Learning with lookahead: Can history-based models rival globally optimized models? Yoshimasa Tsuruoka Japan Advanced Institute of Science and Technology (JAIST) Yusuke Miyao National Institute of Informatics (NII) Junichi Kazama National Institute of Information and Communications Technology (NICT)
History-based models Structured prediction problems in NLP – POS tagging, named entity recognition, parsing, … History-based models – Decompose the structured prediction problem into a series of classification problems Have been widely used in many NLP tasks – MEMMs (Ratnaparkhi, 1996; McCallum et al., 2000) – Transition-based parsers (Yamada & Matsumoto, 2003; Nivre et al., 2006) Becoming less popular
Part-of-speech (POS) tagging Perform multi-class classification at each word Features are defined on observations (i.e. words) and the POS tags on the left I saw a dog with eyebrows NVDPNVDP NVDPNVDP NVDPNVDP NVDPNVDP NVDPNVDP NVDPNVDP
Dependency parsing I saw a dog with eyebrows OPERATIONSTACKQUEUE Shift ReduceL ReduceR I saw a dog with eyebrows
Dependency parsing I saw a dog with eyebrows OPERATIONSTACKQUEUE Shift ReduceL ReduceR Isaw a dog with eyebrows
Dependency parsing I saw a dog with eyebrows OPERATIONSTACKQUEUE Shift ReduceL ReduceR a dog with eyebrows Isaw
Dependency parsing I saw a dog with eyebrows OPERATIONSTACKQUEUE Shift ReduceL ReduceR sawa dog with eyebrows
Dependency parsing I saw a dog with eyebrows OPERATIONSTACKQUEUE Shift ReduceL ReduceR saw adog with eyebrows
Dependency parsing I saw a dog with eyebrows OPERATIONSTACKQUEUE Shift ReduceL ReduceR saw a dogwith eyebrows
Dependency parsing I saw a dog with eyebrows OPERATIONSTACKQUEUE Shift ReduceL ReduceR saw dogwith eyebrows
Dependency parsing I saw a dog with eyebrows OPERATIONSTACKQUEUE Shift ReduceL ReduceR saw dog witheyebrows
Dependency parsing I saw a dog with eyebrows OPERATIONSTACKQUEUE Shift ReduceL ReduceR saw dog with eyebrows
Dependency parsing I saw a dog with eyebrows OPERATIONSTACKQUEUE Shift ReduceL ReduceR saw dog with
Dependency parsing I saw a dog with eyebrows OPERATIONSTACKQUEUE Shift ReduceL ReduceR saw dog
Lookahead Playing Chess If I move this pawn, then the knight will be captured by that bishop, but then I can …
POS tagging with lookahead Consider all possible sequences of future tagging actions to a certain depth I saw a dog with eyebrows NVD NVDPNVDP NVDPNVDP
POS tagging with lookahead Consider all possible sequences of future tagging actions to a certain depth I saw a dog with eyebrows NVD NVDPNVDP NVDPNVDP
POS tagging with lookahead Consider all possible sequences of future tagging actions to a certain depth I saw a dog with eyebrows NVD NVDPNVDP NVDPNVDP
POS tagging with lookahead Consider all possible sequences of future tagging actions to a certain depth I saw a dog with eyebrows NVD NVDPNVDP NVDPNVDP
POS tagging with lookahead Consider all possible sequences of future tagging actions to a certain depth I saw a dog with eyebrows NVD NVDPNVDP NVDPNVDP
Dependency parsing I saw a dog with eyebrows OPERATIONSTACKQUEUE Shift ReduceL ReduceR saw dogwith eyebrows Shift ReduceL ReduceR saw dog witheyebrows
Dependency parsing I saw a dog with eyebrows OPERATIONSTACKQUEUE Shift ReduceL ReduceR saw dogwith eyebrows Shift ReduceL ReduceR sawwith eyebrows
Choosing the best action by search S1S1 S2S2 SmSm a1a1 a2a2 amam S1*S1*S2*S2*S3*S3* search depth S
Search
Decoding cost Time complexity: O(nm^(D+1)) – n: number of actions to complete the structure – m: average number of possible actions at each state – D: search depth Time complexity of k-th order CRFs: O(nm^(k+1)) History-based models with k-depth lookahead are comparable to k-th order CRFs in terms of training/testing time
Perceptron learning with Lookahead S1S1 S2S2 SmSm S1*S1*S2*S2*Sm*Sm* a1a1 a2a2 amam Without lookahead With lookahead Linear scoring model Correct action Guaranteed to converge
Experiments Sequence prediction tasks – POS tagging – Text chunking (a.k.a. shallow parsing) – Named entity recognition Syntactic parsing – Dependency parsing Compared to first-order CRFs in terms of speed and accuracy
POS tagging Accuracy WSJ corpus
Training time Second WSJ corpus
POS tagging (+ tag trigram features) Accuracy WSJ corpus
Chunking (shallow parsing) F-score CoNLL 2000 data set
Named entity recognition F-score BioNLP/NLPBA 2004 data set
Dependency parsing F-score WSJ corpus (Zhang and Clark, 2008)
Related work MEMMs + Viterbi – label bias problem (Lafferty et al., 2001) Learning as search optimization (LaSO) (Daume III and Marcu 2005) – No lookahead Structured perceptron with beam search (Zhang and Clark, 2008)
Conclusion Can history-based models rival globally optimized models? – Yes, they can be more accurate than CRFs The same computational cost as CRFs
Future work Feature Engineering Flexible search extension/reduction Easy-first tagging/parsing – (Goldbergand & Elhadad, 2010) Max-margin learning
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