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

BIU

NICE

DFKI

FB

K

HEI

ALM

A

OMQ

joint Israeli

project

joint work on entailment;

book chapter

joint work on EC

QALL-ME project

joint project

joint project

joint work on

entailment

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

Textual

Inference

Customer

Interactions

Open Platform for

Multi-lingual TI

Inference-based

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 inferenceEXCITEMENT: EXploring Customer Interactions through Textual EntailMENT

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

English German Italian

Email NICE OMQ

Speech NICE ALMA

Social

Media

HEI ALMA


English German Italian

Email OMQ

Speech NICE

Social

Media

ALMA

Evaluation on Domain Benchmarks (Open Platform)

On-Field Evaluation (Industrial Systems)

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


Pert diagram

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.

Technology

Excitement introduction