an incremental algorithm for transition-based ccg parsing
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1 an incremental algorithm for transition-based ccg parsing B. R. Ambati, T. Deoskar, M. Johnson and M. Steedman Miyazawa Akira January 22, 2016 The Graduate University For Advanced Studies / National Institute of Informatics
2 incrememtal parser What does Incremental mean? An incremental parser computes the relationship between words as soon as it receives them from the input. Why is incrementality important? Statistical Machine Translation Automatic Speech Recognition 1
3 baseline algorithm Their baseline algorithm NonInc is based on Zhang and Clark (2011). It has four actions. Shift Push a word from the input buffer to the stack and assign it with a CCG category. Reduce Left Pop the top two nodes from the stack, combine them into a new node and push it back onto the stack with a new category. The right node is the head and the left node is reduced. Reduce Right This is similar to the action above except that the left node is the head and the right node is reduced. Unary Change the category of the top node on the stack. 2
4 noninc algorithm 0 John likes mangoes from India madly 3
5 noninc algorithm 1 Shift likes mangoes from India madly NP John 3
6 noninc algorithm 2 Shift mangoes from India madly NP John (S\NP)/NP likes 3
7 noninc algorithm 3 Shift from India madly NP John (S\NP)/NP likes NP mangoes 3
8 noninc algorithm 4 Shift India madly NP John (S\NP)/NP likes NP mangoes (NP\NP)/NP from 3
9 noninc algorithm 5 Shift madly NP John (S\NP)/NP likes NP mangoes (NP\NP)/NP from NP India 3
10 noninc algorithm 6 Reduce Right madly NP John (S\NP)/NP likes NP mangoes NP\NP from from India 3
11 noninc algorithm 7 Reduce Right madly NP John (S\NP)/NP likes NP mangoes mangoes from India 3
12 noninc algorithm 8 Reduce Right madly NP John S\NP likes likes mangoes from India 3
13 noninc algorithm 9 Shift NP John S\NP likes (S\NP)\(S\NP) madly likes mangoes from India 3
14 noninc algorithm 10 Reduce Right NP John S\NP likes likes mangoes madly from India 3
15 noninc algorithm 11 Reduce Left S likes John likes mangoes from India madly 3
16 noninc algorithm Finish S likes John likes mangoes from India madly 3
17 problem in noninc The algorithm above is not incremental. The dependncy graph starts to grow only after almost all the words are pushed to the stack. To solve this problem, they introduce a revealing technique (Pareschi and Steedman (1987)). 4
18 revinc algorithm 0 John likes mangoes from India madly 5
19 revinc algorithm 1 Shift likes mangoes from India madly NP John 5
20 revinc algorithm 2 Shift mangoes from India madly NP John (S\NP)/NP likes 5
21 revinc algorithm 3-1 Type-Raising mangoes from India madly S/(S\NP) John (S\NP)/NP likes 5
22 revinc algorithm 3-2 Reduce Left mangoes from India madly S/NP likes likes John 5
23 revinc algorithm 4 Shift from India madly S/NP likes NP mangoes likes John 5
24 revinc algorithm 5 Reduce Right from India madly S likes likes John mangoes 5
25 revinc algorithm 6 Shift India madly S likes (NP\NP)/NP from likes John mangoes 5
26 revinc algorithm 7 Shift madly likes S likes (NP\NP)/NP from NP India John mangoes 5
27 revinc algorithm 8 Reduce Right madly S likes NP\NP from likes John mangoes from India 5
28 revinc algorithm 9 Right Reveal madly S likes likes John mangoes S likes R> NP\NP from from India S/NP likes NP mangoes < NP > S 5
29 revinc algorithm 10 Shift S likes (S\NP)\(S\NP) madly likes John mangoes from India 5
30 revinc algorithm 11 Left Reveal S likes likes John mangoes from India madly S likes (S\NP)\(S\NP) madly NP John R< S\NP likes < S\NP S < 5
31 revinc algorithm Finish S likes likes John mangoes from India madly 5
32 revealing actions 1. Left Reveal (LRev) Pop the top two nodes in the stack (left, right). Identify the left node s child with a subject dependency. Abstract over this child node and split the category of left node into two categories. Combine the nodes using CCG combinators accodingly. S likes (S\NP)\(S\NP) madly R< NP John S\NP likes S\NP < S < 6
33 revealing actions 2. Right Reveal (RRev) Pop the top two nodes in the stack (left, right). Check the right periphery of the left node in the dependency graph, extract all the nodes with compatible CCG categories and identify all the possible nodes that the right node can combine with. Abstract over this node, split the category into two categories accordingly and combine the nodes using CCG combinators. S likes NP\NP from R> S/NP likes NP mangoes NP < S > 7
