Knowledge Representation and Inference Models for Textual Entailment

1. Knowledge Representation and Inference Models for Textual Entailment Dan Roth University of Illinois Urbana-Champaign with Rodrigo Braz, Roxana Girju, Vasin Punyakanok, Mark Sammons

2. Fundamental Task By “textually entailed” we mean: most people would agree that one sentence implies the other. (more later) Entails Subsumed by WalMart defended itself in court today against claims that its female employees were kept out of jobs in management because they are women  WalMart was sued for sexual discrimination

3. Why Textual Entailment? • A fundamental task that can be used as a building block in multiple NLP and information extraction applications • There is always a risk in solving a separate ’fundamental’ task rather than the task one really wants to solve… • Some of the examples here are very direct, though. • Has multiple direct applications

4. Question Answering • Given: Q: Who acquired Overture? • Determine: A: Eyeing the huge market potential, currently led by Google, Yahoo took over search company Overture Services Inc last year. (and distinguish from other candidates) Entails Subsumed by Eyeing the huge market potential, currently led by Google, Yahoo took over search company Overture Services Inc last year  Yahoo acquired Overture

5. Story Comprehension • A process that maintains and updates a collection of propositions about the state of affairs. • Viewed this way, a fundamental task to consider is that of textual entailment: Given a snippet of text S, does it entail a proposition T? (ENGLAND, June, 1989) - Christopher Robin is alive and well. He lives in England. He is the same person that you read about in the book Winnie the Pooh. As a boy, Chris lived in a pretty home called Cotchfield Farm. When Chris was three years old, his father wrote a poem about him. The poem was printed in a magazine for others to read. Mr. Robin then wrote a book. He made up a fairy tale land where Chris lived. His friends were animals. There was a bear called Winnie the Pooh. There was also an owl and a young pig, called a piglet. All the animals were stuffed toys that Chris owned. Mr. Robin made them come to life with his words. The places in the story were all near Cotchfield Farm. Winnie the Pooh was written in 1925. Children still love to read about Christopher Robin and his animal friends. Most people don't know he is a real person. He has written books of his own that tell what it is like to be famous. [REMEDIA] 1. Christopher Robin was born in England. 2. Winnie the Pooh is a title of a book. 3. Christopher Robin’s dad was a magician. 4. Christopher Robin must be at least 65 now.

6. You may disagree with the truth of this statement; and you may infer also that: the presidential candidate’s wife was born in N.C. More Examples • A key problem in natural language understanding is to abstract over the inherent syntactic and semantic variability in natural language. • Multiple tasks attempt to do just that. • Relation Extraction: Dole’s wife, Elizabeth, is a native of Salisbury, N.C.  Elizabeth Dole wasborn inSalisbury, N.C • Information Integration (Data Bases) Different database schemas represent the same information under different titles. • Information retrieval: Multiple issues, from variability in the query and target text, to relations • Summarization • Multiple techniques can be applied; all are entailment problems.

7. Direct Application: Semantic Verification • Given: A long contract that you need to ACCEPT • Determine: Does it satisfy the 3 conditions that you really care about? (and distinguish from other candidates) ACCEPT?

8. Why Study Textual Entailment? • A fundamental task for language comprehension. • Builds on a lot of research (and tools) done in the last few years in Learning and Inference in Natural Language. • Opens up a large collection of questions both from the natural language perspective and from the machine learning, knowledge representation and inference perspectives.

9. This Talk • A brief perspective & technical motivation • An Approach to Textual Entailment • The CCG Inference model for textual entailment • Inference as optimization • Some examples • Knowledge modules • Conclusions

10. Two Extremes in Representation and Inference • Statistics: Using relatively simple statistical techniques for BOW and/or paraphrases • Multiple problems that may not be addressed just from the data: Entailment vs. Correlation [Geffet & Dagan’s 04,05] • An important component, but: • How to put together/chain/weigh paraphrases? Inference model. • Inference in NL requires mapping sentences to logical forms and using general purpose theorem proving. • Extensions include various relaxations in the way the representation is generated and in the type of information incorporated in a KB, to support the theorem prover; non-logical, probabilistic paradigms. • Key problems include the realization that underspecificty of the language is a feature, rather than a bug. •  representation, but not a canonical representation

11. New (Better?) View on Problems • Access to information requires tolerating “loose speak” [Porter et. al, ‘04] • Refers to the imprecise way queries/questions are formed – with respect to the representation of the information source. • Metonymy: referring to the an entity or event by one of its attributes • Causal factor: referring to a result by one of its causes • Aggregate: referring to an aggregate by one of its members • Generic: referring to a specific concept by the generic class to which it belongs [The potato was cultivated first in SA] • Noun compounds: referring to a relation between nouns by using just the noun phrase consisting of the two nouns. [wooden table] • Many other kinds of ambiguities – some language related and some knowledge related.

