1. Linguistic and Logical Tools for an Advanced Interactive Speech System in Spanish J. Álvarez, V. Arranz, N. Castell & M. Civit TALP Research Centre UPC, Barcelona

2. Contents Introduction Corpora Construction System Architecture Understanding Module: –Input Problems & Solutions Adopted –Language Processing: Morphology Syntax Semantic Extraction Dialogue Manager Conclusions

3. Introduction Increasing need for more natural HMI Development of a dialogue system: –spontaneous speech –restricted-domain: railway information –rather user-friendly communication exchange –language of application: Spanish Other related systems: ATIS, TRAINS, LIMSI ARISE, TRINDI,...

4. Corpora Construction Project objective: none available in Spanish Two different corpora developed: –human-human –human-machine (Wizard of Oz technique): 150 different situations + an open scenario total of 227 dialogues

5. System Architecture

6. Understanding Module

7. Input Problems (1) Recognition Errors: –Excess of information: U: “sábado treinta de octubre” (“Saturday, October 30”) R: “un tren que o sábado treinta de octubre” (“a train that or...”) –Erroneous recognition: U: “gracias” (“thank you”) R: “sí pero ellos”(“yes but they”)

8. Input Problems (2) –Grammar errors: Lack of prep+det contractions: “de el”  “del” Wrong use of indefinite determiner: “un de octubre”  “uno de octubre” (“1 October”) Wrong orthographical transcriptions: “qué/que, a/ha”,...  (“what/that, to/has,...”)

9. Input Problems (3) Problems caused by spontaneous speech: –Syntactic disfluencies: U: “a ver los horarios de los trenes que van de Teruel a Barcelona el este próximo viernes y que vayan de Barcelona a Teruel el próximo que vuelvan de Barcelona a Teruel el próximo domingo” –Lexical disfluencies, pauses, noises,...

10. Solutions Adapting the recogniser to the domain Adapting the recogniser to spontaneous speech Adapting the understanding module Closing the entry channel

11. Tools MACO+: Morphological Analyzer Corpus Oriented [Carmona et al., 98] RELAX: Relaxation Labelling Based Tagger [Padró, 97] TACAT: Tagged Corpus Analyzer Tool [Castellón et al., 98] PRE+: Production Rule Environment [Turmo, 99]

12. Example User turn: “Me gustaría información sobre trenes de Guadalajara a Cáceres para la primera semana de agosto” (“I would like some information about trains from Guadalajara to Caceres for the first week of August”)

13. Language Processing Transcription Text Morphological Analysis Syntactic Analysis Analysed Text Semantic Extraction Frames Understanding

14. Morphology (1) MACO+: –contains knowledge organised into classes and inflection paradigms –uses a task/domain lexicon: less ambiguity and better execution time –provides all possible labels per word RELAX: –disambiguates obtained labels –is constraint based with relaxation labelling

15. Morphology (2) me yo PP1CSO00 gustaría gustar VMCP1S0 información información NCFS000 sobre sobre SPS00 trenes tren NCMP000 de de SPS00 Guadalajara guadalajara NP000C0 a a SPS00 Cáceres cáceres NP000C0 para para SPS00 la la TDFS0 primera primero MOFS00 semana semana NCFS000 de de SPS00 agosto agosto NCMS000.. Fp

16. Syntax (1) TACAT: –shallow parser –context-free grammar adapted for the domain: rules re-written for dates, timetables and proper names –bottom-up strategy –this adaptation helps semantic searches

17. Syntax (2) [ { pos=>S } [ { pos=>patons } [ { pos=>pp1cso00, forma=>"Me", lema=>"yo" } ] ] [ { pos=>grup-verb } [ { pos=>vmcp3s0, forma=>"gustaría", lema=>"gustar" } ] ] [ { pos=>sn } [ { pos=>ncfs000, forma=>"información", lema=>"información" } ] ] [ { pos=>grup-sp } [ { pos=>sps00, forma=>"sobre", lema=>"sobre" } ] [ { pos=>sn } [ { pos=>ncmp000, forma=>"trenes", lema=>"tren" } ] ] ] [ { pos=>grup-sp } [ { pos=>sps00, forma=>"de", lema=>"de" } ] [ { pos=>sn } [ { pos=>np000c0, forma=> "Guadalajara", lema=> " Guadalajara" } ] ] ] [ { pos=>grup-sp } [ { pos=>sps00, forma=>"a", lema=>"a" } ] [ { pos=>sn } [ { pos=>np000c0, forma=>"Cáceres", lema=> " Cáceres" } ] ] ] [ { pos=>grup-sp }.........

18. Semantic Extraction (1) (HORA-SALIDA) CIUDAD-ORIGEN: Guadalajara CIUDAD-DESTINO: Cáceres INTERVALO-FECHA-SALIDA: 31-7-2000/6-8-2000 Aim: generation of semantic frames

19. Semantic Extraction (2) System implemented in PRE+ PRE+: –production rule environment –very flexible and robust: rule conditions contain syntactic patterns and lexical items to search for priority, score and control: allow to specify rule application, location of concept to extract,...

20. Semantic Extraction (3) (rule CiudadOrigen3 ruleset CiudadOrigen priority 10 score [0,_,1,0] control forever ending Postrule (InputSentence ^tree tree_matching( [{pos=>grup-sp} [{lema=> de|desde}] [{pos=> np000c0, forma=>?forma}] ])) -> (?_ := Print(CiudadOrigen,?forma)) (?_ := REM(CiudadOrigen,X,+a)))

21. Understanding Module

22. Dialogue Manager (1) Implemented using YAYA [Alvarez, 00] Reasoning engine combines: –frames from the understanding module, with –facts from the dialogue history, and with –axioms in order to generate: –reaction facts from the system Output based on frames: –for the natural language generator (content) –for the recogniser (Speech Act prediction)

23. Dialogue Manager (2) Sentence to generate: “De Guadalajara a Cáceres ¿qué día desea viajar?” (“From Guadalajara to Caceres, when do you wish to travel?”) Output Frame: (CONFIRMACIÓN) TIPO: implícita CIUDAD-ORIGEN: Guadalajara CIUDAD-DESTINO: Cáceres (SOLICITUD) CONCEPTO: FECHA-SALIDA

24. Conclusions Corpus development: valuable resource Adaptation of general NLP tools for: –domain –spontaneous speech dialogue Development of new tools: –semantic extraction (use of PRE+): flexible & robust –dialogue manager (use of YAYA): fast to develop & easy to modify Challenge: processing in real time

25. Linguistic and Logical Tools for an Advanced Interactive Speech System in Spanish J. Álvarez, V. Arranz, N. Castell & M. Civit TALP Research Centre UPC, Barcelona