Supervised Learning of Complete Morphological Paradigms
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1 Supervised Learning of Complete Morphological Paradigms Greg Durrett and John DeNero UC Berkeley / Google
2 Morphological Inflection train (de) Zug
3 Morphological Inflection train (de) Zug NOM, SING: Zug NOM, PLU: Züge GEN, SING: Zuges GEN, PLU: Züge (8 total)
4 Morphological Inflection train (de) Zug NOM, SING: Zug NOM, PLU: Züge GEN, SING: Zuges GEN, PLU: Züge (8 total) u NOM, SING: u NOM, PLU: ü GEN, SING: u GEN, PLU: ü
5 Morphological Inflection train (de) Zug NOM, SING: Zug NOM, PLU: Züge GEN, SING: Zuges GEN, PLU: Züge (8 total) u NOM, SING: u NOM, PLU: ü GEN, SING: u GEN, PLU: ü g NOM, SING: g NOM, PLU: ge GEN, SING: ges GEN, PLU: ge
6 Morphological Inflection express train (de) Schnellzug NOM, SING: Schnellzug NOM, PLU: Schnellzüge GEN, SING: Schnellzuges GEN, PLU: Schnellzüge u NOM, SING: u NOM, PLU: ü GEN, SING: u GEN, PLU: ü g NOM, SING: g NOM, PLU: ge GEN, SING: ges GEN, PLU: ge
7 Morphological Inflection egg white (fi) valkuainen culprit (fi) syyllinen NOM, SING: valkuainen GEN, SING: valkuaisen PART, SING: valkuaista NOM, SING: syyllinen GEN, SING: syyllisen PART, SING: syyllistä
8 Morphological Inflection egg white (fi) valkuainen culprit (fi) syyllinen NOM, SING: valkuainen GEN, SING: valkuaisen PART, SING: valkuaista NOM, SING: syyllinen GEN, SING: syyllisen PART, SING: syyllistä
9 Morphological Inflection egg white (fi) valkuainen back vowels culprit (fi) syyllinen NOM, SING: valkuainen GEN, SING: valkuaisen PART, SING: valkuaista NOM, SING: syyllinen GEN, SING: syyllisen PART, SING: syyllistä
10 Morphological Inflection egg white (fi) valkuainen back vowels culprit (fi) syyllinen front vowels NOM, SING: valkuainen GEN, SING: valkuaisen PART, SING: valkuaista NOM, SING: syyllinen GEN, SING: syyllisen PART, SING: syyllistä
11 Morphological Inflection egg white (fi) valkuainen back vowels NOM, SING: valkuainen GEN, SING: valkuaisen PART, SING: valkuaista culprit (fi) syyllinen front vowels NOM, SING: syyllinen GEN, SING: syyllisen PART, SING: syyllistä Suffix morphology can depend on context in a complex way
12 Morphological Inflection Given Base form (Word)
13 Morphological Inflection Given Base form (Word) Predict Inflection table (variants) Attributes(1): Inflected form(1) Attributes(2): Inflected form(2)
14 Morphological Inflection Given Base form (Word) Predict Inflection table (variants) Attributes(1): Inflected form(1) Attributes(2): Inflected form(2) Language/POS independence
15 Morphological Inflection Given Base form (Word) Predict Inflection table (variants) Attributes(1): Inflected form(1) Attributes(2): Inflected form(2) Language/POS independence Handle multi-part paradigms
16 Morphological Inflection Given Base form (Word) Predict Inflection table (variants) Attributes(1): Inflected form(1) Attributes(2): Inflected form(2) Language/POS independence Handle multi-part paradigms Rich way to predict inflections
17 Outline
18 Outline Rule extraction (>1000) Zug Kluft
19 Outline Rule extraction (>1000) (<50) Zug Kluft u g
20 Outline Rule extraction (>1000) (<50) Zug Kluft u g ( Paradigm prediction Zug ), u
21 Outline Rule extraction (>1000) (<50) Zug Kluft u g ( Paradigm prediction Zug ), u Base form predictor Inflection table
22 Wiktionary Data Thousands of full inflection tables for many languages and parts of speech
23 Wiktionary Data Thousands of full inflection tables for many languages and parts of speech
24 Wiktionary Data Thousands of full inflection tables for many languages and parts of speech more
25 Previous Work Dreyer and Eisner (2011) develops a semisupervised Bayesian model
26 Previous Work Dreyer and Eisner (2011) develops a semisupervised Bayesian model Wicentowski (2004) predicts one variant at a time (the lemma)
27 Previous Work Dreyer and Eisner (2011) develops a semisupervised Bayesian model Wicentowski (2004) predicts one variant at a time (the lemma) u NOM, SING: u NOM, PLU: ü GEN, SING: u GEN, PLU: ü
