Outline P1EDA’s simple features currently implemented –And their ablation test Features we have reviewed from Literature –(Let’s briefly visit them) –Iftene’s. –MacCarteny et al. (Stanford system) –BIUTEE gap mode features. Discussion: what we want to (re-)implement, and bring back into EOP. –As aligners, –or as features.

Current features for mk.1 Basic Idea: Simple features first. Word coverage ratio –How much of the H components (here, Tokens) are covered by those of T components? –“base alignment score” Content word coverage ratio –Content words are more important than, non- content words (prepositions, articles, etc) –“Penalize if missed content words”

Current features for mk.1 Proper Noun coverage ratio –Proper nouns (or name entities) are quite specific. Missing (no alignment) PNs should be penalized severely. –Iftene’s rules on NERs. Named entity drops are always non entailment. The only exception is dropping of first name. Verb coverage ratio –Two most effective features of an alignment-based system (Stanford) was –Is the main predicate of Hypothesis covered? –Are the arguments of that predicate covered?

Current results (with optimal settings on mk1 features and aligners ) English: 67.0 % (accuracy) –Aligners: identical.lemma, wordNet, VerbOcean, Meteor paraphrase –Features: word, content word, PN coverage. Italian: % (accuracy) –Aligners: identical.lemma, Italian WordNet –Features: word, content word, verb coverage German: 64.5 % (accuracy) –Aligners: identical.lemma, GermaNet –Features: word, content word, PN coverage.

Ablation test. impact of features ( accuracy (impact) ) ALL features (not necessarily best) Without Verb Coverage feature Without Proper Noun Coverage feat. Without Content word Coverage feat. EN (WN, VO, Para) (-0.25)66.0 (+0.75) (+1.625) IT (WN, Para) (+0.625) (- 0.25) (+2.5) DE (GN) ( ) (+1.125) 63.0 (+1.875)

Ablation test, impact of aligners (with best features of previous slide) EN (67.0 with all of the following + base) –without WordNet: (+1.875) –without VerbOcean: (+0.25) –without Paraphrase (meteror): (+2.125) IT ( with the following + base) –without WordNet(IT): (+0.125) –without Paraphrase (Vivi’s): (-0.5) DE (62.25 with the following + base) –without GermaNet: (+0.125) –without Paraphrase (meteor): 64.5 (-2.25)

FEATURES IN LITERATURE (PREVIOUS RTE SYSTEMS)

Iftene’s RTE system Approach: alignment score and threshold –Alignment has two parts: Positive contribution parts, Negative contribution parts –Use a (manually designed) score function to combine various scores into one final, global alignment score. –Learns a threshold to determine “entailment” (better then threshold) and “non-entailment” (all else)

Iftene’s RTE system Base unit of alignment: node-edge of tree –(Hypothesis) node – edge – node. –Text nodes dependency node-edge-nodes are compared with extended match. (partial match) –Alignment score forms the base-line for score. WordNet, and other resources are used on those matches Additional scores are designed to reflect various good / bad match

Iftene’s RTE system, features Numerical compatibility rule (positive rule) –Numbers and quantities are normally not mapped by lexical resource + local alignment “at least 80 percent” -> “more than 70 percent” “killed 109 people on board and four workers” -> “killed 113 people” –Special calculator was used to calculate the compatibility of the numeric expressions –Reported some impact (1% +) on accuracy. –Our choice: possible aligner candidate?

Iftene’s RTE system, features Negation rules –Truth of the verbs are are denoted on all verbs. –Traversing dependency tree and check existence of “not”, “never”, “may”, “might”, “cannot”, “could”, etc. Particle rules –Particle “to” gets special checking: strongly influenced by active verb, adverb, or noun before particle to –Search for positive (believe, glad, claim) and negative (failed, attempted) ques. “Non matching parts” → add negative score

Iftene’s RTE system, features Named Entity Rule –If an NE on Hypothesis not mapped –Outright rejection as non entailment Exception: if it is a human name, dropping (no alignment) of First name is Okay. –Our choice? NER aligner would be nice. (poor man’s ner coverage checking == current Proper Noun coverage feature)

Stanford TE system Stanford TE system (MacCarteny et al) –1) do monolingual alignment Trained on gold (manually prepared) alignment –2) get alignment score no negative elements in this alignment step. –3) apply feature extraction Design features that would reflect various linguistic phenomena

Stanford TE system, Polarity features Polarity features –Polarity of T-H is checked by existence of negative linguistic markers. Negation (not), downward-monotone marker (no, few), restricting prepositions (without, except) –Features on polarity: polarity of T, polarity of H, does two polarity T-H same? Our choice? –TruthTeller would be better. –But on the other hand, “word” based simple approaches might be useful for other languages.

Stanford TE system, Modality / Factivity features Modality preservation feature –Record modal changes from T to H, and generates a nominal feature. “could be XX” (T) -> “XX” (H) → “WEAK_NO” “cannot YY” (T) -> “not YY” (H) → “WEAK YES” Factivity preservation feature –Focus on verbs that affects “Truth” or “Factivity” “tried to escape” (T) -> “escape” (H) (Feature: false) “managed to escape” (T) -> “escape” (H) (Feature: true)

Stanford TE system, Adjunction feature If T-H are both in positive context –“A dog barked” -> “A dog barked loudly” (not safe adding) –“A dog barked carefully” -> “A dog barked” (safe dropping) If T-H are both in negative context –“The dog did not bark” -> “The dog did not bark loudly” (safe adding) –“The dog did not bark loudly” -> “The dog did not bark” (not safe dropping) Features: “not safe adjunct drop detected”, “not safe adjunct addition detected”, …

Stanford TE system Some other features … Antonym feature –Antonyms found in aligned region (with WordNet). Date/Numbers feature –Binary features that indicates “dates described in T – H aligned region are not matched”. Quantifier feature –Quantifies modifying two aligned parts are “not matched”.

BIUTEE gap mode Main approach of Gap mode –Transform Text as close to Hypothesis with reliable rules → T’ –Evaluate T’ – H pair, by extracting features to evaluate the pair. Two set of features –Lexical Gap features –Predicate Argument Gap features

BIUTEE gap mode, lexical gap feature Two numerical feature values –Score for non-predicate words –Score for predicate words –Not all words are equal: missing rare terms are more heavily penalized (weight: log prob) Sum of all missing terms’ weight, forms one feature value.

BIUTEE gap mode, predicate-argument gap feature Requires predicate-argument structure –How well the structures are covered from that of Text? Degree of matching from Full match to Partial match –Full match: same head words, same governing predicate, set of content words. –Category I, II, III partial matches are defined. 5 numeric features represents degree of match –No. of matched NE arguments, no. of matched non- NE arguments, no. of argument in Cat I, II, III.

Priorities? Features that we might hope to try soon … –Main verb of H matched? Its arguments matched? –Weighted coverage (such as IDF), on word coverage –Date matcher (an aligner) –Features that use TruthTeller alignments (number of matching/non-matching predicate truth) –Polarity/Modality/Factivity features (cheaper than TruthTeller … )