Tarek Sakakini - 2017 - MORSE: Semantic-ally Drive-n MORpheme SEgment-er
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ACL 2017 - P17-1051
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Tarek Sakakini - 2017 - MORSE: Semantic-ally Drive-n MORpheme SEgment-er
- 1. MORSE: Semantic-ally Drive-n MORpheme SEgment-er Tarek Sakakini Suma Bhat Pramod Viswanath Samuel MORSE minimized the number of on-off clicks for non-verbal communication. This MORSE minimizes the vocabulary size for Natural Language Processing systems.
- 4. Not a trivial task +ing Player Playing Beijing Butterflies s +er +s
- 5. Machine Translation Applications Quick • Rapide ly • ment Sad • Triste Sadly Tristement Quickly •Rapidement Sad •Triste Sadly ??? Model: Test: Model: Test:
- 6. Applications Information Retrieval Here at Toyota World, we have the cheapest cars in town. We are proudly called the first and last stop. … …
- 8. Letter Successor Variety (Harris, 1970) H e l p l e s s l y
- 9. Morfessor (Creutz and Lagos, 2005) Help: 2387 Helping: 1586 Helper: 498 Helps: 2437 Jump: 1847 Jumping: 1664 Jumper: 1290 Jumps: 2987
- 11. Locally Semantic car caring car cars Cosine similarity (Schone and Jurafsky, 2000) (Narasimhan et al., 2015) (Luo et al., 2017)
- 12. Distinguishing criteria car cars car cars player players runner runners goal goals play plays fine fines wheel wheels hand hands laptop laptops lab labs
- 13. MORSE3 Unsupervised Morphology Learning Input: Word Embeddings Segmentation: Optimization Problem 4 hyperparameters: Small tuning dataset
- 15. Collecting candidate morphological rules Vocabulary: jump play buy jumping playing buying jumper player buyer ….. and stand (suf, ∅, ing): (suf, ∅, er): (pre, ∅, st): (jump, jumping) (play, playing) (buy, buying) (jump, jumper) (play, player) (buy, buyer) (and, stand) (ore, store) (one, stone) (jump, jumping)(play, playing)(buy, buying) (and, stand) (Soricut and Och, 2015)
- 16. Signals Orthographic Semantic Word Embeddings quick quickly quick quickly wrong wrongly beautiful beautifully confident confidently beautiful beautifully confident confidently wrong wrongly
- 17. What makes a good rule? Signal 1: Orthography (beautiful, beautifully)(quick, quickly) (confident, confidently) Rule = (suf, ∅, ly) Size = 8723 ……………………………………… ……………………. (wrong, wrongly) Rule = (pre, ∅, st) Size= 16 (ore, store) …… (amp, stamp)
- 18. What makes a good rule? Signal 2: Semantics ore store amp stamp and stand one stone quick quickly wrong wrongly beautiful beautifully confident confidently
- 19. What makes a good member of a rule? Scope: Vocabulary-Wide quick quickly wrong wrongly beautiful beautifully confident confidently on only
- 20. What makes a good member of a rule? Scope: Local confident confidently on only
- 22. Linear Optimization Problem uncaring (caring, uncaring)(ring, uncaring) (uncare, uncaring) t1 t2 t3 t4
- 23. (care, caring)(car, caring) Iterate caring t1 t2 t3 t4 (carol, caring) un + caring
- 24. (car, care) Iterate care t1 t2 t3 t4 (ca, care) (re, care) un + care + ing
- 25. Experiments4
- 26. Experimental Setup Training Languages Gold Datasets Morpho Challenge jumping jump ing playing play ing jumps jump s calls call s rooms room s
- 28. Morpho Challenge downsides Business Turning-point Player’s Turning Non-compositional Human error Trivial instances
- 29. Experiments ◉ 2000 words ◉ Compositional ◉ 91% inter-annotator agreement ◉ In canonical (butterfly + ies) and non-canonical version (butterfl + ies) New Dataset: SD17
- 30. Results on SD17 57.31 81.01 83.96 0 10 20 30 40 50 60 70 80 90 Morfessor MORSE (tuned on MC) MORSE (tuned on SD17) F-score
- 31. Against state-of-the-art 83.96 79.9 67.4 67.14 0 10 20 30 40 50 60 70 80 90 MORSE MorphoChain Morfessor S + W Morfessor S + W+ L F-Scores
- 32. Negative Dataset ◉ 100 words like: honeymoon, passport, outdoors ◉ Checks for robustness 43 7 0 5 10 15 20 25 30 35 40 45 50 Morfessor MORSE #Segmentations
- 33. Looking forward ◉ Robustness to highly agglutinative languages ◉ Extending to other languages (non-concatenative) k a t a b ai A
- 34. Looking forward ◉ Morphological mappings across languages (suf, ∅, ly) (suf, ∅, ment) (suf, ∅, s) English French (suf, ∅, es) (suf, ∅, s)
- 38. Prerequisite Morpho-syntactic regularities in word vectors play playing jump jumping scream screaming s sing store tore scream cream smilemile slay lay Valid rule with an invalid instance Invalid rule (suf, ∅, ing) (s, sing) (pre, ∅, s)