28 Rule Extraction
29 Rule Extraction to insist (de) dringen 1P, PRES, SING: dringe 2P, PRES, SING: dringst 3P, PRES, SING: dringt 1P, PAST, SING: drang (27 total)
30 Rule Extraction to insist (de) dr i ngen 1P, PRES, SING: dringe 2P, PRES, SING: dringst 3P, PRES, SING: dringt 1P, PAST, SING: drang (27 total) i 1P, PRES, SING: i 2P, PRES, SING: i 3P, PRES, SING: i 1P, PAST, SING: a
31 Rule Extraction to insist (de) dr i ng en 1P, PRES, SING: dringe 2P, PRES, SING: dringst 3P, PRES, SING: dringt 1P, PAST, SING: drang (27 total) i 1P, PRES, SING: i 2P, PRES, SING: i 3P, PRES, SING: i 1P, PAST, SING: a en 1P, PRES, SING: e 2P, PRES, SING: st 3P, PRES, SING: t 1P, PAST, SING: -
32 Alignment to Base Form d r i n g e 1P, PRES, SING
33 Alignment to Base Form d r i n g e 1P, PRES, SING D
34 Alignment to Base Form 1P, PRES, SING d r i n g e
35 Alignment to Base Form d r i n g s t 2P, PRES, SING 1P, PRES, SING d r i n g e
36 Alignment to Base Form d r i n g s t 2P, PRES, SING S S 1P, PRES, SING d r i n g e
37 Alignment to Base Form 1P, PRES, SING 2P, PRES, SING d r i n g e d r i n g s t
38 Alignment to Base Form d r i n g t 3P, PRES, SING S D 1P, PRES, SING 2P, PRES, SING d r i n g e d r i n g s t
39 Alignment to Base Form 1P, PRES, SING 2P, PRES, SING 3P, PRES, SING d r i n g e d r i n g s t d r i n g t
40 Alignment to Base Form S d r a n g 1P, PAST, SING D D 1P, PRES, SING 2P, PRES, SING 3P, PRES, SING d r i n g e d r i n g s t d r i n g t
41 Alignment to Base Form 1P, PRES, SING 2P, PRES, SING 3P, PRES, SING 1P, PAST, SING d r i n g e d r i n g s t d r i n g t d r a n g
42 Concatenation 1P, PRES, SING 2P, PRES, SING 3P, PRES, SING 1P, PAST, SING d r i n g e d r i n g s t d r i n g t d r a n g
43 Concatenation 1P, PRES, SING 2P, PRES, SING 3P, PRES, SING 1P, PAST, SING d r i n g e d r i n g s t d r i n g t d r a n g
44 Concatenation 1P, PRES, SING 2P, PRES, SING 3P, PRES, SING 1P, PAST, SING d r i n g e d r i n g s t d r i n g t d r a n g
45 Concatenation 1P, PRES, SING 2P, PRES, SING 3P, PRES, SING 1P, PAST, SING d r i n g e d r i n g s t d r i n g t d r a n g
46 Concatenation 1P, PRES, SING 2P, PRES, SING 3P, PRES, SING 1P, PAST, SING d r i n g e d r i n g s t d r i n g t d r a n g
47 Concatenation 1P, PRES, SING 2P, PRES, SING 3P, PRES, SING 1P, PAST, SING d r i n g e d r i n g s t d r i n g t d r a n g
48 Concatenation 1P, PRES, SING 2P, PRES, SING 3P, PRES, SING 1P, PAST, SING d r i n g e d r i n g s t d r i n g t d r a n g
49 Concatenation 1P, PRES, SING 2P, PRES, SING 3P, PRES, SING 1P, PAST, SING d r i n g e d r i n g s t d r i n g t d r a n g
50 Concatenation 1P, PRES, SING 2P, PRES, SING 3P, PRES, SING 1P, PAST, SING d r i n g e d r i n g s t d r i n g t d r a n g
51 Concatenation 1P, PRES, SING 2P, PRES, SING 3P, PRES, SING 1P, PAST, SING d r i n g e d r i n g s t d r i n g t d r a n g d r i n g e d r i n g s t d r i n g t d r a n g
52 Concatenation 1P, PRES, SING 2P, PRES, SING 3P, PRES, SING 1P, PAST, SING d r i n g e d r i n g s t d r i n g t d r a n g d r i n g e d r i n g s t d r i n g t d r a n g
53 Outline Rule extraction (>1000) (<50) Zug Kluft u g ( Paradigm prediction Zug ), u Base form predictor Inflection table
54 Paradigm Prediction w i n d e n to wind (de)
55 Paradigm Prediction w i n d e n to wind (de) i 1 i i i i a en 1 en e st t -
56 Paradigm Prediction w i n d e n to wind (de) i 1 i i i i a en 1 en e st t - en 2 en e est et - n 1 n - st t te
57 Paradigm Prediction w i n d e n to wind (de) = = = i 1 en 2 i 1 i i i i a en 1 en e st t - en 2 en e est et - n 1 n - st t te
58 Paradigm Prediction w i n d e n to wind (de) = = = = = = i 1 en 2 i 1 i i i i a en 1 en e st t - en 2 en e est et - n 1 n - st t te
59 Paradigm Prediction w i n d e n to wind (de) = = = = = = i 1 n 1 en 2 i 1 i i i i a en 1 en e st t - en 2 en e est et - n 1 n - st t te
60 Paradigm Prediction w i n d e n to wind (de) = = = = = = i 1 n 1 n 1 en 1 en 2 i 1 i i i i a en 1 en e st t - en 2 en e est et - n 1 n - st t te
