A Path-based Transfer Model for Machine Translation

1. A Path-based Transfer Model for Machine Translation Dekang Lin presented by Joshua Johanson

2. Training • Get a parallel corpus • Source language is in dependency trees • The text is word-aligned • Extract the paths from dependency trees • Learn translation rules from the paths using word alignment

3. Translation • Parse the sentence into dependency trees • Extract Paths • Merge the paths • Choose the transfer rules that give the highest probability • Output the resulting sentence

4. What is a Dependency Tree? • A dependency tree shows the relationship between the words of a sentence. Links are directed from the head to the modifier.

5. Comparing a Dependency Tree with a POS Tree

6. What is a path? A simple path is a link of two nodes or two links with an unassigned preposition.

7. Learning the Transfer Rules • Extracts only paths with all words aligned • A prepostion in the middle of a path is allowed to be aligned. • Uses the word alignment to create the relative order of the paths. (there could be gaps) • Learns the word alignment and the remapping.

8. Phrases • Head span: the word sequence aligned with the node n. • Phrase span: the word sequence from the lower bound of the head spans of all nodes in the subtree rooted at n to the upper bound of the same set of spans. • All of these correspond to the index of the target language.

9. Start with a simple path, let’s say from Connect (H) to controller (M), where H aligns to Branchez (H’) and M aligns to contrôleur (M’). (A simple path can have a middle node with an unaligned preposition, like “to”.) Connect both power cables to the controller Branchez les deux câbles d’ alimentation sur le contrôleur

10. Start with a simple path, let’s say from Connect (H) to controller (M), where H aligns to Branchez (H’) and M aligns to contrôleur (M’). (A simple path can have a middle node with an unaligned preposition, like “to”.) • Let S be the phrase span of a sibling of M (or head span of H) that is between H’ and M’ and closest to M’. In this case it corresponds to câbles d’ alimentation (S). Connect both power cables to the controller Branchez les deux câbles d’ alimentation sur le contrôleur

11. Start with a simple path, let’s say from Connect (H) to controller (M), where H aligns to Branchez (H’) and M aligns to contrôleur (M’). (A simple path can have a middle node with an unaligned preposition, like “to”.) • Let S be the phrase span of a sibling of M (or head span of H) that is between H’ and M’ and closest to M’. In this case it corresponds to câbles d’ alimentation (S). • The right hand side is the simple link in the original language Connect both power cables to the controller Branchez les deux câbles d’ alimentation sur le contrôleur

12. Start with a simple path, let’s say from Connect (H) to controller (M), where H aligns to Branchez (H’) and M aligns to contrôleur (M’). (A simple path can have a middle node with an unaligned preposition, like “to”.) • Let S be the phrase span of a sibling of M (or head span of H) that is between H’ and M’ and closest to M’. In this case it corresponds to câbles d’ alimentation (S). • The right hand side is the simple link in the original language • The left hand side is: • The link between H’ and M’ Connect both power cables to the controller Branchez les deux câbles d’ alimentation sur le contrôleur

13. Start with a simple path, let’s say from Connect (H) to controller (M), where H aligns to Branchez (H’) and M aligns to contrôleur (M’). (A simple path can have a middle node with an unaligned preposition, like “to”.) • Let S be the phrase span of a sibling of M (or head span of H) that is between H’ and M’ and closest to M’. In this case it corresponds to câbles d’ alimentation (S). • The right hand side is the simple link in the original language • The left hand side is: • The link between H’ and M’ • A link between M’ and the nodes between S and the phrase span of M. Connect both power cables to the controller Branchez les deux câbles d’ alimentation sur le contrôleur

14. Start with a simple path, let’s say from Connect (H) to controller (M), where H aligns to Branchez (H’) and M aligns to contrôleur (M’). (A simple path can have a middle node with an unaligned preposition, like “to”.) • Let S be the phrase span of a sibling of M (or head span of H) that is between H’ and M’ and closest to M’. In this case it corresponds to câbles d’ alimentation (S). • The right hand side is the simple link in the original language • The left hand side is: • The link between H’ and M’ • A link between M’ and the nodes between S and the phrase span of M. • All unaligned word (like sur) will be leaf nodes. Connect both power cables to the controller Branchez les deux câbles d’ alimentation sur le contrôleur

15. To align more complicated paths, just combine the translation of more simple paths This can create rules that are not paths:

16. Divergences • This will create dependency trees that are not consistent with the new language. • In this case the translation will still produce the words in the correct order. X swim across Y X cruzar Y nadando

17. 21 permutations

18. Generalize

19. Calculate Translation Probability • Si is the path (Connect to controller) • Ti is the tree fragment (Branchez sur contrôleur) • c(Si) is the count of Si • c(Ti,Si) is the count of both Ti and Si occuring together • M is a constant

20. Translation • Parse the sentence to obtain its dependency structure.

21. Translation • Parse the sentence to obtain its dependency structure. • Extract all the paths in the dependency tree and retrieve the translations of all the paths.

22. Translation • Parse the sentence to obtain its dependency structure. • Extract all the paths in the dependency tree and retrieve the translations of all the paths. • Find rules that can be merged to cover the whole tree

23. Merging • If two target nodes are mapped to the same source node, it gets merged. • Merging will not create a loop • We only have to worry about the unaligned words, which are leaf nodes and don’t point to anything • This new translation is a tree • They are all connected and there aren’t any loops.

24. Node ordering • If two nodes go on different sides of h, then go to the respective sides. • deux câbles & câbles existants • deux câbles existants

25. Node ordering • If they are on the same side as h in the target sentence, stay the same distance from h as in the source sentence. • existing coaxial cables • câbles coaxiaux existants

26. Node ordering • If they are on the same side in the target sentence, but not the source sentence, use the word order of the original in the source sentence • m1 h m2 (source) • h m1 m2 (target)

27. Translation • Parse the sentence to obtain its dependency structure. • Extract all the paths in the dependency tree and retrieve the translations of all the paths. • Find rules that can be merged to cover the whole tree • Output the one with highest probability

28. Probability • C is a set of paths covering S • There can be overlap in C, but no path will completely contained in another in the final output. • This is a direct translation (not noisy channel model)

29. Experiment • Used English-French portion of 1999 European Parliament Proceedings. • Used 1,755 sentences with 5-15 words out of 116,889. • Used Minipar to parse the sentences. • Used ProAlign to align the words.

30. Results

31. What is different about this approach? • Translations are based on a dependency tree in the source language • Syntactically based • There are fewer paths than subtrees (quadratic instead of exponential) • Less sparse • It automatically learns word order • No need to know anything but syntax of target language