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WP4-22. Final Evaluation of Subtitle Generator

WP4-22. Final Evaluation of Subtitle Generator. Vincent Vandeghinste, Pan Yi CCL – KULeuven. Example. Transcript: Het meest spectaculaire aan de daadwerkelijke start van de euro is dat er eigenlijk niets spectaculairs te melden valt. Ondertitel:

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WP4-22. Final Evaluation of Subtitle Generator

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  1. WP4-22. Final Evaluation of Subtitle Generator Vincent Vandeghinste, Pan Yi CCL – KULeuven

  2. Example Transcript: Het meest spectaculaire aan de daadwerkelijke start van de euro is dat er eigenlijk niets spectaculairs te melden valt. Ondertitel: Het meest spectaculaire aan de start van de euro was dat er niets spectaculairs te melden valt.

  3. Flow

  4. Availability Calculator • Pronunciation Time of Input Sentence => estimate nr of characters available in subtitle • If UNKNOWN, estimate it by • counting nr of syllables • Average speaking rate for Dutch

  5. Syllable Counter • Rule-based • Evaluated on CGN-lexicon combined with FREQ-lists • Estimated nr  Nr of syl in phonetic transcripts • 99.63% of all words in CGN is correctly estimated

  6. Average Syllable Duration

  7. Availability Calculator • When pronunciation time not given: estimate it • Subtitles: 70 chars / 6 sec = 11.67 chars/sec • If nr of chars > nr of available chars => compress sentence

  8. Sentence Compressor • Parallel Corpus • Sentence Analysis • Sentence Compression • Evaluation

  9. Parallel Corpus • Sentence aligned • Source & Target corpus: • Tagging • Chunking • SSUB detection • Chunk alignment

  10. Chunk Alignment Every 4-gram from src-chnk is compared with every 4-gram from tgt-chnk A = ( m / (m+n)) . (L1 + L2)/2 If (A > 0.315) then Align Chunk F-value for NP/PP-alignment is 95%

  11. Sentence Analysis • Tagging (TnT): accuracy = 96.2% (Oostdijk et al., 2002) • Chunking

  12. Sentence Analysis (2) • SSUB detection

  13. Sentence Compression • Use of statistics • Use of rules • Word reduction • Selection of the Compressed Sentence

  14. Use of statistics

  15. Use of rules • To avoid generating ungrammatical sentences • Rules of type For every NP, never remove the head noun • Rules are applied recursively

  16. Word Reduction • Example: replace gevangenisstraf by straf • Counterexample: replace voetbal by bal • Making use of Wordbuilding module (WP2) • Introduces a lot of errors: added accuracy? • Better integration with rest of system should be possible

  17. Selection of the Compressed Sentence • All previous steps result in an ordered list of sentence alternatives • Supposedly grammatically correct • Sentences are ordered depending on their probability • First sentence (most probable) with a length smaller than available nr of chars is chosen

  18. Evaluation

  19. Subtitle Layout Generator Actieve of gewezen voetballers zoals Ruud Gullit of Dennis Bergkamp moeten het stellen met nauwelijks anderhalf miljard . wordt Actieve of gewezen voetballers zoals Ruud Gullit of Dennis Bergkamp moeten het stellen met nauwelijks anderhalf miljard .

  20. Conclusion • System approach works very well: • If sentence analysis is correct • If there are possible reductions (according to the ruleset) • A lot of No Output cases: System cannot reduce sentence • Sentence cannot be reduced (even by humans) • Rule-set is too strict / Wrong sentence analysis • Not fine-grained enough statistical info • Bad output: • Wrong sentence analysis (CONJ) • Wrong word-reductions

  21. Future • Near future (within Atranos) • Better integration of word-reduction • Combine advantages of CNTS approach and CCL approach into one approach • Far future (outside Atranos) • Better sentence analysis: full parse is needed • More fine-grained analysis of parallel corpus

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