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AI-Based Syntactic Complexity Metrics and Sight Interpreting Performance

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Intelligent Human Computer Interaction (IHCI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13184))

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Abstract

Complex syntax may lead to increased cognitive effort during translation. However, it is unclear what kinds of syntactic complexity have a stronger impact on translation performance. In this paper, we employ several syntactic metrics which enable us to explore the impact of syntactic complexity on the quality in English-to-Chinese sight interpreting. We have operationalized syntactic complexity by six metrics, namely, Incomplete Dependency Theory metric (IDT), Dependency Locality Theory metric (DLT), Combined IDT and DLT metric (IDT+DLT), Left Embeddedness metric (LE), Nested Nouns Distancemetric (NND), and Bilingual Complexity Ratio metric (BRC). Three professional translators have manually annotated translation errors using MQM-derived error taxonomies, which includes accuracy, fluency, and style errors, each as critical or minor errors. We assessed inter-rater agreement by adopting weighted Fleiss’ Kappa scores. We found that there are strong correlations between the IDT and IDT+DLT metrics and sight interpreting errors. We also found that language-specific syntactic differences between English and Chinese such as directions of branching and noun modifiers can have a strong influence on accuracy and critical errors.

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Notes

  1. 1.

    See https://sites.google.com/site/centretranslationinnovation/tpr-db/public-studies?authuser=0.

  2. 2.

    See https://www.taus.net/academy/news/press-release/dqf-and-mqm-harmonized-to-create-an-industry-wide-quality-standard.

  3. 3.

    See https://github.com/ContentSide/lingx.

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Author information

Authors and Affiliations

  1. Kent State University, 475 Janik Drive, Kent, OH, 44242, USA

    Longhui Zou & Michael Carl

  2. R&D Department, ContentSide, 23 Jules Favre, 69006, Lyon, France

    Mehdi Mirzapour & Hélène Jacquenet

  3. University of Bristol, Beacon House, Queens Road, Bristol, BS8 1QU, UK

    Lucas Nunes Vieira

Authors
  1. Longhui Zou
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  2. Michael Carl
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  3. Mehdi Mirzapour
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  4. Hélène Jacquenet
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  5. Lucas Nunes Vieira
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Corresponding author

Correspondence to Longhui Zou .

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Editors and Affiliations

  1. Kent State University, Kent, OH, USA

    Jong-Hoon Kim

  2. University of Tartu, Tartu, Estonia

    Madhusudan Singh

  3. Kent State University, Kent, OH, USA

    Javed Khan

  4. Indian Institute of Information Technology, Allahabad, India

    Uma Shanker Tiwary

  5. Massachusetts Institute of Technology, Cambridge, MA, USA

    Marigankar Sur

  6. Hankuk University of Foreign Studies, Seoul, Korea (Republic of)

    Dhananjay Singh

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Zou, L., Carl, M., Mirzapour, M., Jacquenet, H., Vieira, L.N. (2022). AI-Based Syntactic Complexity Metrics and Sight Interpreting Performance. In: Kim, JH., Singh, M., Khan, J., Tiwary, U.S., Sur, M., Singh, D. (eds) Intelligent Human Computer Interaction. IHCI 2021. Lecture Notes in Computer Science, vol 13184. Springer, Cham. https://doi.org/10.1007/978-3-030-98404-5_49

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  • DOI: https://doi.org/10.1007/978-3-030-98404-5_49

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