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draft [2009/07/15 15:44] ptacek |
draft [2009/09/01 00:10] ufal |
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+ | [[http:// | ||
- | ====== Description of Czech Companion November | + | ====== Description of Czech Companion November |
- | The Czech version of Companion deals with the Reminiscing about User's Photos scenario taking advantage of data recorded in first phase of the project. The basic architecture is same as of the English version, i.e. set of modules communicating through the Inamode Relayer (TID) backbone; | + | The Czech version of the Companion deals with the Reminiscing about User's Photos scenario, taking advantage of data recorded in first phase of the project. The basic architecture is same as of the English version, i.e. set of modules communicating through the Inamode Relayer (TID) backbone; however the set of modules |
- | however the set of modules | + | |
- | + | ||
- | photopal domena, nahranej korpus, ze na to sou dafy (reusing SHEFF DM intergrated through Inamode Relayer (TID)) vhodny, moreover reusable for expected pomdp DM from UOX (reuse states, let pomdp' | + | |
- | typy odpovedi a zpusob jejich implementace, | + | |
- | NLP server s tectomt, ASR/TTS/SR client, connected over network | + | |
- | XXX JPta | + | |
- | + | ||
- | advances in Czech NLU (on reconstructed spoken data): 300-500vet(? | + | |
- | pos ? analyzovat, generovat a kontrolovat ' | + | |
+ | The dialog is driven by a dialog manager component by USFD (originally developed for the English Senior Companion prototype), we supply the transition network (DAFs). The selection is backed by (a) appropriateness for the type of dialog we aim for (the corpus reveals frequent reoccurring topics to be handled by DAFs) , (b) availability of mature package within time frame that allows for integration, | ||
+ | Our DAFs covering selected topics contain not only Companion replies mined from the corpora, but also new human-authored assessments, | ||
+ | For a sample dialogue, see the Scenario Brief below. | ||
+ | {{user: | ||
===== Automatic Speech Recognition (WP 5.1)===== | ===== Automatic Speech Recognition (WP 5.1)===== | ||
features: improved language models, real-time speaker adaptation | features: improved language models, real-time speaker adaptation | ||
- | performance indicator: WER | + | performance indicator: WER |
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+ | ===== Speech Reconstruction (WP 5.2) ===== | ||
+ | features: omit filler phrases, remove irrelevant speech events, handle false starts, repetitions, | ||
- | ===== Speech Reconstruction (WP 5.1 ???) ===== | ||
- | features: omit filler phrases, remove irrelevant speech events, handle false starts, repetitions, | ||
- | performance indicator: BLEU score between actual output and manually reconstructed sentences from corpora (T5.2.1), baseline: Moses with default settings | ||
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===== Morphology Analyzer and POS tagging (WP 5.2) ===== | ===== Morphology Analyzer and POS tagging (WP 5.2) ===== | ||
- | features: coverage of photo-pal domain, domain adapted tagger | + | features: coverage of photo-pal domain, domain adapted tagger |
- | performance indicator: OOV rate, accuracy | + | performance indicator: OOV rate, accuracy |
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features: induce dependencies and labels | features: induce dependencies and labels | ||
performance indicator: accuracy (correctly induced edges, labels) | performance indicator: accuracy (correctly induced edges, labels) | ||
- | v tipu je natrenovat MacDonnalda na dialog datech, ten task je do M42, ted ne. | ||
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===== Semantic Parsing (WP 5.2) ===== | ===== Semantic Parsing (WP 5.2) ===== | ||
- | features: assignment of semantic roles (69 roles), coordinations, | + | features: assignment of semantic roles (69 roles), coordinations, |
- | performance indicator: accuracy (correctly induced edges, labels) | + | performance indicator: accuracy (correctly induced edges, labels) |
