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Smart subtitles for vocabulary learning

Published:26 April 2014Publication History

ABSTRACT

Language learners often use subtitled videos to help them learn. However, standard subtitles are geared more towards comprehension than vocabulary learning, as translations are nonliteral and are provided only for phrases, not vocabulary. This paper presents Smart Subtitles, which are interactive subtitles tailored towards vocabulary learning. Smart Subtitles can be automatically generated from common video sources such as subtitled DVDs. They provide features such as vocabulary definitions on hover, and dialog-based video navigation. In our pilot study with intermediate learners studying Chinese, participants correctly defined over twice as many new words in a post-viewing vocabulary test when they used Smart Subtitles, compared to dual Chinese-English subtitles. Learners spent the same amount of time watching clips with each tool, and enjoyed viewing videos with Smart Subtitles as much as with dual subtitles. Learners understood videos equally well using either tool, as indicated by self-assessments and independent evaluations of their summaries.

References

  1. Bansal, M., DeNero, J., and Lin, D. Unsupervised translation sense clustering. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics (2012), 773--782. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Bianchi, F., and Ciabattoni, T. Captions and Subtitles in EFL Learning: an investigative study in a comprehensive computer environment. EUT-Edizioni Università di Trieste (2008).Google ScholarGoogle Scholar
  3. Bird, S., Klein, E., and Loper, E. Natural Language Processing with Python. O'Reilly, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Breen, J. WWWJDIC - A Feature-Rich WWW-Based Japanese Dictionary. eLEX2009 (2009), 31.Google ScholarGoogle Scholar
  5. Danan, M. Reversed subtitling and dual coding theory: New directions for foreign language instruction. Language Learning 42, 4 (1992), 497--527.Google ScholarGoogle ScholarCross RefCross Ref
  6. Danan, M. Captioning and subtitling: Undervalued language learning strategies. Meta: Translators? Journal 49, 1 (2004), 67--77.Google ScholarGoogle Scholar
  7. Dummitt, N. Chinese Through Tone and Color. Hippocrene Books, 2008.Google ScholarGoogle Scholar
  8. d'Ydewalle, G. Foreign-language acquisition by watching subtitled television programs. Journal of Foreign Language Education and Research 12 (2002), 59--77.Google ScholarGoogle Scholar
  9. Fukunaga, N. "Those anime students": Foreign language literacy development through Japanese popular culture. Journal of Adolescent & Adult Literacy 50, 3 (2006), 206--222.Google ScholarGoogle ScholarCross RefCross Ref
  10. Harris, C., and Stephens, M. A combined corner and edge detector. In Alvey vision conference, vol. 15, Manchester, UK (1988), 50.Google ScholarGoogle Scholar
  11. Herron, C. An investigation of the effectiveness of using an advance organizer to introduce video in the foreign language classroom. The Modern Language Journal 78, 2 (1994), 190--198.Google ScholarGoogle ScholarCross RefCross Ref
  12. Katsamanis, A., Black, M., Georgiou, P. G., Goldstein, L., and Narayanan, S. SailAlign: Robust long speech-text alignment. In Proc. of Workshop on New Tools and Methods for Very-Large Scale Phonetics Research (2011).Google ScholarGoogle Scholar
  13. Koolstra, C. M., Peeters, A. L., and Spinhof, H. The pros and cons of dubbing and subtitling. European Journal of Communication 17, 3 (2002), 325--354.Google ScholarGoogle ScholarCross RefCross Ref
  14. Krippendorff, K. Computing Krippendorff's alpha reliability. Departmental Papers (ASC) (2007), 43.Google ScholarGoogle Scholar
  15. Kurohashi, S., Nakamura, T., Matsumoto, Y., and Nagao, M. Improvements of Japanese morphological analyzer JUMAN. In Proceedings of The International Workshop on Sharable Natural Language (1994), 22--28.Google ScholarGoogle Scholar
  16. Lee, D.-S. Effective gaussian mixture learning for video background subtraction. Pattern Analysis and Machine Intelligence, IEEE Transactions on 27, 5 (2005), 827--832. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. MDBG. CC-CEDICT Chinese-English Dictionary. MDBG (2013).Google ScholarGoogle Scholar
  18. Microsoft. Microsoft Office OneNote 2010. Microsoft (2010).Google ScholarGoogle Scholar
  19. Mitterer, H., and McQueen, J. M. Foreign subtitles help but native-language subtitles harm foreign speech perception. PloS one 4, 11 (2009), e7785.Google ScholarGoogle Scholar
  20. Raine, P. Incidental Learning of Vocabulary through Authentic Subtitled Videos. JALT - The Japan Association for Language Teaching (2012).Google ScholarGoogle Scholar
  21. Sakunkoo, N., and Sakunkoo, P. GliFlix: Using Movie Subtitles for Language Learning. In UIST 2013 Adjunct, ACM (2009).Google ScholarGoogle Scholar
  22. Secules, T., Herron, C., and Tomasello, M. The effect of video context on foreign language learning. The Modern Language Journal 76, 4 (1992), 480--490.Google ScholarGoogle ScholarCross RefCross Ref
  23. Shea, P. Leveling the playing field: A study of captioned interactive video for second language learning. Journal of Educational Computing Research 22, 3 (2000), 243--264.Google ScholarGoogle ScholarCross RefCross Ref
  24. Smith, R. An overview of the Tesseract OCR engine. In Document Analysis and Recognition, 2007. ICDAR 2007. Ninth International Conference on, vol. 2, IEEE (2007), 629--633. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Speed, E. Rikaikun. Google Chrome Web Store (2013).Google ScholarGoogle Scholar
  26. Tseng, H., Chang, P., Andrew, G., Jurafsky, D., and Manning, C. A Conditional Random Field Word Segmenter for SIGHAN bakeoff 2005. In Proceedings of the Fourth SIGHAN Workshop on Chinese Language Processing, vol. 171, Jeju Island, Korea (2005).Google ScholarGoogle Scholar
  27. W3C. WebVTT: The Web Video Text Tracks Format. W3C (2013).Google ScholarGoogle Scholar
  28. Wesche, M., and Paribakht, T. S. Assessing Second Language Vocabulary Knowledge: Depth Versus Breadth. Canadian Modern Language Review 53, 1 (1996), 13--40.Google ScholarGoogle Scholar
  29. Zesch, T., Müller, C., and Gurevych, I. Extracting Lexical Semantic Knowledge from Wikipedia and Wiktionary. In LREC, vol. 8 (2008), 1646--1652.Google ScholarGoogle Scholar
  30. Zuggy, B. SubRip (2011).Google ScholarGoogle Scholar

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      cover image ACM Conferences
      CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
      April 2014
      4206 pages
      ISBN:9781450324731
      DOI:10.1145/2556288

      Copyright © 2014 ACM

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      Publication History

      • Published: 26 April 2014

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      CHI '14 Paper Acceptance Rate465of2,043submissions,23%Overall Acceptance Rate6,199of26,314submissions,24%

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