1. 1 Learning with Latent Alignment Structures Quasi-synchronous Grammar and Tree-edit CRFs for Question Answering and Textual Entailment Mengqiu Wang Joint work with Chris Manning, Noah Smith

2. 2 Task definition At a high-level: Learning the syntactic and semantic relations between two pieces of text Application-specific definition of the relations Question Answering Q: Who is the leader of France? A: Bush later met with French President Jacques Chirac Machine Translation C: 温总理昨天会见了日本首相安培晋三。 E: Premier Wen Jiabao met with Japanese Prime Minister Shinzo Abe yesterday. Summarization T: US rounds up 400 Saddam diehards as group claims anti-US attacks in Iraq. S: US rounded up 400 people in Iraq. Textual Entailment (IE, IR, QA, SUM) Txt: Responding to Scheuer's comments in La Repubblica, the prime minister's office said the analysts' allegations, "beyond being false, are also absolutely incompatible with the contents of the conversation between Prime Minister Silvio Berlusconi and U.S. Ambassador to Rome Mel Sembler." Hyp: Mel Sembler represents the U.S.

3. 3 The Challenges Latent alignment structure QA: Who is the leader of France? Bush later met with French President Jacques Chirac MT: 温总理昨天会见了日本首相安培晋三。 Premier Wen Jiabao met with Japanese Prime Minister Shinzo Abe yesterday. Sum: US rounds up 400 Saddam diehards as group claims anti-US attacks in Iraq. US rounded up 400 people in Iraq. RTE: Responding to … the conversation between Prime Minister Silvio Berlusconi and U.S. Ambassador to Rome Mel Sembler.“ Mel Sembler represents the U.S.

4. 4 Other modeling challenges QuestionAnswer Ranking Who is the leader of France ? 1. Bush later met with French president Jacques Chirac. 2. Henri Hadjenberg, who is the leader of France ’s Jewish community, … 3. …

5. 5 Semantic Tranformations Q:“Who is the leader of France?” A: Bush later met with French president Jacques Chirac.

6. 6 Syntactic Transformations Who leadertheFranceofis? BushmetFrenchwithpresidentJacquesChirac mod

7. 7 Syntactic Variations Who leadertheFranceofis? HenriHadjenberb,wholeaderistheofFrance’s’sJewishcommunity mod

8. 8 What’s been done? The latent alignment problem Instead of treating alignment as latent variable, treat it as a separate task. First find the best alignment, then proceed with the rest of the task Pros: Usually simple and efficient. Cons: Not very robust, no way to correct alignment errors in later steps. Modeling syntax and semantics Extract features from syntactic parse trees and semantic resources then throw them into a linear classifier. Use syntax and semantic to enrich the feature space, but no principled ways to make use of syntax Pros: No need to worry about trees too much Cons: Ad-hocs

9. 9 What I think an ideal model should do Carry alignment uncertainty into final task Treat alignment as latent variables and jointly learn about proper alignment structure and the overall task In other words, model the distribution over alignments and sum out all possible alignments at decoding time. Syntax-based and feature-rich models Directly model syntax Enable the use of rich semantic features and features from other world-knowledge resources.

10. 10 Road map Present two models that address the raised issues 1: A model based on Quasi-synchronous Grammar (EMNLP 07’) Experiments on Question Answering task 2: A tree-edit CRFs model (current work) Experiments on RTE Discuss and compare these two models Modeling power Pros and cons Future work

11. 11 Switching gear… Quasi-synchronous Grammar for Question Answering

12. 12 Tree-edit CRFs for RTE Extension to McCallum et al. UAI2005 work on CRFs for finite-state String Edit Distance Key attractions: Models the transformation of dependency parse trees (thus directly models syntax), unlike McCallum et al. ’05, which only models word strings Discriminatively trained (not a generative model, unlike QG) Trained on both the positive and negative instances of sentence pairs (QG is only trained on positive Q/A pairs) CRFs – the underlying graphical model is an undirected graphical model (QG is basically a Bayes Net, directed) Joint model over alignments (vs. local alignment models in QG) Feature rich

13. 13 TE-CRFs model in details First of all, let’s look at the correspondence between alignment (with constraints) and edit operations