12. Example: New (Better?) View on Problems • Collin Powel addressed the general assembly yesterday  Collin Powelgave a speech at the UN The secretary of state gave a speech at the UN • Resolving the sense ambiguity in “addressed” ? • Or a weaker, “existential”, Yes/No with respect to “gave a speech” is sufficient [Ido Dagan; Seneval’04] • How about Collin Powel? • In many disambiguation problems, the view taken when studying entailment is that keeping the underspecificity of language is possible, and perhaps the right thing to do.

13. Task-based Refinement

14. Reflection from the Past Learning in order to Reason [’94-’97] • An unified framework to study Learning, Knowledge Representation and Reasoning. • A series of theoretical results on the advantages of a unified framework for L, KR & R, in a situations where: • The goal is to Reason - deduction; abduction (best explanation) • Starting point for Reasoning is not a static Knowledge Base but rather A representation ofknowledge learned via interaction with the world. • Quality of the learned representation is determined by the reasoning stage. • Intermediate Representation is important – but only to the extent that it is learnable, and it facilitates reasoning. • There may not be a need (or even a possibility) to learn an exact intermediate representation, but only to the extent that is supports Reasoning. [Khardon & Roth JACM97, AAAI94; Roth95, Roth96, Khardon&Roth99 Learning to Plan: Khardon’99] Lesson:

15. This Talk • A brief perspective & technical motivation • An Approach to Textual Entailment • The CCG Inference model for textual entailment • Inference as optimization • Some examples • Knowledge modules • Conclusions

16. Defining Textual Entailment • Mapping text to a canonical representation is often not the right approach (or: not possible) • Not a computational issue • Rather, the representation might depend on the task, in our case, on the hypothesis sentence. • Suggests a definition for textual entailment: Let s, t, be text snippets with representations rs, rt2 R. We say that s textually entails t if there is a representation r 2 R of s, for which we can prove that rµ rt

17. Defining Semantic Entailment • R - a knowledge representation language, with a well defined syntax and semantics or a domain D. • For text snippets s, t: • rs, rt - their representations in R. • M(rs), M(rt) their model theoretic representations • There is a well defined notion of subsumption in R, defined model theoretically • u, v 2 R: u is subsumed by v when M(u) µ M(v) • Not an algorithm; need a proof theory.

18. Defining Semantic Entailment (2) • The proof theory is weak; will show rs µ rt only when they are relatively “similar”. • r 2 R is faithful to s if M(rs) = M(r) Definition: Let s, t, be text snippets with representations rs, rt2 R. We say that s textually entails t if there is a representation r 2 R that is faithful to s, for which we can prove that rµ rt • Given rs one needs to generate many equivalent representations r’s and test r’s µ rt Cannot be done exhaustively How to generate alternative representations?

19. The Role of Knowledge: Refining Representations • A rewrite rule (l,r) is a pair of expressions in R such that lµ r • Given a representation rs of s and a rule (r,l) for which rsµ l the augmentation of rs via (l,r) is r’s = rsÆ r. Claim: r’sis faithful to s. Proof: In general, since r’s = rsÆ r then M(r’s)= M(rs) Å M(r) However, since rsµ lµ r then M(rs) µ M(r). Consequently: M(r’s)= M(rs) And the augmented representation is faithful to s. µ rs l µr, rsµ l r’s = rsÆr

20. Comments • The claim suggests an algorithm for generating alternative (equivalent) representations, and for textual entailment. • The resulting algorithm is sound, but is not complete. • Completeness depends on the quality of the KB of rules. • The power of this re-representation algorithm is in the rules KB and in an inference procedure that incorporates them. • Choosing appropriate refinements • Depends on the target sentence • Is an optimization procedure .

21. Induce an abstract representation of S (a concept graph) Induce an abstract representation of S (a concept graph) Re-represent S Re-represent S General Strategy Cartoon Given a sentence S (answer) Given a sentence T (question) e Given a KB of semantic; structural and pragmatic transformations (rules). Find the optimal set of transformations that maps one sentence to the target sentence.