61 Paradigm Prediction w i n d e n = = = = = = i 1 n 1 n 1 en 1 en 2
62 Paradigm Prediction w i n d e n = = = = = = i 1 n 1 n 1 en 1 en 2 Paths through this lattice are hypotheses
63 Paradigm Prediction w i n d e n = = = = = = i 1 n 1 n 1 en 1 en 2 Paths through this lattice are hypotheses Model with semi-markov CRF (Sarawagi and Cohen 2004)
64 Prediction Features w i n d e n i 1
65 Prediction Features w i n d e n i 1 binden verbinden
66 Prediction Features w i n d e n i 1 binden verbinden
67 Prediction Features w i n d e n i 1 [i1] nd binden verbinden
68 Prediction Features w i n d e n i 1 [i1] nd binden verbinden Rule identity conjoined with 1- through 4-grams at offsets up to +/-5
69 Prediction Features w i n d e n i 1 [i1] nd binden verbinden Rule identity conjoined with 1- through 4-grams at offsets up to +/-5 Coarse features shared between different rules
70 Learning The gold inflection table of every training example can be produced using our rules
71 Learning The gold inflection table of every training example can be produced using our rules Optimize conditional log-likelihood of the correct rule sequence
72 Outline Rule extraction (>1000) (<50) Zug Kluft u g ( Paradigm prediction Zug ), u Base form predictor Inflection table
73 Wiktionary Evaluation Setup # lines/table Train size Test size German nouns German verbs Spanish verbs Finnish nouns/adjs Finnish verbs
74 Wiktionary Evaluation Setup # lines/table Train size Test size German nouns German verbs Spanish verbs Finnish nouns/adjs Finnish verbs Most common 200 inflection tables are not chosen for the test set; we can memorize these
75 Wiktionary Results NAÏVE 100 THIS WORK ORACLE FACTORED 75 Average inflected form accuracy Suffixing baseline
76 Wiktionary Results NAÏVE 100 THIS WORK ORACLE FACTORED 75 Average inflected form accuracy
77 Wiktionary Results NAÏVE 100 THIS WORK ORACLE FACTORED 75 Average inflected form accuracy The best we can do with our rules
78 Wiktionary Results 100 NAÏVE THIS WORK ORACLE FACTORED 75 Average inflected form accuracy Separate predictor for each inflected form
79 Wiktionary Results NAÏVE 100 THIS WORK ORACLE FACTORED 75 Average whole table accuracy
80 Dreyer and Eisner (2011)
81 Dreyer and Eisner (2011) Evaluate on German verbs in CELEX Results averaged over 10 random train/test splits Small train sets (50 or 100 observed tables) Large test sets (5415 verbs)
82 Dreyer and Eisner (2011) Evaluate on German verbs in CELEX Results averaged over 10 random train/test splits Small train sets (50 or 100 observed tables) Large test sets (5415 verbs) Hierarchical Bayesian model of inflection Type-level transducers of variants can be trained in a supervised fashion Additionally incorporate unlabeled text with a token-level model
83 CELEX Results Inflected form accuracy DE11 DE11+CORPUS THIS WORK Training set size (example tables)
84 CELEX Results Inflected form accuracy DE11 DE11+CORPUS THIS WORK Training set size (example tables)
85 Conclusion Morphological inflection rules can be learned from supervised data, which is widely available
86 Conclusion Morphological inflection rules can be learned from supervised data, which is widely available Structured prediction of entire tables at once is effective for inflecting unseen base forms
87 Conclusion Morphological inflection rules can be learned from supervised data, which is widely available Structured prediction of entire tables at once is effective for inflecting unseen base forms Code and Wiktionary data is available at
88 Conclusion Morphological inflection rules can be learned from supervised data, which is widely available Structured prediction of entire tables at once is effective for inflecting unseen base forms Code and Wiktionary data is available at Thank you!
89 Accuracy Breakdown Inflected form accuracy THIS WORK DE verbs DE nouns ES verbs FI verbs FI nouns/adjs
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