A Kernel-based Approach to Learning Semantic Parsers

A Kernel-based Approach to Learning Semantic Parsers
Rohit J. Kate Doctoral Dissertation Proposal Supervisor: Raymond J. Mooney November 21, 2005

Outline Semantic Parsing Related Work
Background on Kernel-based Methods Completed Research Proposed Research Conclusions

Semantic Parsing Semantic Parsing: Transforming natural language (NL) sentences into computer executable complete meaning representations (MRs) Importance of Semantic Parsing Natural language communication with computers Insights into human language acquisition Example application domains CLang: Robocup Coach Language Geoquery: A Database Query Application

CLang: RoboCup Coach Language
In RoboCup Coach competition teams compete to coach simulated players The coaching instructions are given in a formal language called CLang If our player 4 has the ball, our player 4 should shoot. Simulated soccer field Coach Semantic Parsing ((bowner our {4}) (do our {4} shoot)) CLang

Geoquery: A Database Query Application
Query application for U.S. geography database containing about 800 facts [Zelle & Mooney, 1996] Which rivers run through the states bordering Texas? User Semantic Parsing answer(traverse_2( next_to(stateid(‘texas’)))) Query

Learning Semantic Parsers
Assume meaning representation languages (MRLs) have deterministic context free grammars true for almost all computer languages MRs can be parsed unambiguously

NL: Which rivers run through the states bordering Texas?
MR: answer(traverse_2(next_to(stateid(‘texas’)))) Parse tree of MR: Non-terminals: ANSWER, RIVER, TRAVERSE_2, STATE, NEXT_TO, STATEID Terminals: answer, traverse_2, next_to, stateid, ‘texas’ Productions: ANSWER  answer(RIVER), RIVER  TRAVERSE_2(STATE), STATE  NEXT_TO(STATE), STATE  NEXT_TO(STATE) TRAVERSE_2  traverse_2, NEXT_TO  next_to, STATEID  ‘texas’ ANSWER answer STATE RIVER NEXT_TO TRAVERSE_2 STATEID stateid ‘texas’ next_to traverse_2

Learning Semantic Parsers
Assume meaning representation languages (MRLs) have deterministic context free grammars true for almost all computer languages MRs can be parsed unambiguously Training data consists of NL sentences paired with their MRs Induce a semantic parser which can map novel NL sentences to their correct MRs Learning problem differs from that of syntactic parsing where training data has trees annotated over the NL sentences

Related Work: CHILL [Zelle & Mooney, 1996]
Uses Inductive Logic Programming (ILP) to induce a semantic parser Learns rules to control actions of a deterministic shift-reduce parser Processes sentence one word at a time making hard parsing decision each time Brittle and ILP techniques do not scale to large corpora

Related Work: SILT [Kate, Wong & Mooney, 2005]
Transformation rules associate NL patterns with MRL templates NL patterns matched in the sentence are replaced by the MRL templates By the end of parsing, NL sentence gets transformed into its MR Two versions: string patterns and syntactic tree patterns NL pattern our left [3] penalty area MRL template AREA  (left (penalty-area our))

Related Work: SILT contd.
Weaknesses of SILT: Hard-matching transformation rules are brittle: For e.g. for NL pattern our left [3] penalty area “our left penalty area” “our left side of penalty area ” “left of our penalty area” “our ah.. left penalty area” Parsing is done deterministically which is less robust than probabilistic parsing

Related Work: WASP [Wong, 2005]
Based on Synchronous Context-free Grammars Uses Machine Translation technique of statistical word alignment to find good transformation rules Builds a maximum entropy model for parsing The transformation rules are hard-matching