22. The One Slide Approach Summary • Inducing an Abstract Representation of Text • Multiple learning Steps; centered around a semantic parse (predicate-argument representation) of a sentence augmented by additional information. • Final representation is a hierarchical concept graph (DL inspired) • Refining the representation using an existing KB • Rewrite rules at multiple levels; application depends on target; [Features] • Modeling Entailment as Constrained Optimization • Entailment is a mapping between sentence representation • Find an optimal mapping [minimal cost proof; abduction]that respects • The hierarchy • Transformations (rules) applied to nodes/edges/sub-graphs • The confidence in the induced information • All modeled as (soft) constraints • Provides robustness against inherent variability in natural language, inevitable noise in learning processes and missing information.

23. Components • Learning, Representing and Reasoning take part at several levels in the process. • A unified knowledge representation of the text, that • provides an hierarchical encoding of the structural, relational and semantic properties of the given text • is integrated with learning mechanisms that can be used to induce such information from newly observed raw text, and • that is equipped with an inferential mechanism that can be used to support inferences with respect to such representations. • An Inference Model for Semantic Entailment[AAAI’05] • Experiments with a Semantic Entailment System[IJCAI’05-WS]

24. An Example s: Lung cancer put an end to the life of Jazz singer Marion Montgomery on Monday. t: Singer dies of carcenoma. s is re-represented in several ways; one of these is shown to be subsumed by t s’1: Lung cancer killed Jazz singer Marion Montgomery on Monday. s’2: Jazz singer Marion Montgomery died of lung cancer on Monday.

25. Representation Hierarchical; Multiple types of information; All hanging on the sentence itself. Formally, represented using Description Logic Expressions; Rewrite rules have the same representation.

26. Representation (2) • Representation is formal – not to be confused with a logical/canonical representation. • Attempt is made to represent the text, and augment/refine the representation as part of the inference process. • The skeleton of the representation is a predicate-argument representation • learned based on PropBank (the semantic role labelling task). • Resources used to augment the representation: • Segmentation; tokenization; • Lemmatizer;POS tagger • Shallow Parser • Syntactic parser (Collins;Charniak) • Named entity tagger • Entity identification. (co-Reference) • Resources used to Rewrite/Refine and for Subsumption • Wordnet • Dirt paraphrase rules (Lin) • Word clusters (Lin) • Ad hoc modules (later) In house machine learning based tools [http://L2R.cs.uiuc.edu/~cogcomp

27. A1: utterance C-A1: utterance A1: thing left A0 : leaver A0 : leaver A2: benefactor The pearls whichIleftto my daughter-in-laware fake. The pearls, Isaid, were left to my daughter-in-law. Ileftmy pearlsto my daughter-in-lawin my will. R-A1 A0 : sayer A2: benefactor A1: thing left AM-LOC Predicate-Argument Representation • For each predicate in a sentence [currently – verbs] Represent all constituents that fill a semantic role • Core Arguments, e.g., Agent, Patient or Instrument • Their adjuncts, e.g., Locative, Temporal or Manner

28. Semantic Role Labelling • Screen shot from a CCG demo http://L2R.cs.uiuc.edu/~cogcomp • This problem itself is modelled as a constrained optimization problem over the output of a large number of classifiers, and multiple constraints. • Solution: formulating it as a linear program and solving integer linear programs. • Top system in CoNLL shared Task; presentation later today

29. Rewrite Rules (KB) • Goal: Acquire transformations that preserve meaning • Basic linguistics processing levels: • Keyword matching; • Grammatical; • Semantic; • (Discourse, Pragmatic, …) • The mechanism supports chaining. Rules may contain variables; the augmentation mechanism supports inheritance. • Some examples later • Rules are used also to avoid semantic parsing problems. managed to enter  entered; failed to enterenternot

30. The Inference Problem • Optimizing over the transformations applied to the initial representation. • Optimizing over the transformations applied to determine final subsumption • Even after the refinement of the representation, requiring exact subsumption (embedding of the target graph in the source graph) is unrealistic. • Words can be replaced by synonyms; modifiers can be dropped, etc. • We develop a notion of functional subsumption: say “yes” when node & edges unify modulo some allowed transformations. • [Why do we separate to two stages?]

