Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen

The curse of knowledge can impede communication between experts and laymen. We propose a new task of expertise style transfer and contribute a manually annotated dataset with the goal of alleviating such cognitive biases. Solving this task not only simplifies the professional language, but also improves the accuracy and expertise level of laymen descriptions using simple words. This is a challenging task, unaddressed in previous work, as it requires the models to have expert intelligence in order to modify text with a deep understanding of domain knowledge and structures. We establish the benchmark performance of five state-of-the-art models for style transfer and text simplification. The results demonstrate a significant gap between machine and human performance. We also discuss the challenges of automatic evaluation, to provide insights into future research directions. The dataset is publicly available at this https URL.

[1]  Tat-Seng Chua,et al.  Recent Advances in Neural Question Generation , 2019, ArXiv.

[2]  Pierre Zweigenbaum,et al.  Paraphrase Acquisition from Comparable Medical Corpora of Specialized and Lay Texts , 2008, AMIA.

[3]  Lucia Specia,et al.  Unsupervised Lexical Simplification for Non-Native Speakers , 2016, AAAI.

[4]  R. P. Fishburne,et al.  Derivation of New Readability Formulas (Automated Readability Index, Fog Count and Flesch Reading Ease Formula) for Navy Enlisted Personnel , 1975 .

[5]  Anirban Laha,et al.  Unsupervised Neural Text Simplification , 2018, ACL.

[6]  Yulia Tsvetkov,et al.  Style Transfer Through Back-Translation , 2018, ACL.

[7]  Ralph Grishman,et al.  Paraphrasing for Style , 2012, COLING.

[8]  Guillaume Lample,et al.  Multiple-Attribute Text Rewriting , 2018, ICLR.

[9]  Tomas Mikolov,et al.  Bag of Tricks for Efficient Text Classification , 2016, EACL.

[10]  Emiel Krahmer,et al.  Sentence Simplification by Monolingual Machine Translation , 2012, ACL.

[11]  Cícero Nogueira dos Santos,et al.  Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer , 2018, ACL.

[12]  Percy Liang,et al.  Delete, Retrieve, Generate: a Simple Approach to Sentiment and Style Transfer , 2018, NAACL.

[13]  George Loewenstein,et al.  The Curse of Knowledge in Economic Settings: An Experimental Analysis , 1989, Journal of Political Economy.

[14]  Ari Rappoport,et al.  Simple and Effective Text Simplification Using Semantic and Neural Methods , 2018, ACL.

[15]  Chris Callison-Burch,et al.  Optimizing Statistical Machine Translation for Text Simplification , 2016, TACL.

[16]  Mirella Lapata,et al.  Learning to Simplify Sentences with Quasi-Synchronous Grammar and Integer Programming , 2011, EMNLP.

[17]  Houfeng Wang,et al.  Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach , 2018, ACL.

[18]  R. Gunning.  The Technique of Clear Writing. , 1968 .

[19]  Zhiyuan Liu,et al.  Low-Resource Name Tagging Learned with Weakly Labeled Data , 2019, EMNLP.

[20]  Salim Roukos,et al.  Bleu: a Method for Automatic Evaluation of Machine Translation , 2002, ACL.

[21]  Eric P. Xing,et al.  Toward Controlled Generation of Text , 2017, ICML.

[22]  Xu Chen,et al.  Bridge Text and Knowledge by Learning Multi-Prototype Entity Mention Embedding , 2017, ACL.

[23]  Chris Callison-Burch,et al.  Problems in Current Text Simplification Research: New Data Can Help , 2015, TACL.

[24]  Ye Zhang,et al.  SHAPED: Shared-Private Encoder-Decoder for Text Style Adaptation , 2018, NAACL.

[25]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[26]  Mirella Lapata,et al.  Sentence Simplification with Deep Reinforcement Learning , 2017, EMNLP.

[27]  Alexander M. Rush,et al.  Adversarially Regularized Autoencoders , 2017, ICML.

[28]  M. Coleman,et al.  A computer readability formula designed for machine scoring. , 1975 .

[29]  Iryna Gurevych,et al.  A Monolingual Tree-based Translation Model for Sentence Simplification , 2010, COLING.

[30]  Jonas Mueller,et al.  IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation , 2019, EMNLP/IJCNLP.

[31]  Luca Soldaini.  QuickUMLS: a fast, unsupervised approach for medical concept extraction , 2016 .

[32]  Matthew Shardlow,et al.  Neural Text Simplification of Clinical Letters with a Domain Specific Phrase Table , 2019, ACL.

[33]  Harsh Jhamtani,et al.  Shakespearizing Modern Language Using Copy-Enriched Sequence to Sequence Models , 2017, Proceedings of the Workshop on Stylistic Variation.

[34]  Enhong Chen,et al.  Style Transfer as Unsupervised Machine Translation , 2018, ArXiv.

[35]  Dongyan Zhao,et al.  Style Transfer in Text: Exploration and Evaluation , 2017, AAAI.

[36]  Lei Li,et al.  Rethinking Text Attribute Transfer: A Lexical Analysis , 2019, INLG.

[37]  Xuanjing Huang,et al.  Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation , 2019, ACL.

[38]  Christoph Lofi,et al.  Evaluating Neural Text Simplification in the Medical Domain , 2019, WWW.

[39]  Alexander M. Rush,et al.  OpenNMT: Open-Source Toolkit for Neural Machine Translation , 2017, ACL.

[40]  Joel R. Tetreault,et al.  Dear Sir or Madam, May I Introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer , 2018, NAACL.

[41]  Yu Cheng,et al.  Domain Adaptive Text Style Transfer , 2019, EMNLP.

[42]  David Kauchak,et al.  Improving Consumer Understanding of Medical Text: Development and Validation of a New SubSimplify Algorithm to Automatically Generate Term Explanations in English and Spanish , 2018, Journal of medical Internet research.

[43]  Xu Sun,et al.  Learning Sentiment Memories for Sentiment Modification without Parallel Data , 2018, EMNLP.

[44]  Xu Chen,et al.  Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision , 2018, EMNLP.

[45]  Siobhan Devlin,et al.  Simplifying Text for Language-Impaired Readers , 1999, EACL.

[46]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[47]  Goran Glavas,et al.  Simplifying Lexical Simplification: Do We Need Simplified Corpora? , 2015, ACL.

[48]  Daphne Koller,et al.  Sentence Simplification for Semantic Role Labeling , 2008, ACL.

[49]  Zhiyuan Liu,et al.  Neural Collective Entity Linking , 2018, COLING.

[50]  Nadee Goonawardene,et al.  Internet Health Information Seeking and the Patient-Physician Relationship: A Systematic Review , 2017, Journal of medical Internet research.

[51]  Olivier Bodenreider,et al.  The Unified Medical Language System (UMLS): integrating biomedical terminology , 2004, Nucleic Acids Res..

[52]  Lili Mou,et al.  Disentangled Representation Learning for Non-Parallel Text Style Transfer , 2018, ACL.

[53]  Ari Rappoport,et al.  BLEU is Not Suitable for the Evaluation of Text Simplification , 2018, EMNLP.

[54]  David Kauchak,et al.  Learning to Simplify Sentences Using Wikipedia , 2011, Monolingual@ACL.

[55]  Rico Sennrich,et al.  Controlling Politeness in Neural Machine Translation via Side Constraints , 2016, NAACL.

[56]  Regina Barzilay,et al.  Style Transfer from Non-Parallel Text by Cross-Alignment , 2017, NIPS.

[57]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[58]  Raman Chandrasekar,et al.  Automatic induction of rules for text simplification , 1997, Knowl. Based Syst..