Related Work: SCISSOR [Ge & Mooney, 2005]
Based on a fairly standard approach to compositional semantics [Jurafsky and Martin, 2000] A statistical parser is used to generate a semantically augmented parse tree (SAPT) Augment Collins’ head-driven model 2 (Bikel’s implementation, 2004) to incorporate semantic labels Translate SAPT into a complete formal meaning representation our player 2 has the ball PRP$-team NN-player CD-unum VB-bowner DT-null NN-null NP-null VP-bowner NP-player S-bowner our player 2 has the ball PRP$-team NN-player CD-unum VB-bowner DT-null NN-null NP-null VP-bowner NP-player S-bowner

Related Work: Zettlemoyer & Collins [2005]
Uses Combinatorial Categorial Grammar (CCG) formalism to learn a statistical semantic parser Generates CCG lexicon relating NL words to semantic types through general hand-built template rules Uses maximum entropy model for compacting this lexicon and doing probabilistic CCG parsing

Traditional Machine Learning with Structured Data
Examples Feature Engineering Information loss Feature Vectors Machine Learning Algorithm

Kernel-based Machine Learning with Structured Data
Examples Kernelized Machine Learning Algorithm Kernel Computations Implicit mapping to potentially infinite number of features

Kernel Functions A kernel K is a similarity function over domain X which maps any two objects x, y in X to their similarity score K(x,y) For x1, x2 ,…, xn in X, the n-by-n matrix (K(xi,xj))ij should be symmetric and positive-semidefinite, then the kernel function calculates the dot-product of the implicit feature vectors in some high-dimensional feature space Machine learning algorithms which use the data only to compute similarity can be kernelized (e.g. Support Vector Machines, Nearest Neighbor etc.)

String Subsequence Kernel
Define kernel between two strings as the number of common subsequences between them [Lodhi et al., 2002] All possible subsequences become the implicit feature vectors and the kernel computes their dot-products s = “left side of our penalty area” t = “our left penalty area” K(s,t) = ?

Define kernel between two strings as the number of common subsequences between them [Lodhi et al., 2002] All possible subsequences become the implicit feature vectors and the kernel computes their dot-products s = “left side of our penalty area” t = “our left penalty area” u = left K(s,t) = 1+?

Define kernel between two strings as the number of common subsequences between them [Lodhi et al., 2002] All possible subsequences become the implicit feature vectors and the kernel computes their dot-products s = “left side of our penalty area” t = “our left penalty area” u = our K(s,t) = 2+?

Define kernel between two strings as the number of common subsequences between them [Lodhi et al., 2002] All possible subsequences become the implicit feature vectors and the kernel computes their dot-products s = “left side of our penalty area” t = “our left penalty area” u = penalty K(s,t) = 3+?

Define kernel between two strings as the number of common subsequences between them [Lodhi et al., 2002] All possible subsequences become the implicit feature vectors and the kernel computes their dot-products s = “left side of our penalty area” t = “our left penalty area” u = area K(s,t) = 4+?

Define kernel between two strings as the number of common subsequences between them [Lodhi et al., 2002] All possible subsequences become the implicit feature vectors and the kernel computes their dot-products s = “left side of our penalty area” t = “our left penalty area” u = left penalty K(s,t) = 5+?

Define kernel between two strings as the number of common subsequences between them [Lodhi et al., 2002] All possible subsequences become the implicit feature vectors and the kernel computes their dot-products s = “left side of our penalty area” t = “our left penalty area” K(s,t) = 11

Normalized String Subsequence Kernel
Normalize the kernel (range [0,1]) to remove any bias due to different string lengths Lodhi et al. [2002] give O(n|s||t|) for computing string subsequence kernel Used for Text Categorization [Lodhi et al, 2002] and Information Extraction [Bunescu & Mooney, 2005b]

Support Vector Machines
Mapping data to high-dimensional feature spaces can lead to overfitting of training data (“curse of dimensionality”) Support Vector Machines (SVMs) are known to be resistant to this overfitting

SVMs: Maximum Margin Given positive and negative examples, SVMs find a separating hyperplane such that the margin ρ between the closest examples is maximized Maximizing the margin is good according to intuition and PAC theory ρ Separating hyperplane

