1. A Joint Model For Semantic Role Labeling Aria Haghighi, Kristina Toutanova, Christopher D. Manning Computer Science Department Stanford University

2. Most Previous Work: Local Models Extract features for each node and the predicate Classify nodes independently the children The ogre cooked NP S VP NP  (n) Phrase Type: NP Path: NP-up-VP-down V Head Word: children Predicate: cook Passive: false Position: after

3. A Drawback of Local Models S VP The ogre cooked NP the children NP a meal NP PATIENT NP AGENT NP BENIFICIARY NP PATIENT

4. Core argument frame constraints Hard Constraints: No overlapping arguments Soft Constraints: AGENT occurs before other core arguments in a active sentence Highly non-local constraints Model a predicate’s argument preferences No core arguments are bad and so are 10 Verb specific rules: Obligatory arguments We’d like to do this statistically without hand- coding constraints or conditions What we’d like to capture

5. Previous Joint Approaches Argument Language model and Viterbi Decoding (Gildea and Jurafsky, 02) Linear Programming over Local Scores (Punyakanok et al, 04 and 05) Our approach: Capture joint information between features and labels discriminatively

6. Joint Discriminative Reranking Use a reranking approach (Collins 00) Start with local model with strong independences Find top N non-overlapping assignments for local model using a simple dynamic program (Toutanova, 05) Use joint model to select best assignment among top N using a joint log-linear model

7. Reranking Upperbounds Reranking not a serious bottleneck Core arguments top 20: f-measure 99.2, whole frame acc 97.4 All arguments top 20: f-measure 98.8, whole frame acc 95.3

8. Global Reranking Features [ AGENT The company] offered [ PATIENT a 20% stake] [ BENEFICIARY to the public] Core Argument Sequence with predicate and voice [ NP AGENT active:pred NP PATIENT PP-to BENEFICIARY ] Lexicalized version: active:pred to active:offer [ AGENT active:pred PATIENT BENEFICIARY ] [ NP active:pred NP PP-to ] Frame Feature [ NP active:pred NP PATIENT PP-to ] Compare to less likely [ NP active:pred NP PATIENT NP ]

9. Joint Results and Improvements Improvement doesn’t match gold parses (Toutanova,05) Argument Identification Bottleneck Flat ModelJoint ModelError Reduction Dev Set F-Measure74.5276.718.6 % Dev Set Whole Frame Accuracy 51.02 %54.92 %7.1 %

10. Using Multiple Trees Argument identification sensitive to parser errors PP attachment, Coordination, etc.. Path feature becomes very noisy Use Top K trees (Charniak Parser ‘ 05) For top local assignments and trees choose assignment and tree to maximize: Only a small boost in performance ….

11. Dealing with Dislocations Argument dislocation via control, subject raising etc. IsMissingSubject and Path For local with overlap: 73.80 to 74.52 AGENT improvement: 81.02 to 83.08 S VP NP i is S VP expected VP NP i -NONE- The trade gap to widen

12. Final Results F-MeasureWhole Frame Test WSJ78.4556.52 % Test Brown67.7137.06 % Combined77.0444.83 % Genaralizing to other domains

13. Thanks !

14. Why hasn’t it been done? Exponential Blowup! A normal-sized tree in the Wall Street Journal will have about 40 internal nodes to be classified About 1 trillion possible assignments (binary ARG/NONE)

15. Thanks !

16. What we’d like to capture ….. Model predicate’s argument preferences Bad: no core arguments, 10 core arguments Verb specific rules: Require A0 or A1 args Model dependencies between labels and features of argument sequence Discourage repeated arguments Model syntactic alternations: [NPA0,gave,NPA2,NPA1] [NPA0,gave,NPA1,PP_toA2] Principled Parameter Estimation

17. Previous Work: Local Classifiers Extract features and classify each node independently S Phrase Type: NP Path: NP-up-VP-down V Head Word: Dursleys NP VP NP V PP a lesson NP Harry Potter gave the Dursleys in magic NP last week  (n)

18. Core argument frame strongly interdependent Hard Constraints: No overlapping arguments Soft Constraints: A0 occurs before A1, A2, etc… Doesn’t capture statistical tendencies in core argument sequences and their syntactic realization Problems with Local Classifiers… NP A0 S VP NP A0 NP A1 V PP a lesson NP Harry Potter gave the Dursleys in magic NP TMP last week