Excitement introduction

EXCITEMENT: EXploring Customer
Interactions through Textual EntailMENT
Short project introduction
FP7-ICT-2011-7 (Objective 4.2b, STREP)

Partners
Academic Partners:
 Bar-Ilan University, Ramat Gan, Israel (I. Dagan)
 DFKI, Saarbrücken, Germany (G. Neumann)
 Fondazione Bruno Kessler, Povo, Italy (B. Magnini)
 University of Heidelberg, Germany (S. Pado)

Industrial Partners:
 NICE, Ra'anana, Israel (English analytics
provider, coordinator)
 German company (OMQ, German IT support company)
 AlmaViva, Roma, Italy (Italian analytics provider)

EXCITEMENT: EXploring Customer Interactions through Textual EntailMENT

Genesis of the Proposal
 Collaboration between BIU and NICE developed in
2009/10
 Topic: Semantic inference
 Smaller scale, national Israeli funding

 Idea for a larger-scale project, early 2010
 Consortium formed at ACL 2010 conference (July)
 Weekly status calls; meeting in Brussels Nov 2010

Previous collaborations
ALM
A
joint project NICE
FB joint Israeli
K joint work on project
joint work on EC
QALL-ME project entailment

BIU
DFKI
joint work on entailment;
joint project book chapter

OMQ HEI

Two goals
 Scientific goal (Goal A) : Develop and advance a
“MOSES-style” open platform for multi-lingual
textual inference
 Industrial goal (Goal B) : Inference-based
processing of customer interactions across
languages and interaction channels
Open Platform for
Multi-lingual TI

Textual Customer
Inference Inference-based Interactions
Interaction
Analysis

Goal A: Multi-lingual Textual Inference
 Identification of semantic inference relations between
texts
 Question: Who acquired Overture?
 Text: Yahoo’s buyout of Overture…
 Anwer: Who = Yahoo

 Framework: Textual inference
 No analysis to specific linguistic formalism
 Instead: Establish mapping between texts at textual level
 Good mapping: valid inference; bad mapping: invalid inference

 Free choice of algorithms, mapping levels, …
 Robustness, efficiency

Textual Inference: Subgoals (1/3)
 Challenges to address:
 Majority of work for English – reusability for other
languages problematic
 No sharing of algorithms or resources across individual
systems
 Wheel must be “reinvented” for each new study

 Goal A1: A Generic Multilingual Architecture
 Develop modular APIs for inference platforms
 Language-independent specifications of
modules, algorithms, resources
 Language-independent preprocessing

Generic Entailment Platform


 Large-scale knowledge about valid inference patterns only
available for English
 Knowledge is never complete
 Methods for approximate matching necessary

 Goal A2: Algorithmic Progress in Textual
Inference
 Multi-lingual and cross-lingual knowledge induction
methods
 Frameworks for probabilistic matching

 NLP application builders interested in inference must
integrate specialized external systems or develop own
system
 High threshold
 No practical re-use of existing
modules, algorithms, resources

 Goal A3: An open-source multi-lingual textual
inference platform
 Open-source reference implementation of a textual
inference platform for practical exploitation of Goal A1
 Similar to MOSES in MT community
 Fostering of an open-source community around platform

Goal B: Customer Interaction Analytics
 Customer interaction analytics is large and growing
 Multiple channels: Call centers, email contacts, web
forums
 Substantial added value for companies:
 (dis)advantages of products, typical customer
problems, typical problems in customer handling, …
 Analysts must be supported by automatic systems
that represent interactions in a compact, expressive
form
 State-of-the-art: Keyword-based analysis

 Our proposal: Replace by textual inference

Interaction Analytics: Subgoals (1/3)
 Goal B1: Interaction exploration and processing
schemes at the statement level
 Development of concrete algorithms for phrasing
interaction analytics queries in terms of entailment
questions
 Focus: Statement level (as opposed to keyword level)

 Use case 1: Creation of entailment graphs
 Entailment for visualization of customer information
 Use case 2: Searching customer information
 Entailment for query expansion


Use case 1: Creation of entailment graphs

Use case 2: Entailment-driven query expansion


 A priori, inference technology groups statements
only according to their inference relations
 May not correspond exactly to analysts’ needs

 Goal B2: Integrating textual inference and
domain knowledge
 Examples: Predefined categories, process ontologies
 Develop methods to integrate such extralinguistic
knowledge into the textual inference computation process



 Goal B3: Implemented industrial application
 Harvesting benefits of goals B1 and B2 in industrial
context
 Identification of sufficiently robust and efficient entailment
modules for practical deployment
 Integration of entailment platforms into existing industrial
systems
 On-field evaluation for different language-channel
combinations


Language-Channel Matrix
Evaluation on Domain Benchmarks (Open Platform)

English German Italian
Email NICE OMQ
Speech NICE ALMA
Social HEI ALMA
Media
On-Field Evaluation (Industrial Systems)

English German Italian
Email OMQ
Speech NICE
Social ALMA
Media EXploring Customer Interactions through Textual EntailMENT
EXCITEMENT:

Work program sketch
 WP 1. User requirements (NICE) M 1-27
 WP 2. Multilingual data collection (ALMA) M 1-27
 WP 3. Specification of architecture (HEI) M 1-27

 WP 4. TE Open Platform (FBK) M 1-36
 WP 5. Multilingual knowledge acquisition (BIU) M 1-36
 WP 6. Textual Inference components development (DFKI) M 6-36

 WP 7. Text exploration: integration and evaluation (OMQ) M 12-36
 WP 8. Open platform evaluation and distribution (BIU) M 10-36
 WP 9. Dissemination and exploitation (FBK) M 1-36
 WP 10. Scientific Coordination (FBK) M 1-36
 WP 11. Management (NICE) M 1-36


Development cycles
 Cycle 1: Month 1-12
 First cycle through all WPs
 End result: Two workable products
 First release of open source platform
 Cycle 2: Month 12/24
 Second cycle through all WPs
 End result: Two robust, feature-complete products
 Second release of open source platform
 Second release of industrial applications
 Cycle 3: Month 24/36
 Third cycle through all WPs
 End result: Two robust, feature-complete products
 Second release of open source platform
 Second release of industrial applications

Milestones
 MS1 (month 12): First Textual Entailment Platform
(internal use) – Cycle I. The milestone is planned in
correspondence with the first year review meeting.

 MS2 (month 18): Open platform - first release. This
milestone aims at showing that the functionalities of
the first version of the Textual Entailment Open
Platform, developed in WP4 and WP5, are
successfully achieved, and that the open source
distribution has properly started.


Milestones
 MS3 (month 24): Open platform and text exploration applications –
Cycle II. This milestone will show the progresses in the distribution of
the open platform (WP8) as well as the text exploration applications
at the end of the second development/evaluation cycle.
Correspondence with both the first EXCITEMENT workshop and the
expected second year review meeting.

 MS4 (month 36): Open platform and text exploration applications –
Cycle III. This milestone aims at showing the final results of the
project at the end of the third development/evaluation cycle, both in
terms of the open platform (WP8) and of the impact of the text
exploration components on the companies applications (WP7). final
EXCITEMENT workshop and the expected third year review
meeting.


Excitement introduction

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