===== Information Extraction (WP 5.2) ===== | ===== Information Extraction (WP 5.2) ===== | ||
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===== Named Entities Recognition (WP 5.2) ===== | ===== Named Entities Recognition (WP 5.2) ===== | ||
- | features: detect person names, geographical locations, | + | features: detect person names, geographical locations, |
performance indicator: f-measure | performance indicator: f-measure | ||
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===== Dialog Act Tagging (WP 5.2) ===== | ===== Dialog Act Tagging (WP 5.2) ===== | ||
- | features: domain tailored | + | features: domain tailored |
performance indicator: accuracy | performance indicator: accuracy | ||
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===== Dialog Manager (WP 5.3) ===== | ===== Dialog Manager (WP 5.3) ===== | ||
- | features: | + | features: |
- | Handmade DAF covering following topics: | + | manual creation of DAFs covering following topics: |
- | performance indicator: | + | performance indicator: |
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===== Natural Language Generation (WP 5.4) ===== | ===== Natural Language Generation (WP 5.4) ===== | ||
- | features: | + | features: |
performance indicator: BLEU score | performance indicator: BLEU score | ||
- | ====== AZ PO LISTOPADU ====== | ||
- | ===== Complete System Evaluation | + | ===== Emotional TTS (WP 5.5) ===== |
- | T5.2.7 tohle zminuje, nick webb to pro nas asi neudela | + | features: emotions will be expressed implicitly, through the usage of communicative functions; new female voice database was recorded for this purposes |
- | performance indicator: | + | performance indicator: |
- | ===== Sentiment Analysis | + | |
- | features: | + | |
- | performance indicator: f-measure | + | |
+ | ===== Emotional Avatar Integration | ||
+ | features: | ||
+ | performance indicator: subjective evaluation of the naturalness and the ability to convey emotions (small-scale, | ||
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+ | ====== Scenario Brief ====== | ||
+ | |||
+ | C1 Dobrý den, jak se jmenujete? (intro-daf-based) | ||
+ | H1 Marie. | ||
+ | |||
+ | C2 Těší mě. Já jsem Pavla a chtěla | ||
+ | |||
+ | C2 Co je tohle? (photo_maintenance) | ||
+ | H2 To je moje rodina, rodiče, sestra, její manžel, moje dvě děti a její dvě děti. | ||
+ | H2 Byli u mě na Vánoce na návštěvě. | ||
+ | |||
+ | C3 Trávíte Vánoce takhle pohromadě? | ||
+ | H3 Teď už ne. | ||
+ | H3 Teď už všechny moje dětičky vylétaly z hnízda. | ||
+ | |||
+ | C4 Jak se jmenuje váš manžel? | ||
+ | H4 Manžel už nežije, jmenoval se Karel Šístek. | ||
+ | |||
+ | C5 Máte ráda Vánoce? | ||
+ | H5 Teď už ne. | ||
+ | H5 Měla jsem je ráda, když byly děti malé. | ||
+ | H5 Teď už to ale není ono. | ||
+ | |||
+ | C6 Takže teď trávíte Vánoce sama? (ask_on_detail-daf-based) | ||
+ | H6 Nejsem úplně sama, chodím každé Vánoce k dceři, ale už to není, jak to bývalo, když byly doma malé děti. | ||
+ | |||
+ | C7 Jak děti vyrostou, tak má člověk najednou příliš mnoho času. | ||
+ | C7 Kdo je kdo na té fotce? | ||
+ | H7 U stromečku je můj synovec, ta holčička vedle je moje dcera, chlapeček s hračkou je můj syn, vedle je sestra, její manžel a nad ním je můj otec. | ||
+ | |||
+ | C8 To jste ale velká rodina. | ||
+ | C8 Jak se jmenuje vaše sestra? (companion commenting last utterance | ||
+ | H8 Sestra se jmenuje Marie Havlová, její manžel je Vladimír, ten bohužel nedávno zemřel. | ||
+ | |||
+ | C9 To je smutné. | ||
+ | C9 Znala jste ho dobře? | ||
+ | H9 Velmi dobře, trávili jsme obě rodiny společně hodně času. | ||
+ | |||
+ | C10 Máte už nějaká vnoučata? | ||
+ | H10 Mám vnučku Natálku. | ||
+ | H10 Ona je takové živé děťátko, taková neposeda. | ||
+ | |||
+ | C11 Takže Natálka je neposeda. repeat-rephrase | ||
+ | C11 Máte ještě nějaké další vnučky nebo vnuky? | ||
+ | H11 Čekáme ještě další přírustek. | ||
+ | |||
+ | C12 Hlídáte je někdy? | ||
+ | H12 Hlídám dost často. | ||
+ | H12 Já mám děti ráda a jsem s nimi moc ráda. | ||
+ | |||
+ | C13 To je fajn. | ||
+ | C13 S dětmi je legrace. | ||
+ | H13 Ano. | ||
+ | |||
+ | C Podíváme se na další fotku? | ||