Excitement introduction
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This document provides an overview of the EXCITEMENT project, which aims to develop an open platform for multi-lingual textual inference. It involves academic and industrial partners working on two main goals: (1) developing algorithms and resources for multi-lingual textual inference, and (2) applying inference techniques to analyze customer interactions in multiple languages and channels. The work is organized into several work packages focusing on requirements, data collection, architecture, platform development, evaluation, and dissemination over a three year period.
Excitement introduction
- 1. EXCITEMENT: EXploring Customer Interactions through Textual EntailMENT Short project introduction FP7-ICT-2011-7 (Objective 4.2b, STREP)
- 2. 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
- 3. 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
- 4. 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
- 5. 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
- 6. 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
- 7. 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
- 8. Generic Entailment Platform EXCITEMENT: EXploring Customer Interactions through Textual EntailMENT
- 9. Textual Inference: Subgoals (2/3) Challenges to address: 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
- 10. Textual Inference: Subgoals (3/3) Challenges to address: 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
- 11. 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 EXCITEMENT: EXploring Customer Interactions through Textual EntailMENT
- 12. 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 EXCITEMENT: EXploring Customer Interactions through Textual EntailMENT
- 13. Use case 1: Creation of entailment graphs
- 14. Use case 2: Entailment-driven query expansion
- 15. Interaction Analytics: Subgoals (2/3) 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 EXCITEMENT: EXploring Customer Interactions through Textual EntailMENT
- 16. Interaction Analytics: Subgoals (3/3) 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 EXCITEMENT: EXploring Customer Interactions through Textual EntailMENT
- 17. 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:
- 18. 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 EXCITEMENT: EXploring Customer Interactions through Textual EntailMENT
- 19. Pert diagram
- 20. 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
- 21. 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. EXCITEMENT: EXploring Customer Interactions through Textual EntailMENT
- 22. 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: EXploring Customer Interactions through Textual EntailMENT