Speech-to-Speech MT in NESPOLE! Design and Engineering Alon Lavie, Lori Levin Work with: Chad Langley, Tanja Schultz, Dorcas Wallace, Donna Gates, Kay Peterson, Kornel Laskowski MT Class, April 2, 2003

Speech-to-speech translation for E-Commerce applications Partners: CMU, Univ of Karlsruhe, ITC-irst, UJF-CLIPS, AETHRA, APT-Trentino Builds on successful collaboration within C-STAR Improved limited-domain speech translation Experiment with multimodality and with MEMT Showcase-1: Travel and Tourism in Trentino, completed in Nov-2001, demonstrated IST,HLT Showcase-2: expanded travel + medical service

April 2, 2003MT Class3 NESPOLE! System Overview Human-to-human spoken language translation for e-commerce application (e.g. travel & tourism) (Lavie et al., 2002) English, German, Italian, and French Translation via interlingua Translation servers for each language exchange interlingua to perform translation –Speech recognition (Speech  Text) –Analysis (Text  Interlingua) –Generation (Interlingua  Text) –Synthesis (Text  Speech)

April 2, 2003MT Class4 Speech-to-speech in E- commerce Augment current passive web E-commerce with live interaction capabilities Client starts via web, can easily connect to agent for specific detailed information “Thin client” - very little special hardware and software on client PC: browser, MS Netmeeting, Shared Whiteboard

April 2, 2003MT Class5 NESPOLE! User Interfaces

April 2, 2003MT Class6 NESPOLE! Translation Monitor

April 2, 2003MT Class7 NESPOLE! Architecture

April 2, 2003MT Class8 Distributed S2S Translation over the Internet

April 2, 2003MT Class9 Language-specific HLT Servers

April 2, 2003MT Class10 Our Parsing and Analysis Approach Goal: A portable and robust analyzer for task-oriented human-to-human speech, parsing utterances into interlingua representations Our earlier systems used full semantic grammars to parse complete DAs –Useful for parsing spoken language in restricted domains –Difficult to port to new domains Current focus is on improving portability to new domains (and new languages) Approach: Continue to use semantic grammars to parse domain-independent phrase-level arguments and train classifiers to identify DAs

April 2, 2003MT Class11 Interchange Format Interchange Format (IF) is a shallow semantic interlingua for task-oriented domains Utterances represented as sequences of semantic dialog units (SDUs) IF representation consists of four parts –Speaker –Speech Act –Concepts –Arguments speaker : speech act +concept* +arguments* } Domain Action

April 2, 2003MT Class12 Hybrid Analysis Approach Text  Argument Parser Text Arguments  SDU Segmenter Text Arguments SDUs  DA Classifier IF  Use a combination of grammar-based phrase- level parsing and machine learning to produce interlingua (IF) representations

April 2, 2003MT Class13 Hybrid Analysis Approach Hello. I would like to take a vacation in Val di Fiemme. c:greeting (greeting=hello) c:give-information+disposition+trip (disposition=(who=i, desire), visit-spec=(identifiability=no, vacation), location=(place-name=val_di_fiemme)) hello i would like to take a vacation in val di fiemme SDU1SDU2 greeting=disposition=visit-spec=location= helloi would like totake a vacationin val di fiemme greetinggive-information+disposition+trip greeting=disposition=visit-spec=location= helloi would like totake a vacationin val di fiemme

April 2, 2003MT Class14 Argument Parsing Parse utterances using phrase-level grammars SOUP Parser (Gavaldà, 2000): Stochastic, chart-based, top-down robust parser designed for real-time analysis of spoken language Separate grammars based on the type of phrases that the grammar is intended to cover

April 2, 2003MT Class15 Domain Action Classification Identify the DA for each SDU using trainable classifiers Two TiMBL (k-NN) classifiers –Speech act –Concept sequence Binary features indicate presence or absence of arguments and pseudo- arguments

April 2, 2003MT Class16 Using the IF Specification Use knowledge of the IF specification during DA classification –Ensure that only legal DAs are produced –Guarantee that the DA and arguments combine to form a valid IF representation Strategy: Find the best DA that licenses the most arguments –Trust parser to reliably label arguments –Retaining detailed argument information is important for translation

April 2, 2003MT Class17 Evaluation: Classification Accuracy 20-fold cross-validation using the NESPOLE! travel domain database EnglishGerman SDUs Domain Actions Speech Acts 70 Concept Sequences Vocabulary The database: Most Frequent Class: EnglishGerman Speech Act 41.4%40.7% Concept Sequence 38.9%40.3%

April 2, 2003MT Class18 Evaluation: Classification Accuracy EnglishGerman Speech Acts 81.25%78.93% Concept Sequences 69.59%67.08% Classification Performance Accuracy

April 2, 2003MT Class19 Evaluation: End-to-End Translation English-to-English and English-to-Italian Training set: ~8000 SDUs from NESPOLE! Test set: 2 dialogs, only client utterances Uses IF specification fallback strategy Three graders, bilingual English/Italian speakers Each SDU graded as perfect, ok, bad, very bad Acceptable translation = perfect+ok Majority scores

April 2, 2003MT Class20 Evaluation: End-to-End Translation Speech recognizer hypotheses66.7%WAR: 56.4% English Source Input Target Language Acceptable (OK + Perfect) Translation fromEnglish 68.1% Human TranscriptionItalian 69.7% Translation fromEnglish 50.4% SR HypothesisItalian 50.2%

April 2, 2003MT Class21 Evaluation: Data Ablation Experiment

April 2, 2003MT Class22 Domain Portability Experimented with porting to a medical assistance domain in NESPOLE! Initial medical domain system up and running, with reasonable coverage of flu-like symptoms and chest pain Porting the interlingua, grammars and modules for English, German and Italian required about 6 person months in total –Interlingua development: ~180 hours –Interlingua annotation: ~200 hours –Analysis grammars, training: ~250 hours –Generation development: ~250 hours

April 2, 2003MT Class23 New Development Tools

April 2, 2003MT Class24 Questions?

