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Speech-to-Speech MT JANUS C-STAR/Nespole!

Speech-to-Speech MT JANUS C-STAR/Nespole!. Lori Levin, Alon Lavie, Bob Frederking LTI Immigration Course September 11, 2000. Outline. Problems in Speech-to-Speech MT The JANUS Approach The C-STAR/NESPOLE! Interlingua (IF) System Design and Engineering Evaluation and User Studies

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Speech-to-Speech MT JANUS C-STAR/Nespole!

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  1. Speech-to-Speech MTJANUS C-STAR/Nespole! Lori Levin, Alon Lavie, Bob Frederking LTI Immigration Course September 11, 2000

  2. Outline • Problems in Speech-to-Speech MT • The JANUS Approach • The C-STAR/NESPOLE! Interlingua (IF) • System Design and Engineering • Evaluation and User Studies • Open Problems, Current and Future Research

  3. JANUS Speech Translation • Translation via an interlingua representation • Main translation engine is rule-based • Semantic grammars • Modular grammar design • System engineered for multiple domains • Incorporate alternative translation engines

  4. The C-STAR Travel Planning Domain General Scenario: • Dialogue between one traveler and one or more travel agents • Focus on making travel arrangements for a personal leisure trip (not business) • Free spontaneous speech

  5. The C-STAR Travel Planning Domain Natural breakdown into several sub-domains: • Hotel Information and Reservation • Transportation Information and Reservation • Information about Sights and Events • General Travel Information • Cross Domain

  6. Semantic Grammars • Describe structure of semantic concepts instead of syntactic constituency of phrases • Well suited for task-oriented dialogue containing many fixed expressions • Appropriate for spoken language - often disfluent and syntactically ill-formed • Faster to develop reasonable coverage for limited domains

  7. Semantic Grammars Hotel Reservation Example: Input: we have two hotels available Parse Tree: [give-information+availability+hotel] (we have [hotel-type] ([quantity=] (two) [hotel] (hotels) available)

  8. The JANUS-III TranslationSystem

  9. The JANUS-III TranslationSystem

  10. The SOUP Parser • Specifically designed to parse spoken language using domain-specific semantic grammars • Robust - can skip over disfluencies in input • Stochastic - probabilistic CFG encoded as a collection of RTNs with arc probabilities • Top-Down - parses from top-level concepts of the grammar down to matching of terminals • Chart-based - dynamic matrix of parse DAGs indexed by start and end positions and head cat

  11. The SOUP Parser • Supports parsing with large multiple domain grammars • Produces a lattice of parse analyses headed by top-level concepts • Disambiguation heuristics rank the analyses in the parse lattice and select a single best path through the lattice • Graphical grammar editor

  12. SOUP Disambiguation Heuristics • Maximize coverage (of input) • Minimize number of parse trees (fragmentation) • Minimize number of parse tree nodes • Minimize the number of wild-card matches • Maximize the probability of parse trees • Find sequence of domain tags with maximal probability given the input words: P(T|W), where T= t1,t2,…,tn is a sequence of domain tags

  13. JANUS Generation Modules Two alternative generation modules: • Top-Down context-free based generator - fast, used for English and Japanese • GenKit - unification-based generator augmented with Morphe morphology module - used for German

  14. Modular Grammar Design • Grammar development separated into modules corresponding to sub-domains (Hotel, Transportation, Sights, General Travel, Cross Domain) • Shared core grammar for lower-level concepts that are common to the various sub-domains (e.g. times, prices) • Grammars can be developed independently (using shared core grammar) • Shared and Cross-Domain grammars significantly reduce effort in expanding to new domains • Separate grammar modules facilitate associating parses with domain tags - useful for multi-domain integration within the parser

  15. Translation with Multiple Domain Grammars • Parser is loaded with all domain grammars • Domain tag attached to grammar rules of each domain • Previously developed grammars for other domains can also be incorporated • Parser creates a parse lattice consisting of multiple analyses of the input into sequences of top-level domain concepts • Parser disambiguation heuristics rank the analyses in the parse lattice and select a single best sequence of concepts