31. Modeling Inference as Optimization • Incrementally augment the original representation and generate faithful re-representations of it. • Compute whether the target representation subsumes the augmented concept graph via an extended subsumption algorithm. • Uncertainty is encoded by optimizing a linear cost function. Cost can be learned in a straight forward way via and EM-like algorithm. • The inference model seeks the optimal re-representation S'i such that: S'i = argmin{S‘ | C(S,S'i) + D(S'i,T) } • Over the space of all possible re-representations of S given KB (subject to multiple constraints – order, structure) • C returns the cost of augmenting S to S'i and • D returns the costs of performing extended subsumption from S'ito T.

32. Inference: Key Points • Hierarchical Subsumption • Decision List: if succeeds at a level, go on to the next; otherwise, fail • At the Predicate-Argument level • At the phrase level • At the word level • Match both attributes and edges (relational information) • Match may not be perfect • Inference (unification) as Optimization • The optimal unification U’ is the one minimizing:Hi{(X,Y) U| X  Hi} iG(X,Y) (X,Y, resp. substructures on S, T) where i is a fixed constant that ensures the hierarchical behavior is as a decision list. • (i makes sure that changes in H0 dominate changes in H1) • Integer Linear Programming formulation for Unification

33. Summary • KR:[Learning & Inference] • A description logic inspired hierarchical KR into which we re-represent the surface level text augmented with multiple abstractions. • KB:[Acquisition & Inference] • A knowledge base consisting of syntactic and semantic rewrite rules, written at several levels of abstractions • Inference: [modeled as optimization: flexibility & error tolerance] • An extended subsumption algorithm which determines subsumption between representations. An Inference Model for Semantic Entailment [AAAI’05] Experiments with a Semantic Entailment System [IJCAI’05-WS] • Evaluation:SRL (CoNLL Shared Task) ; Pascal Ablation study on the PARC collection

34. This Talk • A brief perspective & technical motivation • An Approach to Textual Entailment • The CCG Inference model for textual entailment • Inference as optimization • Some examples • Knowledge modules • Conclusions

35. Ablation study on the PARC Data • PARC Data • 76 Pairs of Q-A sentences • questions converted manually • treat label “unknown” as “false” • Designed to test linguistic (lexical and constructional) entailment • Out of 76 pairs: • 64 pairs – got perfect SRL labelling • System versions: Vary Two Dimensions • Structure: add more parsing capabilities • Semantic: add more semantic resources (some use parse structure)

36. System Versions • Suite of tests, incrementally adding system components • System versions: • LLM: Uses BOW++ to match entire sentences • SRL + LLM: Uses SRL tagging (filter) and BOW on verb arguments • SRL + Deep Structure: System parses arguments of Verbs • Uses full parse, shallow parse tagging to identify argument structure • Knowledge Base (of rewrite rules) active or inactive

37. Testing the Entailment System • Entailment (Knowledge Base) Modules (can only be activated when appropriate parse structure is present) • Verb Phrase Compression • Rewrite verb constructions – modal, VERB to VERB, tense • Discourse Analysis • Detect embedded predicates • Annotate effect of embedding predicate on embedded predicate • Qualifier Reasoning • Detect qualifiers and scope – some, no, all, any, etc. • Determine entailment of qualified arguments • Not shown: Functional Subsumption – rules (e.g., synonyms) used to allow other rules to fire.

38. Results for Different Entailment Systems • Perfect Corpus with applicable entailment modules, with Knowledge Base

39. Results for Different Entailment Systems • Full Corpus with applicable entailment modules, with Knowledge Base

40. Baseline Entailment System (1) * • Baseline system is Lexical Level Matching (LLM) • Ignores many “stopwords”, including “be” verbs, prepositions, determiners • Lemmatizes words before matching • Requiring structure may hurt: LLM allows entailment when SRL-based subsumption requires a rewrite rule: • For LLM, the only words of T that register are ”diplomat” and “Iraq” • As these are present in S, LLM will return “true” S: [The diplomat]/ARG1 visited [Iraq]/ARG1 [in September]/AM_TMP T: [The diplomat]/ARG1 was in [Iraq]/ARG2

41. Baseline System (1.1) * • But, LLM is insensitive to small changes in wording • LLM ignores modal “could”, so returns incorrect answer “true”. S: [Legally]/AM_ADV, [John]/ARG0 [could]/AM_MOD drive. T: [John]/ARG0 drove.