April 2, 2003MT Class25 Grammars Argument grammar –Identifies arguments defined in the IF s[arg:activity-spec=]  (*[object-ref=any] *[modifier=good] [biking]) –Covers "any good biking", "any biking", "good biking", "biking", plus synonyms for all 3 words Pseudo-argument grammar –Groups common phrases with similar meanings into classes s[=arrival=]  (*is *usually arriving) –Covers "arriving", "is arriving", "usually arriving", "is usually arriving", plus synonyms

April 2, 2003MT Class26 Grammars Cross-domain grammar –Identifies simple domain-independent DAs s[greeting] ([greeting=first_meeting] *[greet:to-whom=]) –Covers "nice to meet you", "nice to meet you donna", "nice to meet you sir", plus synonyms Shared grammar –Contains low-level rules accessible by all other grammars

April 2, 2003MT Class27 Segmentation Identify SDU boundaries between argument parse trees Insert a boundary if either parse tree is from cross-domain grammar Otherwise, use a simple statistical model

April 2, 2003MT Class28 Using the IF Specification Check if the best speech act and concept sequence form a legal IF If not, test alternative combinations of speech acts and concept sequences from ranked set of possibilities Select the best combination that licenses the most arguments Drop any arguments not licensed by the best DA

April 2, 2003MT Class29 Grammar Development and Classifier Training Four steps 1.Write argument grammars 2.Parse training data 3.Obtain segmentation counts 4.Train DA classifiers Steps 2-4 are automated to simplify testing new grammars Translation servers include a development mode for testing new grammars

April 2, 2003MT Class30 Evaluation: IF Specification Fallback 182 SDUs required classification 4% had illegal DAs 29% had illegal IFs Mean arguments per SDU: 1.47 Changed Speech Act5% Concept Sequence26% Domain Action29% Arguments dropped per SDU Without fallback0.38 With fallback0.07

April 2, 2003MT Class31 Evaluation: Data Ablation Experiment 16-fold cross validation setup Test set size (# SDUs): 400 Training set sizes (# SDUs): 500, 1000, 2000, 3000, 4000, 5000, 6009 (all data) Data from previous C-STAR system No use of IF specification

April 2, 2003MT Class32 Future Work Alternative segmentation models, feature sets, and classification methods Multiple argument parses Evaluate portability and robustness –Collect dialogues in a new domain –Create argument and full DA grammars for a small development set of dialogues –Assess portability by comparing grammar development times and examining grammar reusability –Assess robustness by comparing performance on unseen data

April 2, 2003MT Class33 References Cattoni, R., M. Federico, and A. Lavie Robust Analysis of Spoken Input Combining Statistical and Knowledge-Based Information Sources. In Proceedings of the IEEE Automatic Speech Recognition and Understanding Workshop, Trento, Italy. Daelemans, W., J. Zavrel, K. van der Sloot, and A. van den Bosch TiMBL: Tilburg Memory Based Learner, version 3.0, Reference Guide. ILK Technical Report Gavaldà, M SOUP: A Parser for Real-World Spontaneous Speech. In Proceedings of the IWPT-2000, Trento, Italy. Gotoh, Y. and S. Renals. Sentence Boundary Detection in Broadcast Speech Transcripts In Proceedings on the International Speech Communication Association Workshop: Automatic Speech Recognition: Challenges for the New Millennium, Paris. Lavie, A., F. Metze, F. Pianesi, et al Enhancing the Usability and Performance of NESPOLE! – a Real-World Speech-to-Speech Translation System. In Proceedings of HLT-2002, San Diego, CA.

April 2, 2003MT Class34 References Lavie, A., C. Langley, A. Waibel, et al Architecture and Design Considerations in NESPOLE!: a Speech Translation System for E-commerce Applications. In Proceedings of HLT-2001, San Diego, CA. Lavie, A., D. Gates, N. Coccaro, and L. Levin Input Segmentation of Spontaneous Speech in JANUS: a Speech-to-speech Translation System. In Dialogue Processing in Spoken Language Systems: Revised Papers from ECAI-96 Workshop, E. Maier, M. Mast, and S. Luperfoy (eds.), LNCS series, Springer Verlag. Lavie, A GLR*: A Robust Grammar-Focused Parser for Spontaneously Spoken Language. PhD dissertation, Technical Report CMU-CS , Carnegie Mellon University, Pittsburgh, PA. Munk, M Shallow Statistical Parsing for Machine Translation. Diploma Thesis, Karlsruhe University. Stevenson, M. and R. Gaizauskas. Experiments on Sentence Boundary Detection In Proceedings of ANLP and NAACL-2000, Seattle. Woszczyna, M., M. Broadhead, D. Gates, et al A Modular Approach to Spoken Language Translation for Large Domains. In Proceedings of AMTA- 98, Langhorne, PA.