SVMs: Probability Estimates
Probability estimate of a point belonging to a class can be obtained using its distance from the hyperplane [Platt, 1999]

Why Kernel-based Approach to Learning Semantic Parsers?
Natural language sentences are structured Natural languages are flexible, various ways to express the same semantic concept CLang MR: (left (penalty-area our)) NL: our left penalty area our left side of penalty area left side of our penalty area left of our penalty area our penalty area towards the left side our ah.. left penalty area

Why Kernel-based Approach to Learning Semantic Parsers?
right side of our penalty area left of our penalty area opponent’s right penalty area our left side of penalty area our ah.. left penalty area our left penalty area our right midfield left side of our penalty area our penalty area towards the left side Kernel methods can robustly capture the range of NL contexts.

KRISP: Kernel-based Robust Interpretation by Semantic Parsing
Learns semantic parser from NL sentences paired with their respective MRs given MRL grammar Productions of MRL are treated like semantic concepts SVM classifier is trained for each production with string subsequence kernel These classifiers are used to compositionally build MRs of the sentences

Train string-kernel-based
Overview of KRISP MRL Grammar Collect positive and negative examples NL sentences with MRs Best semantic derivations (correct and incorrect) Train string-kernel-based SVM classifiers Training Semantic Parser Testing Novel NL sentences Best MRs

Overview of KRISP’s Semantic Parsing
We first define Semantic Derivation of an NL sentence We define probability of a semantic derivation Semantic parsing of an NL sentence involves finding its most probable semantic derivation Straightforward to obtain MR from a semantic derivation

Semantic Derivation of an NL Sentence
MR parse with non-terminals on the nodes: ANSWER answer STATE RIVER NEXT_TO TRAVERSE_2 STATEID stateid ‘texas’ next_to traverse_2 Which rivers run through the states bordering Texas?

MR parse with productions on the nodes: ANSWER  answer(RIVER) RIVER  TRAVERSE_2(STATE) TRAVERSE_2  traverse_2 STATE  NEXT_TO(STATE) NEXT_TO  next_to STATE  STATEID STATEID  ‘texas’ Which rivers run through the states bordering Texas?

Semantic Derivation: Each node covers an NL substring: ANSWER  answer(RIVER) RIVER  TRAVERSE_2(STATE) TRAVERSE_2  traverse_2 STATE  NEXT_TO(STATE) NEXT_TO  next_to STATE  STATEID STATEID  ‘texas’ Which rivers run through the states bordering Texas?

Semantic Derivation: Each node contains a production and the substring of NL sentence it covers: (ANSWER  answer(RIVER), [1..9]) (RIVER  TRAVERSE_2(STATE), [1..9]) (TRAVERSE_2  traverse_2, [1..4]) (STATE  NEXT_TO(STATE), [5..9]) (NEXT_TO  next_to, [5..7]) (STATE  STATEID, [8..9]) (STATEID  ‘texas’, [8..9]) Which rivers run through the states bordering Texas?

Substrings in NL sentence may be in a different order: ANSWER  answer(RIVER) RIVER  TRAVERSE_2(STATE) TRAVERSE_2  traverse_2 STATE  NEXT_TO(STATE) NEXT_TO  next_to STATE  STATEID STATEID  ‘texas’ Through the states that border Texas which rivers run?

Nodes are allowed to permute the children productions from the original MR parse (ANSWER  answer(RIVER), [1..10]) (RIVER  TRAVERSE_2(STATE), [1..10]] (STATE  NEXT_TO(STATE), [1..6]) (TRAVERSE_2  traverse_2, [7..10]) (NEXT_TO  next_to, [1..5]) (STATE  STATEID, [6..6]) (STATEID  ‘texas’, [6..6]) Through the states that border Texas which rivers run?