42. SRL + LLM (2.) • SRL + LLM system uses Semantic Role Labeler tagging • First, tries to match verb and argument types in the two sentences • If successful, system uses LLM to determine entailment of arguments • Advantage over LLM when argument or modifier attached to different verb in T than in S: • Words are identical, so LLM incorrectly labels example “true” • SRL+LLM returns “false” because arguments of “said”, “visit” don’t match. S: [The president]/ARG0 said [[the diplomat]/ARG0 left [Iraq]/ARG1]/ARG1 T: [The diplomat]/ARG0 said [[the president]/ARG0 left [Iraq]/ARG1]/ARG1

43. SRL + LLM (2.1) * • Disadvantage of using SRL+LLM compared to LLM: • SRL generates predicate frames verbs ignored as stopwords by LLM • Example: “went” in following sentence pair: • LLM ignores “went”, returns correct label “true” • SRL generates a verb frame for “went” • Subsumption fails as no match for this verb in S • In this data set, more instances like the second case than like the first • the result is a drop in performance • However, SRL forms crucial backbone for other functionality S: [The president]/ARG0 visited [Iraq]/ARG1 [in September]/AM_TMP T: [The president]/ARG0 went to [Iraq]/ARG1.

44. SRL+LLM with Verb Processing (3.0) * • The Verb Processing (VP) module rewrites certain verb phrases as a single verb with additional attributes • Uses word order and Part of Speech information to identify candidate patterns • Presently recognizes modal and tense constructions, and simple verb compounds of the form ”VERB to VERB” (such as “manage to enter”) • Verb phrase replaced by single predicate (verb) node with additional attributes • Modality (“CONFIDENCE”) • Tense • Requires POS and word order information • Default CONFIDENCE is “FACTUAL”

45. SRL+LLM with Verb Processing (3.1) * • Example where Verb Processing (VP) module helps: • Subsumption in LLM and SRL+LLM system succeeds, as argument and verb lemma in T match those in S • VP module rewrites “could drive” as “drive”, adds attribute “CONFIDENCE: POTENTIAL” to “drive” predicate node • In SRL+LLM+VP, subsumption fails at verb level, as CONFIDENCE attributes don’t match S: [Legally]/AM_ADV, [John]/ARG0 [could]/AM_MOD drive. T: [John]/ARG0 drove.

46. SRL+LLM with Verb Processing (3.2) • VP module rewrites auxiliary construction in T as a single verb with tense and modality attributes attached • Now, SRL generates only a single predicate frame for “sold” • This matches its counterpart in S, and subsumption succeeds, • qualifying effect of the verb ``said'' in S cannot be recognized without the deeper parse structure and the Discourse Analysis module. S: Bush said that Khan sold centrifuges to North Korea. T: Centrifuges sold to North Korea.

47. SRL + Deep Structure (4.0) * • SRL + Deep Structure entailment system identifies substructure in SRL predicate arguments • uses full- and shallow parse, Named Entity and Part of Speech information • identifies the key entity in each argument • Identifies modifiers of key entity such as adjectives, titles, and quantities • Enables further semantic modules, such as Qualifier module for reasoning about entailment of qualified arguments

48. SRL + Deep Structure (4.0) * • “Some” and “no” are stopwords (i.e., ignored by LLM), so LLM and SRL+LLM incorrectly label this example “true” • SRL + Deep Structure gives correct label, “false”, because “no” and “some” are identified as key entity modifiers for matching argument, and they don’t match S: No US congressman visited Iraq until the war. T: Some US congressmen visited Iraq before the war.

49. SRL + Deep Structure (4.2) • Handling modifiers: • No rules for modifiers: The LLM and SRL+LLM systems find no match for “intelligent” in S, and so return the correct answer, “false” • SRL + Deep Structure system allows unbalanced T adjective modifiers (assumption: S must be more general than T) and returns “true”. • Context sensitive handling of modifiers? S: The room was full of women. T: The room was full of intelligent women.

50. SRL + Deep Structure + Discourse Analysis (5.0) * • Detecting the effects of an embedding predicate on the embedded predicate • Presently, supports distinction between “FACTUAL” (default assumption) and a set of values that distinguish various types of uncertainty, such as “REPORTED” • All systems lacking Discourse Analysis (DA) module label this sentence pair “true”, because T is a literal fragment of S • Actual truth value depends on interpretation of “reported” • Other embedding constructions DA can handle: • Adjectival: “It is unlikely that Hanssen sold secrets…” • Nominal: “There was a suspicion that Hanssen sold secrets…” S: The New York Times reported that Hanssen sold FBI secrets to the Russians and could face the death penalty. T: Hanssen sold FBI secrets to the Russians.