SOFTCARDINALITY: Learning to Identify Directional Cross-Lingual Entailment from Cardinalities and SMT

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in this paper we describe our system submitted for evaluation in the CLTE-SemEval-2013 task, which achieved the best results in two of the four data sets, and finished third in average. This system consists of a SVM classifier with features extracted from texts (and their translations SMT) based on a cardinality function. Such function was the soft cardinality. Furthermore, this system was simplified by providing a single model for the 4 pairs of languages obtaining better (unofficial) results than separate models for each language pair. We also evaluated the use of additional circular-pivoting translations achieving results 6.14% above the best official results.

Technology Education

SOFTCARDINALITY:
Learning to Identify Directional Cross-Lingual
Entailment from Cardinalities and SMT
Sergio Jimenez and
Claudia Becerra
(a participating system in the Cross-lingual
Textual Entailment, CLTE, TASK-8)
Alexander Gelbukh
Instituto Politécnico
Nacional, Mexico

Soft Cardinality
A=
, ,
B=
, ,
|A|=3
|B|=3
Classical
(integer)
Soft
(real)
Cardinality: number of different elements in a
collection, i.e. set definition.
C=
,
= |C|=1 |C|’=1.0

Soft Cardinality
inter-elements
similarity
elements
weights
“softness”
control
word-to-word
similarity
idf term
weighting

Word-to-word similarity functions
• Character q-grams
• Edit-distance
• Jaro-Winkler
m is the number of order mismatches between the common characters

Language-pair Model
T1
(EN)
T2
(FR)
SMT
SMT
T1
t
(FR)
T2
t
(EN)
translate
TextPre-processing
• Tokenizing
• Stemming
• Stop-words
removal
• idf term
weighting FeatureExtraction
4-wayclassificationmodel
Gold
standard
SVM

Submitted Systems
• RUN1: 4 language-pair models (es-
en, fr-en, it-en, de-en) each one
trained with 1,000 text pairs. SVM
using C=1.0
• RUN2: same as RUN1 but optimizing
C for max. accuracy.

Circular Pivoting Translations
T1
(EN)
T2
(FR)
SMT
SMT
T1
t
(FR)
T2
t
(EN)
SMT
SMT
T1
tt
(EN)
T2
tt
(FR)
SMT
SMT
T1
ttt
(FR)
T2
ttt
(EN)
Original feature set: 2 comparable text pairs x14 features= 28 features
Extended feature set: 2+2+4 comparable text pairs x14 features= 112 features
Original feature set: 2+2 comparable text pairs x14 features= 56 features

Single Multilingual Model
1,000 feature vectors
1,000 feature vectors
1,000 feature vectors
1,000 feature vectors
4,000
features
vector
training
data set
4-wayclassificationmodel

Single Multilingual Model Results
4.6%
better than
best official
5.3%
better than
best official 1.3%
better than
best official
4.4%
below best
official
6.0%
better than
best official
baseline

Conclusions
• Soft Cardinality + SMT + SVM seems to be a
good combination for CLTE.
• A single multilingual model produced
improved results than language-pair models.
• Additional circular pivoting translations
produced slightly improved but consistent
improvements.
• Character q-grams seems to be better than
Edit-distance and Jaro-Winkler.

Soft Cardinality at *SEM and SemEval
• STS-2012, official 3th out of 89 systems
• STS-2013-CORE task, 18th out of 90 systems
(4th un-official)
• STS-2013-TYPED task, top-system UNITOR team
• CLTE-2012, 3rd out of 29 systems (1st un-official)
• CLTE-2013, among the 2-top systems
• SRA-2013, among the 2-top systems
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SOFTCARDINALITY: Learning to Identify Directional Cross-Lingual Entailment from Cardinalities and SMT

1. SOFTCARDINALITY: Learning to Identify Directional Cross-Lingual Entailment from Cardinalities and SMT Sergio Jimenez and Claudia Becerra (a participating system in the Cross-lingual Textual Entailment, CLTE, TASK-8) Alexander Gelbukh Instituto Politécnico Nacional, Mexico

2. Soft Cardinality A= , , B= , , |A|=3 |B|=3 Classical (integer) Soft (real) Cardinality: number of different elements in a collection, i.e. set definition. C= , = |C|=1 |C|’=1.0

3. Soft Cardinality inter-elements similarity elements weights “softness” control word-to-word similarity idf term weighting

4. Word-to-word similarity functions • Character q-grams • Edit-distance • Jaro-Winkler m is the number of order mismatches between the common characters

5. Features for Text Pairs T1 , T2 T1 T2

6. Language-pair Model T1 (EN) T2 (FR) SMT SMT T1 t (FR) T2 t (EN) translate TextPre-processing • Tokenizing • Stemming • Stop-words removal • idf term weighting FeatureExtraction 4-wayclassificationmodel Gold standard SVM

7. Submitted Systems • RUN1: 4 language-pair models (es- en, fr-en, it-en, de-en) each one trained with 1,000 text pairs. SVM using C=1.0 • RUN2: same as RUN1 but optimizing C for max. accuracy.

8. Official Results

9. Circular Pivoting Translations T1 (EN) T2 (FR) SMT SMT T1 t (FR) T2 t (EN) SMT SMT T1 tt (EN) T2 tt (FR) SMT SMT T1 ttt (FR) T2 ttt (EN) Original feature set: 2 comparable text pairs x14 features= 28 features Extended feature set: 2+2+4 comparable text pairs x14 features= 112 features Original feature set: 2+2 comparable text pairs x14 features= 56 features

10. Single Multilingual Model 1,000 feature vectors 1,000 feature vectors 1,000 feature vectors 1,000 feature vectors 4,000 features vector training data set 4-wayclassificationmodel

11. Single Multilingual Model Results 4.6% better than best official 5.3% better than best official 1.3% better than best official 4.4% below best official 6.0% better than best official baseline

12. Conclusions • Soft Cardinality + SMT + SVM seems to be a good combination for CLTE. • A single multilingual model produced improved results than language-pair models. • Additional circular pivoting translations produced slightly improved but consistent improvements. • Character q-grams seems to be better than Edit-distance and Jaro-Winkler.

13. Soft Cardinality at *SEM and SemEval • STS-2012, official 3th out of 89 systems • STS-2013-CORE task, 18th out of 90 systems (4th un-official) • STS-2013-TYPED task, top-system UNITOR team • CLTE-2012, 3rd out of 29 systems (1st un-official) • CLTE-2013, among the 2-top systems • SRA-2013, among the 2-top systems , , ’

SOFTCARDINALITY: Learning to Identify Directional Cross-Lingual Entailment from Cardinalities and SMT

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