14. Bush NNP person met VBD French JJ location president NN Jacques Chirac NNP person who WP qword leader NN is VB the DT France NNP location Q:A: $ root $ root subjobj detof root subjwith nmod substitute delete insert Fancy substitute

15. 15 TE-CRFs model in details Each valid tree edit operation sequence that transforms one tree into the other corresponds to an alignment. A tree edit operation sequence is models as a transition sequence among a set of states in a FSM S1 S2 S3 D, S, I D, E, I D, S, I substitute deletesubstitute insert substitute S1 S2 S1 S3S1 S2 S3 S2 S1S2 S1S3 S2 S1 S3 S2 … … … … … … …

16. 16 FSM This is for one edit operation sequence substitute deletesubstitute insert substitute S1 S2 S1 S3S1 S2 S3 S2 S1S2 S1S3 S2 S1 S3 S2 … … … … … … … delete substitute insert substitute S1 S2 S1 S3S1 … … … … … … … substitute deletesubstitute insert S1 S2 S1 S3S1 … … … … … … … substitute deletesubstitute insert substitute S1 S2 S1 S3S1 … … … … … … … There are many other valid edit sequences

17. 17 FSM cont. S1 S2 S3 D, S, I Start Stop ε ε S1 S2 S3 D, S, I Positive State Set Negative State Set ε ε

18. 18 FSM transitions S3 S2 S1 S3 S2 Start S2 S3 S1 S2 S1 S2 S1 S3 … …… … S2 … … … … … … … Stop S3 S2 S1 S3 S2 S3 S1 S2 S1 S2 S1 S3 … …… … S2 … … … … … … … Positive State Set Negative State Set

19. 19 Parameterization S1 S2 substitute positive or negative positive and negative

20. 20 Training using EM E-step M-step Using L-BFGS Jensen’s Inequality

21. 21 Features for RTE Substitution Same -- Word/WordWithNE/Lemma/NETag/Verb/Noun/Adj/Adv/Other Sub/MisSub -- Punct/Stopword/ModalWord Antonym/Hypernym/Synonym/Nombank/Country Different – NE/Pos Unrelated words Delete Stopword/Punct/NE/Other/Polarity/Quantifier/Likelihood/Condition al/If Insert Stopword/Punct/NE/Other/Polarity/Quantifier/Likelihood/Condition al/If Tree RootAligned/RootAlignedSameWord Parent,Child,DepRel triple match/mismatch Date/Time/Numerical DateMismatch, hasNumDetMismatch, normalizedFormMismatch

22. 22 Tree-edit CRFs for Textual Entailment Preliminary results Trained on RTE2 dev, tested on RTE2 test. model taken after 50 EM iterations acc:0.6275, map:0.6407. RTE2 official results 1.Hickl (LCC) acc:0.7538, map:0.8082 2.Tatu (LCC) acc:0.7375, map:0.7133 3.Zanzotto (Milan & Rome) acc:0.6388, map:0.6441 4.Adams (Dallas) acc:0.6262, map:0.6282

23. 23 Comparison: QG vs. TE-CRFs 1.Generative 2.Directed, BayesNet, local 3.Allow arbitrary swapping in alignment 4.Allow limited use of semantic features (lexical- semantic log-linear model in mixture model) 5.Computationally cheaper 1. Discriminative 2. Undirected, CRFs, global 3. No swapping – can’t do substitutions that involve swapping (can be extended, see future work) 4. Allow arbitrary semantic features 5. Computationally more expensive QG TE-CRFs

24. 24 Future work 1.Generative Train discriminatively using Noah’s Contrastive Estimation 2.Directed, BayesNet, local Higher-order Markovization 3.Allow arbitrary swapping in alignment 4.Allow limited use of semantic features (lexical- semantic log-linear model in mixture model) 5.Computationally cheaper 6.Run RTE experiments 1. Discriminative 2. Undirected, CRFs, global 3. No swapping Constrained unordered trees Fancy edit operations (e.g. substitute sub-trees) 4. Allow arbitrary semantic features 5. More expensive 6. Run QA and MT alignment experiments QG TE-CRFs

25. 25 Thank you! Questions?