Probability of a Semantic Derivation
Let Pπ(s[i..j]) be the probability that production π covers the substring s[i..j], For e.g., PNEXT_TO  next_to (“the states bordering”) Obtained from the string-kernel-based SVM classifiers trained for each production π Probability of a semantic derivation D: (NEXT_TO  next_to, [5..7]) the states bordering

Computing the Most Probable Semantic Derivation
Task of semantic parsing is to find the most probable semantic derivation Let En,s[i..j], partial derivation, denote any subtree of a derivation tree with n as the LHS non-terminal of the root production covering sentence s from index i to j Example of ESTATE,s[5..9] : Derivation D is then EANSWER, s[1..|s|] (STATE  NEXT_TO(STATE), [5..9]) (STATE  STATEID, [8..9]) (STATEID  ‘texas’, [8..9]) (NEXT_TO  next_to, [5..7]) the states bordering Texas?

Computing the Most Probable Semantic Derivation contd.
Let E*STATE,s[5.,9], denote the most probable partial derivation among all ESTATE,s[5.,9] This is computed recursively as follows: E*STATE,s[5..9] (STATE  NEXT_TO(STATE), [5..9]) the states bordering Texas?

Let E*STATE,s[5.,9], denote the most probable partial derivation among all ESTATE,s[5.,9] This is computed recursively as follows: E*STATE,s[5..9] (STATE  NEXT_TO(STATE), [5..9]) E*NEXT_TO,s[i..j] E*STATE,s[i..j] the states bordering Texas?

Let E*STATE,s[5.,9], denote the most probable partial derivation among all ESTATE,s[5.,9] This is computed recursively as follows: E*STATE,s[5..9] (STATE  NEXT_TO(STATE), [5..9]) E*NEXT_TO,s[5..5] E*STATE,s[6..9] the states bordering Texas?

Let E*STATE,s[5.,9], denote the most probable partial derivation among all ESTATE,s[5.,9] This is computed recursively as follows: E*STATE,s[5..9] (STATE  NEXT_TO(STATE), [5..9]) E*NEXT_TO,s[i..j] E*STATE,s[i..j] the states bordering Texas?

Let E*STATE,s[5.,9], denote the most probable partial derivation among all ESTATE,s[5.,9] This is computed recursively as follows: E*STATE,s[5..9] (STATE  NEXT_TO(STATE), [5..9]) E*STATE,s[i..j] E*NEXT_TO,s[i..j] the states bordering Texas?

Implemented by extending Earley’s [1970] context-free grammar parsing algorithm Predicts subtrees top-down and completes them bottom-up Dynamic programming algorithm which generates and compactly stores each subtree once Extended because: Probability of a production depends on which substring of the sentence it covers Leaves are not terminals but substrings of words

Does a greedy approximation search, with beam width ω, and returns ω most probable derivations it finds Uses a threshold θ to prune low probability trees

Train string-kernel-based
Overview of KRISP MRL Grammar Collect positive and negative examples NL sentences with MRs Best semantic derivations (correct and incorrect) Train string-kernel-based SVM classifiers Pπ(s[i..j]) Training Semantic Parser Testing Novel NL sentences Best MRs

KRISP’s Training Algorithm
Takes NL sentences paired with their respective MRs as input Obtains MR parses Proceeds in iterations In the first iteration, for every production π: Call those sentences positives whose MR parses use that production Call the remaining sentences negatives

KRISP’s Training Algorithm contd.
First Iteration STATE  NEXT_TO(STATE) which rivers run through the states bordering texas? what is the most populated state bordering oklahoma ? what is the largest city in states that border california ? … what state has the highest population ? what states does the delaware river run through ? which states have cities named austin ? what is the lowest point of the state with the largest area ? Positives Negatives String-kernel-based SVM classifier PSTATENEXT_TO(STATE) (s[i..j])

Using these classifiers Pπ(s[i..j]), obtain the ω best semantic derivations of each training sentence Some of these derivations will give the correct MR, called correct derivations, some will give incorrect MRs, called incorrect derivations For the next iteration, collect positives from most probable correct derivation Collect negatives from incorrect derivations with higher probability than the most probable correct derivation

Most probable correct derivation: (ANSWER  answer(RIVER), [1..9]) (RIVER  TRAVERSE_2(STATE), [1..9]) (TRAVERSE_2  traverse_2, [1..4]) (STATE  NEXT_TO(STATE), [5..9]) (NEXT_TO  next_to, [5..7]) (STATE  STATEID, [8..9]) (STATEID  ‘texas’, [8..9]) Which rivers run through the states bordering Texas?

