Assessing Relative Sentence Complexity using an Incremental CCG Parser

By Bharat Ram Ambati, Siva Reddy, Mark Steedman. 2016

38 citations recorded by the source corpus.

Given a pair of sentences, we present computational models to assess if one sentence is simpler to read than the other. While existing models explored the usage of phrase structure features using a non-incremental parser, experimental evidence suggests that the human language processor works incrementally. We empirically evaluate if syntactic features from an incremental CCG parser are more useful than features from a non-incremental phrase structure parser. Our evaluation on Simple and Standard Wikipedia sentence pairs suggests that incremental CCG features are indeed more useful than phrase structure features achieving 0.44 points gain in performance. Incremental CCG parser also gives significant improvements in speed (12 times faster) in comparison to the phrase structure parser. Furthermore, with the addition of psycholinguistic features, we achieve the strongest result to date reported on this task. Our code and data can be downloaded from https://github. com/bharatambati/sent-compl.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text (2022, methodology): “…for tasks like simplification is the readability of text, which could be measured using scales such as the ones proposed by Kincaid et al. (1975) or Ambati et al. (2016). van der Lee et al. point out that the relationship between these objective measures and subjective readability assessments is…”
OneStopEnglish corpus: A new corpus for automatic readability assessment and text simplification (2018, methodology): “Texts from this source were previously used for training sentence level readability models (Vajjala and Meurers, 2016; Ambati et al., 2016; Howcroft and Demberg, 2017), for performing corpus analysis about the characteristics of simplified text (Allen, 2009), and in user studies about the relationsh”
Best practices for the human evaluation of automatically generated text (2019, background): “It is also conceivable that these criteria can be assessed using more objective measures, similar to existing readability measures (e.g., Ambati et al., 2016; Kincaid et al., 1975; Pitler and Nenkova, 2008; Vajjala and Meurers, 2014), where objective text metrics (e.g. average word length, average…”