Analysing Soft Syntax Features and Heuristics for Hierarchical Phrase Based Machine Translation
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1 Analysing Soft Syntax Features and Heuristics for Hierarchical Phrase Based Machine Translation David Vilar, Daniel Stein, Hermann Ney IWSLT 2008, Honolulu, Hawaii 20. October 2008 Human Language Technology and Pattern Recognition Lehrstuhl für Informatik 6 Computer Science Department RWTH Aachen University, Germany D. Vilar, D. Stein, H. Ney Soft-syntax 1 IWSLT October 2008
2 1 Introduction Hierarchical phrase-based models: Generalization of phrase-based-models Allow for gaps in the phrases Integration of reordering in the translation model Study the effect of extraction heuristics Extension with inclusion of (soft) syntactic features D. Vilar, D. Stein, H. Ney Soft-syntax 2 IWSLT October 2008
3 Outline 1 Introduction 2 Hierarchical Phrases 3 Heuristic Features 4 Syntactical Features 5 Experimental Results 6 Conclusions D. Vilar, D. Stein, H. Ney Soft-syntax 3 IWSLT October 2008
4 2 Hierarchical Phrases Formalization as a synchronous CFG Rules of the form X γ, α,, where: X is a non-terminal γ and α are strings of terminals and non-terminals is a one-to-one correspondence between the non-terminals of α and γ Example: X 中 X 0 那个 X 1, It s the X 1 in the X 0 X 也要 X 0 一些 X 1, like to X 0 some X 1 too Additionally: Glue rules S S 0 X 1, S 0 X 1 S X 0, X 0 D. Vilar, D. Stein, H. Ney Soft-syntax 4 IWSLT October 2008
5 Illustration meal toddler a order you did bambini per piatto un ordinato ha Alignment D. Vilar, D. Stein, H. Ney Soft-syntax 5 IWSLT October 2008
6 Illustration meal toddler a order you did bambini per piatto un ordinato ha Standard phrases D. Vilar, D. Stein, H. Ney Soft-syntax 5 IWSLT October 2008
7 Illustration X~1 toddler a X~0 X~0 Example rule bambini per X~1 un D. Vilar, D. Stein, H. Ney Soft-syntax 5 IWSLT October 2008
8 3 Heuristic Features Following features were tested: Paste rule Binary feature for rules of the form X X 0 α, X 0 β or X αx 0, βx 0 Hierarchical penalty Binary feature for hierarchical rules Number of non-terminals Two binary features indicating if the rule has one or two non-terminals. Extended glue rule added rule of the form X X 0 X 1, X 0 X 1 D. Vilar, D. Stein, H. Ney Soft-syntax 6 IWSLT October 2008
9 4 Syntactical Features Goal: include linguistic information from a deep syntactic parser Idea: introduce additional soft syntactic features This can be done during the extraction of the phrases No additional computational costs during decoding Can be done both on source and target side Rules are not filtered out D. Vilar, D. Stein, H. Ney Soft-syntax 7 IWSLT October 2008
10 Valid syntactical phrases A phrase is valid when a node exists that completely covers all positions In order to obtain a normalized score, we add up all the counts and divide by the number of occurences of the phrase pair S S WHADVP VP VP WRB AUX NP VV P NP Where is DT the JJ public NN toilet 洗手间 PP 在 PN 哪里 Extracted rule: X 0 在哪里 # Where is X 0 D. Vilar, D. Stein, H. Ney Soft-syntax 8 IWSLT October 2008
11 Scoring variants m(i, j) = minimum number of words to be deleted or added to a phrase, so that it fits the yield of a node S WHADVP VP WRB AUX NP Where is DT JJ NN Source Phrases: the public toilet public toilet is the D. Vilar, D. Stein, H. Ney Soft-syntax 9 IWSLT October 2008
12 Scoring variants m(i, j) = minimum number of words to be deleted or added to a phrase, so that it fits the yield of a node S WHADVP VP WRB AUX NP Where is DT JJ NN Source Phrases: the public toilet public toilet m(i, j) = 1 is the D. Vilar, D. Stein, H. Ney Soft-syntax 9 IWSLT October 2008
13 Scoring variants m(i, j) = minimum number of words to be deleted or added to a phrase, so that it fits the yield of a node S WHADVP VP WRB AUX NP Where is DT JJ NN Source Phrases: the public toilet public toilet m(i, j) = 1 is the m(i, j) = 1 D. Vilar, D. Stein, H. Ney Soft-syntax 9 IWSLT October 2008
14 Four count ( smoothing ) variants: δ (m(i, j), 0) binary c(i, j t) := 1 m(i, j) exp (m(i, j)) j i (j i) + m(i, j) linear exponentional relative D. Vilar, D. Stein, H. Ney Soft-syntax 10 IWSLT October 2008
15 5 Experimental Results IWSLT BTEC Data (Tourist and Travel domain) Chinese English Training data Sentences Running words Vocabulary Test 2004 Data Sentences 500 Running words OOVs Test 2005 Data Sentences 506 Running words OOVs Test 2008 Data Sentences 507 Running words 6325 OOVs 87 D. Vilar, D. Stein, H. Ney Soft-syntax 11 IWSLT October 2008
16 Results test04 test05 test08 BLEU TER BLEU TER BLEU baseline non-syntactic information hierarch paste glue NT2NT syntactic information binary linear exponential relative D. Vilar, D. Stein, H. Ney Soft-syntax 12 IWSLT October 2008
17 Results test04 test05 test08 BLEU TER BLEU TER BLEU baseline non-syntactic information hierarch + paste hierarch + paste + glue hierarch + paste + glue2 + 1NT2NT combination of both syntactic and non-syntactic information (all features) binary linear exponential relative D. Vilar, D. Stein, H. Ney Soft-syntax 13 IWSLT October 2008
18 Example Translations reference Where is the exchange counter? baseline The currency exchange office is syntactical Where is the currency exchange office? reference Could you exchange it for a new one? baseline You can buy a new one? syntactical Could you change it for a new one? reference You can take our airport shuttle bus to pick up the car. baseline You can take our airport shuttle bus with me. syntactical You can take our the airport shuttle bus come to pick it up. D. Vilar, D. Stein, H. Ney Soft-syntax 14 IWSLT October 2008
19 6 Conclusions Analyzed heuristics for phrase extraction Introduced soft syntactic constraints Use of source- and target-side information No additional search effort High variability of results Test on bigger corpora Bigger improvements when dealing with speech input (system talk tomorrow!) Applicable also to phrase-based systems D. Vilar, D. Stein, H. Ney Soft-syntax 15 IWSLT October 2008
20 Analysing Soft Syntax Features and Heuristics for Hierarchical Phrase Based Machine Translation David Vilar, Daniel Stein, Hermann Ney IWSLT 2008, Honolulu, Hawaii 20. October 2008 Human Language Technology and Pattern Recognition Lehrstuhl für Informatik 6 Computer Science Department RWTH Aachen University, Germany D. Vilar, D. Stein, H. Ney Soft-syntax 16 IWSLT October 2008
21 The Blackslide GoBack
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