Most probable correct derivation: Collect positive examples (ANSWER  answer(RIVER), [1..9]) (RIVER  TRAVERSE_2(STATE), [1..9]) (TRAVERSE_2  traverse_2, [1..4]) (STATE  NEXT_TO(STATE), [5..9]) (NEXT_TO  next_to, [5..7]) (STATE  STATEID, [8..9]) (STATEID  ‘texas’, [8..9]) Which rivers run through the states bordering Texas?

Incorrect derivation with probability greater than the most probable correct derivation: (ANSWER  answer(RIVER), [1..9]) (RIVER  TRAVERSE_2(STATE), [1..9]) (TRAVERSE_2  traverse_2, [1..7]) (STATE  STATEID, [8..9]) (STATEID  ‘texas’, [8..9]) Which rivers run through the states bordering Texas? Incorrect MR: answer(traverse_2(stateid(‘texas’)))

Most Probable Correct derivation: Incorrect derivation: Which rivers run through the states bordering Texas? (ANSWER  answer(RIVER), [1..9]) (RIVER  TRAVERSE_2(STATE), [1..9]) (TRAVERSE_2  traverse_2, [1..4]) (STATE  NEXT_TO (STATE), [5..9]) (STATE  STATEID, [8..9]) (STATEID  ‘texas’, [8..9]) (NEXT_TO  next_to, [5..7]) Which rivers run through the states bordering Texas? (ANSWER  answer(RIVER), [1..9]) (RIVER  TRAVERSE_2(STATE), [1..9]) (TRAVERSE_2  traverse_2, [1..7]) (STATE  STATEID, [8..9]) (STATEID ‘texas’,[8..9]) Traverse both trees in breadth-first order till the first nodes where their productions differ are found.

Most Probable Correct derivation: Incorrect derivation: Which rivers run through the states bordering Texas? (ANSWER  answer(RIVER), [1..9]) (RIVER  TRAVERSE_2(STATE), [1..9]) (TRAVERSE_2  traverse_2, [1..4]) (STATE  NEXT_TO (STATE), [5..9]) (STATE  STATEID, [8..9]) (STATEID  ‘texas’, [8..9]) (NEXT_TO  next_to, [5..7]) Which rivers run through the states bordering Texas? (ANSWER  answer(RIVER), [1..9]) (RIVER  TRAVERSE_2(STATE), [1..9]) (TRAVERSE_2  traverse_2, [1..7]) (STATE  STATEID, [8..9]) (STATEID ‘texas’,[8..9]) Mark the words under these nodes.

Most Probable Correct derivation: Incorrect derivation: Which rivers run through the states bordering Texas? (ANSWER  answer(RIVER), [1..9]) (RIVER  TRAVERSE_2(STATE), [1..9]) (TRAVERSE_2  traverse_2, [1..4]) (STATE  NEXT_TO (STATE), [5..9]) (STATE  STATEID, [8..9]) (STATEID  ‘texas’, [8..9]) (NEXT_TO  next_to, [5..7]) Which rivers run through the states bordering Texas? (ANSWER  answer(RIVER), [1..9]) (RIVER  TRAVERSE_2(STATE), [1..9]) (TRAVERSE_2  traverse_2, [1..7]) (STATE  STATEID, [8..9]) (STATEID ‘texas’,[8..9]) Consider all the productions covering the marked words. Collect negatives for productions which cover any marked word in incorrect derivation but not in the correct derivation.

