wide variety of readers. In some domains, such as healthcare, fully automated approaches cannot be used since information must be accurately preserved. Instead, semi-automated approaches can be used that assist a human writer in simplifying text faster and at a higher quality. In this paper, we examine the application of autocomplete to text simplification in the medical domain. We introduce a new parallel medical data set consisting of aligned English Wikipedia with Simple English Wikipedia sentences and examine the application of pretrained neural language models (PNLMs) on this dataset. We compare four PNLMs (BERT, RoBERTa, XLNet, and GPT-2), and show how the additional context of the sentence to be simplified can be incorporated to achieve better results (6.17% absolute improvement over the best individual model). We also introduce an ensemble model that ...
Keyword: Computer and Information Science; Information and Knowledge Engineering; Intelligent System; Natural Language Processing; Neural Network
URL: https://dx.doi.org/10.48448/yfa2-s831
https://underline.io/lecture/6995-automets-the-autocomplete-for-medical-text-simplification.
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22
A Closer Look at Linguistic Knowledge in Masked Language Models: The Case of Relative Clauses in American English ...
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23
SemEval Task 6: DeftEval ...
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24
The surprising vulnerability of automatic content scoring systems to adversarial input ...
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Cross-lingual Transfer Learning for Grammatical Error Correction ...
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A Straightforward Approach to Narratologically Grounded Character Identification ...
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Part 4 - Extension Layers and Comparison to Other Formalisms ...
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An Empirical Study of the Downstream Reliability of Pre-Trained Word Embeddings ...
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Living Machines: A study of atypical animacy ...
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Effective Use of Target-side Context for Neural Machine Translation ...
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Neural Machine Translation of Artwork Titles Using Iconclass Codes ...
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32
The Financial Narrative Summarisation Shared Task (FNS 2020) ...
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Transliteration of Judeo-Arabic Texts into Arabic Script Using Recurrent Neural Networks ...
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3. Word Embeddings ...
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5. Sense Embeddings ...
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36
Answer-driven Deep Question Generation based on Reinforcement Learning ...
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Scalar vs. mereological conceptualizations of the N-BY-N and NUM-BY-NUM adverbials ...
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Automatic detection of unexpected/erroneous collocations in learner corpus ...
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AprilE: Attention with Pseudo Residual Connection for Knowledge Graph Embedding ...
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Life still goes on: Analysing Australian WW1 Diaries through Distant Reading ...
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