34 data and settings Data: CCGbank training: sections development: section 00 testing: section 23 POS tagger: C&C POS tagger Supertagger: C&C supertagger 8
35 features NonInc Features based on the top four nodes in the stack and the next four words in the input buffer. feature templates 1 S 0wp, S 0c, S 0pc, S 0wc, S 1wp, S 1c, S 1pc, S 1wc, S 2pc, S 2wc, S 3pc, S 3wc, 2 Q 0wp, Q 1wp, Q 2wp, Q 3wp, 3 S 0Lpc, S 0Lwc, S 0Rpc, S 0Rwc, S 0Upc, S 0Uwc, S 1Lpc, S 1Lwc, S 1Rpc, S 1Rwc, S 1Upc, S 1Uwc, 4 S 0wcS 1wc, S 0cS 1w, S 0wS 1c, S 0cS 1c, S 0wcQ 0wp, S 0cQ 0wp, S 0wcQ 0p, S 0cQ 0p, S 1wcQ 0wp, S 1cQ 0wp, S 1wcQ 0p, S 1cQ 0p, 5 S 0wcS 1cQ 0p, S 0cS 1wcQ 0p, S 0cS 1cQ 0wp, S 0cS 1cQ 0p, S 0pS 1pQ 0p, S 0wcQ 0pQ 1p, S 0cQ 0wpQ 1p, S 0cQ 0pQ 1wp, S 0cQ 0pQ 1p, S 0pQ 0pQ 1p, S 0wcS 1cS 2c, S 0cS 1wcS 2c, S 0cS 1cS 2wc, S 0cS 1cS 2, S 0pS 1pS 2, 6 S 0cS 0HcS 0Lc, S 0cS 0HcS 0Rc, S 1cS 1HcS 1Rc, S 0cS 0RcQ 0p, S 0cS 0RcQ 0w, S 0cS 0LcS 1c, S 0cS 0LcS 1w, S 0cS 1cS 1Rc, S 0wS 1cS 1Rc, RevInc + B 1c and B 1cS 0c, where B 1 is the bottom most node in the right periphery. 9
36 measures of incrementality Measures of incrementality: Connectedness the average number of nodes in the stack before shifting Waiting time the number of nodes that need to be shifted to the stack before a dependency between any two nodes in the stack is resolved Table 1: Connectedness and waiting time. Algorithm Connectedness Waiting Time NonInc RevInc
37 results and analysis Table 2: Performance on the development data 1. Algorithm UP UR UF LP LR LF Cat Acc. NonInc (beam=1) RevInc (beam=1) NonInc (beam=16) Z&C (beam=16) NonInc gets higher precision because it can use more context while making a decision. RevInc achieves higher recall because information on nodes is available even after they are reduced. 1 U stands for unlabaled and L stands for labeles. P, R and F are precision, recall and F-score respectively. 11
38 results and analysis Table 3: Label-wise F-score of RevInc and NonInc parsers. Category RevInc NonInc (NP\NP)/NP (NP\NP)/NP ((S\NP)\(S\NP))/NP ((S\NP)\(S\NP))/NP (S[dcl]\NP)/NP (S[dcl]\NP)/NP (S\NP)\(S\NP) NonInc performs better in labels corresponding to PP due to the availability of more context. RevInc has advantage in the case of verbal arguments and verbal modifiers as the effect of reveal actions. 12
39 parsing speed Parsing speed: NonInc parses 110 sentences/sec. RevInc parses 125 sentences/sec. Significant amount of parsing time is spent on the feature extraction step. But in RevInc, usually only two nodes have their feature extracted because connectedness = 2.15, while all four nodes have to be processed in NonInc (connectedness = 4.62). Complex actions, LRev and RRev are rarely used (5%). 13
40 results and analysis Table 4: Performance on the test data. Algorithm UP UR UF LP LR LF Cat Acc. NonInc (beam=1) RevInc (beam=1) NonInc (beam=16) Z&C (beam=16) Hassan et al F-scores are improved compared to NonInc in both unlabaled and labeled cases. 14
41 future plan Use information about lexical category probabilities (Auli and Lopez (2011)). Explore the limited use of a beam to handle lexical ambiguity. Use a dynamic oracle strategy (Xu et al. (2014)). Apply the method to SMT and ASR. 15
42 conclusion They designed and implemented an incremental CCG parser by introducing a technique called revealing. The parser got high scores in two measures of incrementality: connectedness and waiting time. It performs better in parsing in the view of recall and F-score. (labeled: +0.88%, unlabeld: +2.0%) It parses sentences faster. 16
43 references Ambati, R. B., Deoskar, T., Johnson, M., and Steedman, M. (2015). An incremental algorithm for transition-based CCG parsing. In Proceedings of the 2015 Conference of NAACL, pages Auli, M. and Lopez, A. (2011). A comparison of loopy belief propagation and dual decomposition for integrated CCG supertagging and parsing. In Proceedings of the 49th Annual Meeting of ACL, pages Pareschi, R. and Steedman, M. (1987). A lazy way to chart-parse with categorial grammars. In Proceedings of the 25th Annual Meeting of ACL. Xu, W., Clark, S., and Zhang, Y. (2014). Shift-reduce CCG parsing with a dependency model. In Proceedings of the 52nd Annual Meeting of ACL, pages Zhang, Y. and Clark, S. (2011). Shift-reduce CCG parsing. In Proceedings of the 49th Annual Meeting of ACL, pages
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