Most Probable Correct derivation: Incorrect derivation: Which rivers run through the states bordering Texas? (ANSWER  answer(RIVER), [1..9]) (RIVER  TRAVERSE_2(STATE), [1..9]) (TRAVERSE_2  traverse_2, [1..4]) (STATE  NEXT_TO (STATE), [5..9]) (STATE  STATEID, [8..9]) (STATEID  ‘texas’, [8..9]) (NEXT_TO  next_to, [5..7]) Which rivers run through the states bordering Texas? (ANSWER  answer(RIVER), [1..9]) (RIVER  TRAVERSE_2(STATE), [1..9]) (TRAVERSE_2  traverse_2, [1..7]) (STATE  STATEID, [8..9]) (STATEID ‘texas’,[8..9]) Consider the productions covering the marked words. Collect negatives for productions which cover any marked word in incorrect derivation but not in the correct derivation.

Next Iteration STATE  NEXT_TO(STATE) Positives Negatives the states bordering texas? state bordering oklahoma ? states that border california ? states which share border next to state of iowa … what state has the highest population ? what states does the delaware river run through ? which states have cities named austin ? what is the lowest point of the state with the largest area ? which rivers run through states bordering … String-kernel-based SVM classifier PSTATENEXT_TO(STATE) (s[i..j])

In the next iteration, SVM classifiers are trained with the new positive examples and the accumulated negative examples Iterate specified number of times

Experimental Corpora CLang Geo250 [Zelle & Mooney, 1996]
300 randomly selected pieces of coaching advice from the log files of the 2003 RoboCup Coach Competition 22.52 words on average in NL sentences 13.42 tokens on average in MRs Geo250 [Zelle & Mooney, 1996] 250 queries for the given U.S. geography database 6.76 words on average in NL sentences 6.20 tokens on average in MRs Geo880 [Tang & Mooney, 2001] Superset of Geo250 with 880 queries 7.48 words on average in NL sentences 6.47 tokens on average in MRs

Experimental Methodology
Evaluated using standard 10-fold cross validation Correctness CLang: output exactly matches the correct representation Geoquery: the resulting query retrieves the same answer as the correct representation Metrics

Experimental Methodology contd.
Compared Systems: SILT [Kate, Wong & Mooney, 2005] WASP [Wong, 2005] SCISSOR [Ge & Mooney, 2005] CHILL COCKTAIL ILP algorithm [Tang & Mooney, 2001] Zettlemoyer & Collins (2005) Different Experimental Setup (600 training, 280 testing examples) Results available only for Geo880 corpus Geobase Hand-built NL interface [Borland International, 1988] Results available only for Geo250

Experimental Methodology contd.
KRISP gives probabilities for its semantic derivation which are taken as confidences of the MRs We plot precision-recall curves by first sorting the best MR for each sentence by confidences and then finding precision for every recall value WASP and SCISSOR also output confidences so we show their precision-recall curves Results of other systems shown as points on precision-recall graphs

requires more annotation on corpus
Results on CLang requires more annotation on corpus CHILL gives 49.2% precision and 12.67% recall with 160 examples, can’t run beyond.

Results on Geo250

Results on Geo880

Results on Multilingual Geo250
We have Geo250 corpus translated into Japanese, Spanish and Turkish KRISP is directly applicable to other languages

Results on Multilingual Geo250

Background on Kernel-based Methods Completed Research Proposed Research Short-term Long term Conclusions

Short Term: Exploiting Natural Language Syntax
KRISP currently uses only word order of the sentence Semantic interpretation depends largely on NL syntax, exploiting it should help semantic parsing We already have syntactic annotations on our corpora, used in SILT-tree and SCISSOR Existing syntactic parsers can be trained on our corpora in addition to WSJ [Bikel, 2004]

Exploiting Natural Language Syntax contd.
Most natural extension of KRISP is to use syntactic-tree kernel instead of string kernel Syntactic-tree kernel Introduced by Collins & Duffy [2001] K(x,y) = Number of subtrees common between x & y NP PP JJ NN left side IN of DT the midfield PRP$ our penalty area K(x,y) = ?