  16. Translation with Multiple Domain Grammars

  17. A SOUP Parse Lattice

  18. Alternative Approaches: SALT SALT - Statistical Analyzer for Lang. Translation • Combines ML trainable and rule-based analysis methods for robustness and portability • Rule-based parsing restricted to well-defined set of argument-level phrases and fragments • Trainable classifiers (NN, Decision Trees, etc.) used to derive the DA (speech-act and concepts) from the sequence of argument concepts. • Phrase-level grammars are more robust and portable to new domains

  19. SALT Approach • Example: Input: we have two hotels available Arg-SOUP: [exist] [hotel-type] [available] SA-Predictor: give-information Concept-Predictor: availability+hotel • Predictors using SOUP argument concepts and input words • Preliminary results are encouraging

  20. Alternative Approaches: MEMT Glossary-based Translation • Translates directly into target language (no IF) • Based on Pangloss translation system developed at CMU • Uses a combination of EBMT, phrase glossaries and a bilingual dictionary • English/German system operational • Good fall-back for uncovered utterances

  21. User Studies • We conducted three sets of user tests • Travel agent played by experienced system user • Traveler is played by a novice and given five minutes of instruction • Traveler is given a general scenario - e.g., plan a trip to Heidelberg • Communication only via ST system, multi-modal interface and muted video connection • Data collected used for system evaluation, error analysis and then grammar development

  22. System Evaluation Methodology • End-to-end evaluations conducted at the SDU (sentence) level • Multiple bilingual graders compare the input with translated output and assign a grade of: Perfect, OK or Bad • OK = meaning of SDU comes across • Perfect = OK + fluent output • Bad = translation incomplete or incorrect

  23. August-99 Evaluation • Data from latest user study - traveler planning a trip to Japan • 132 utterances containing one or more SDUs, from six different users • SR word error rate 14.7% • 40.2% of utterances contain recognition error(s)

  24. Evaluation Results

  25. Evaluation - Progress Over Time

  26. Speech-to-speech translation for eCommerce • CMU, Karlsruhe, IRST, CLIPS, 2 commercial partners • Improved limited-domain speech translation • Experiment with multimodality and with MEMT • EU-side has strict scheduling and deliverables • First test domain: Italian travel agency • Second “showcase”: international Help desk • Tied in to CSTAR-III

  27. C-STAR-III • Partners: ATR, CMU, CLIPS, ETRI, IRST, UKA • Main Research Goals: • Expandability - towards unlimited domains • Accessibility - Speech Translation over wireless phone • Usability - real service for real users

  28. 9/00 12/00 9/01 9/02 LingWear for the Information Warrior New Ideas • The pre-development of appropriate interlingua representations for domains of interest facilitates generation into a new language within two weeks. • The development of new MT engines (e.g. learnable transfer rules) and improved multi-engine integration supports rapid deployment of MT for a new language with scarce resources. • Gisting and summarzation in the source language followed by MT is better than vice versa. Impact • Allow military and relief organizations to converse in limited domains of interest with the local population in an area of conflict and/or disaster • Allow military and other operatives in the field to assimilate forien language information they encounter on-the-move • Rapidly port and deploy the technology into new languages with scarce resources Schedule Port to second language Baseline summarizer ready Baseline MT systems ready Port to third language Carnegie Mellon University School of Computer Science: A.Waibel, L. Levin, A. Lavie, R. Frederking

  29. Current and Future Work • Expanding the travel domain: covering descriptive as well as task-oriented sentences • Development of the SALT statistical approach and expanding it to other domains • Full integration of multiple MT approaches: SOUP, SALT, Pangloss • Task-based evaluation • Disambiguation: improved sentence-level disambiguation; applying discourse contextual information for disambiguation

  30. Students Working on the Project • Chad Langley: improved SALT approach • Dorcas Wallace: DA disambiguation using decision trees, English grammars • Taro Watanabe: DA correction and disambiguation using Transformation-based Learning, Japanese grammars • Ariadna Font-Llitjos: Spanish Generation

  31. The JANUS/C-STAR/Nespole! Team • Project Leaders: Lori Levin, Alon Lavie, Alex Waibel, Bob Frederking • Grammar and Component Developers: Donna Gates, Dorcas Wallace, Kay Peterson, Chad Langley, Taro Watanabe, Celine Morel, Susie Burger, Vicky Maclaren, Dan Schneider

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