1. Speech-to-Speech MT JANUS 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 Translation System

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= t 1,t 2,…,t n 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. 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. Carnegie Mellon University School of Computer Science: A.Waibel, L. Levin, A. Lavie, R. Frederking 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 9/0012/0 0 9/019/02 Baseline MT systems ready Port to third language Baseline summarizer ready Port to second language

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