Most natural extension of KRISP is to use syntactic-tree kernel instead of string kernel Syntactic-tree kernel Introduced by Collins & Duffy [2001] K(x,y) = Number of subtrees common between x & y NP NP NP PP NP PP IN NP IN NP JJ NN JJ NN of of PRP$ NN DT NN left side NN left side our penalty area the midfield K(x,y) = 1+?

Most natural extension of KRISP is to use syntactic-tree kernel instead of string kernel Syntactic-tree kernel Introduced by Collins & Duffy [2001] K(x,y) = Number of subtrees common between x & y NP NP NP PP NP PP IN NP IN NP JJ NN JJ NN of of PRP$ NN DT NN left side NN left side our penalty area the midfield K(x,y) = 8

Often the syntactic information needed is present in dependency trees, full syntactic trees not necessary Dependency trees capture most important functional relationship between words Various dependency tree kernels have been used successfully for doing Information Extraction [Zelenko, Aone & Richardella, 2003], [Cumby & Roth, 2003], [Culotta & Sorenson, 2004], [Bunescu & Mooney, 2005a]

Short Term: Noisy NL Sentences
If users are interacting with the semantic parser through speech then many ways noise can be present [Zue & Glass, 2000] Speech recognition errors Interjections (um’s and ah’s) Environment noise (door slams, phone rings etc.) Out-of-domain words and ill-formed utterances In KRISP, presence of extra words or corrupted words may decrease kernel values but won’t affect semantic parsing in a hard way KRISP is hence more robust to noise compared to systems with hard-matching rules like SILT and WASP, or systems doing complete syntactic-semantic parsing like SCISSOR

Noisy NL Sentences contd.
We plan to do preliminary experiments by artificially corrupting our existing corpora Then we plan to get and experiment on some real world noisy corpus

Short Term: Committees of Semantic Parsers
System Correct CLang MRs out of 300 KRISP 178 WASP 185 SCISSOR 232 Committee Upper-bound on Correct MRs KRISP+WASP 223 KRISP+SCISSOR 253 WASP+SCISSOR 246 KRISP+WASP+SCISSOR 259 Good indication that forming their committee will improve performance.

Committees of Semantic Parsers contd.
Two general approaches to combine parse trees [Henderson & Brill, 1999] Parser Switching: Learn which parser works best on which types of sentences Parse Hybridization: Look into output MRs and combine their best components Particularly useful when none of the parser generates complete MRs Prior work is specific to combining syntactic parses. We plan to explore these two general approaches for combining MRs.

Long Term: Non-parallel Training Corpus
Training data contained NL sentences aligned with their respective MRs In some domains many NL sentences and semantic MRs may be available but not aligned For e.g. in RoboCup commentary task [Binsted et al., 2000], NL sentences and symbolic description of events are available but not aligned Referential ambiguity: Which NL description refers to which symbolic description? In our present work we resolve which portion of the sentence refers to which production of MR parse Same approach could be extended to one level higher

Non-parallel Training Corpus contd.
Let training corpus be {(Mi, Si)|i=1..N} where each Mi is a set of MRs and each Si is a set of NL sentences Align every MR in Mi to every NL sentence in Si for i=1..N Use KRISP’s training algorithm to learn classifiers Find best alignment for MRs and NL sentences in (Mi, Si) by semantic parsing using these classifiers Repeat till alignments don’t change We plan to first do preliminary experiments by artificially making our corpus non-parallel and then extracting the alignments then test on a real-world corpus

Long Term: Complex Relation Extraction
Bunescu & Mooney [2005b] use string-based kernel to extract binary relation “protein-protein interaction” from text This can be viewed as learning for an MRL grammar with only one production INTERACTION  PROTEIN PROTEIN Complex relation is an n-ary relation among n typed entities [McDonald et al., 2005] For example, (person, job, company) NL sentence: John Smith is the CEO of Inc. Corp. Extraction: (John Smith, CEO, Inc. Corp.)

