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Content Provider | ACM Digital Library |
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Author | Bigham, Jeffrey P. Rello, Luz Lasecki, Walter S. |
Abstract | Text can often be complex and difficult to read, especially for people with cognitive impairments or low literacy skills. Text simplification is a process that reduces the complexity of both wording and structure in a sentence, while retaining its meaning. However, this is currently a challenging task for machines, and thus, providing effective on-demand text simplification to those who need it remains an unsolved problem. Even evaluating the simplicity of text remains a challenging problem for both computers, which cannot understand the meaning of text, and humans, who often struggle to agree on what constitutes a good simplification. This paper focuses on the evaluation of English text simplification using the crowd. We show that leveraging crowds can result in a collective decision that is accurate and converges to a consensus rating. Our results from 2,500 crowd annotations show that the crowd can effectively rate levels of simplicity. This may allow simplification systems and system builders to get better feedback about how well content is being simplified, as compared to standard measures which classify content into 'simplified' or 'not simplified' categories. Our study provides evidence that the crowd could be used to evaluate English text simplification, as well as to create simplified text in future work. |
Starting Page | 1 |
Ending Page | 9 |
Page Count | 9 |
File Format | |
ISBN | 9781450333429 |
DOI | 10.1145/2745555.2746658 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2015-05-18 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | Crowdsourcing Nlp Text simplification Accessibility |
Content Type | Text |
Resource Type | Article |
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