Complex Relation Extraction contd.
(person, job,company) (person, job) (job, company) John Smith is the CEO of Inc. Corp. KRISP should be applicable to extract complex relations by treating it like higher level production composed of lower level productions.

Conclusions KRISP: A new kernel-based approach to learning semantic parser String-kernel-based SVM classifiers trained for each MRL production Classifiers used to compositionally build complete MRs of NL sentences Evaluated on two real-world corpora Performs better than deterministic rule-based systems Performs comparable to recent statistical systems Proposed work: exploit NL syntax, form committees and broaden application domains

Thank You! Questions??

Extra: Dealing with Constants
MRL grammar may contain productions corresponding to constants in the domain: STATEID  ‘new york’ RIVERID  ‘colorado’ NUM  ‘2’ STRING  ‘DR4C10’ User can specify these as constant productions giving their NL substrings Classifiers are not learned for these productions Matching substring’s probability is taken as 1 If n constant productions have same substring then each gets probability of 1/n STATEID  ‘colorado’ RIVERID  ‘colorado’

Extra: Better String Subsequence Kernel
Subsequences with gaps should be downweighted Decay factor λ in the range of (0,1] penalizes gaps All subsequences are the implicit features and penalties are the feature values s = “left side of our penalty area” t = “our left penalty area” u = left penalty K(s,t) = 4+?

Subsequences with gaps should be downweighted Decay factor λ in the range of (0,1] penalizes gaps All subsequences are the implicit features and penalties are the feature values s = “left side of our penalty area” t = “our left penalty area” u = left penalty K(s,t) = 4+λ3*λ0 +? Gap of 3 => λ3 Gap of 0 => λ0

Subsequences with gaps should be downweighted Decay factor λ in the range of (0,1] penalizes gaps All subsequences are the implicit features and penalties are the feature values s = “left side of our penalty area” t = “our left penalty area” K(s,t) = 4+3λ+3 λ3+ λ5

Extra: KRISP’s Training Algorithm contd.
What if none of the ω most probable derivations of a sentence is correct? Extended Earley’s algorithm can be forced to derive only the correct derivations by making sure all subtrees it generates exist in the correct MR parse

Extra: N-best MRs for Geo880

Extra: KRISP’s Average Running Times
Corpus Average Training Time (minutes) Average Testing Time (minutes) Geo250 1.44 0.05 Geo880 18.1 0.65 CLang 58.85 3.18 Average running times per fold in minutes taken by KRISP.

Extra: KRISP’s Learning PR Curves on CLang

Extra: KRISP’s Learning PR Curves on Geo250

Extra: KRISP’s Learning PR Curves on Geo880

Extra: Experimental Methodology
Correctness CLang: output exactly matches the correct representation Geoquery: the resulting query retrieves the same answer as the correct representation If the ball is in our penalty area, all our players except player 4 should stay in our half. Correct: ((bpos (penalty-area our)) (do (player-except our{4}) (pos (half our))) ((bpos (penalty-area opp)) (do (player-except our{4}) (pos (half our))) Output:

Extra: Formal Language Grammar
NL: If our player 4 has the ball, our player 4 should shoot. CLang: ((bowner our {4}) (do our {4} shoot)) CLang Parse: Non-terminals: RULE, CONDITION, ACTION… Terminals: bowner, our, 4… Productions: RULE  CONDITION DIRECTIVE DIRECTIVE  do TEAM UNUM ACTION ACTION  shoot RULE CONDITION DIRECTIVE do TEAM UNUM ACTION bowner our 4 shoot

A Kernel-based Approach to Learning Semantic Parsers

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A Kernel-based Approach to Learning Semantic Parsers

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