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draft [2009/07/14 16:09] ptacek |
draft [2009/07/15 15:56] ptacek |
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+ | ====== Progress Report ====== | ||
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+ | [[Progress Report]] - dal jsem to na zvlastni stranku, abysme si nelezli do zeli | ||
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====== Description of Czech Companion November Prototype ====== | ====== Description of Czech Companion November Prototype ====== | ||
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+ | 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; | ||
+ | however the set of modules differs (see Figure 1). Regarding the physical settings: the Czech version runs on two notebook computers connected by local network; one can be seen as a Speech Client, running modules dealing with ASR,TTS and ECA, second as an NLP Server. | ||
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' | 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, | + | typy odpovedi a zpusob jejich implementace, |
NLP server s tectomt, ASR/TTS/SR client, connected over network | NLP server s tectomt, ASR/TTS/SR client, connected over network | ||
XXX JPta | XXX JPta | ||
- | advances in Czech NLU (on reconstructed spoken data): 100vet(?) rucne anotovat pos, a-tree, t-tree, IE predicates, Named Entities, DA pro eval in-domain testy | ||
- | pos ? analyzovat, generovat a kontrolovat ' | ||
- | ===== Speech Reconstruction ===== | ||
- | features: omit filler phrases, irrelevant speech events, false starts, repetitions, | ||
- | imlementation(zahrnout tuhle info?): moses natrenovany na korpusu | ||
- | performance indicator: BLEU score (overall scoring of all features) to annotated corpora from T5.2.1., nejaka baseline | ||
- | XXX Mirek | ||
- | ===== Morphology Analyzer and POS tagging ===== | + | ===== Automatic Speech Recognition (WP 5.1)===== |
- | features: XXX Mirek/ | + | features: improved language models, real-time speaker adaptation |
- | performance indicator: accuracy | + | performance indicator: WER |
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+ | ===== 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 | ||
+ | features: | ||
+ | performance indicator: | ||
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- | ===== Syntactic Parsing ===== | + | ===== Syntactic Parsing |
features: induce dependencies and labels | features: induce dependencies and labels | ||
- | performance indicator: | + | performance indicator: |
- | v tipu je natrenovat MacDonnalda na dialog datech, ten task je do M42, ze bysme | + | v tipu je natrenovat MacDonnalda na dialog datech, ten task je do M42, ted ne. |
- | ===== Semantic Parsing ===== | ||
- | features: meaning representation with semantic roles (69 labels), coordinations, | ||
- | performance indicator: f-measure | ||
- | ===== Information Extraction ===== | + | |
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+ | ===== Semantic Parsing (WP 5.2) ===== | ||
+ | features: assignment of semantic roles (69 roles), coordinations, | ||
+ | performance indicator: accuracy (correctly induced edges, labels) | ||
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+ | ===== Information Extraction | ||
features: template based identification of predicates | features: template based identification of predicates | ||
covering predicates from before-mentioned set of DAFs. | covering predicates from before-mentioned set of DAFs. | ||
performance indicator: accuracy | performance indicator: accuracy | ||
- | ===== Named Entities Recognition ===== | + | |
- | features: detect person names, geographical locations | + | |
+ | ===== Named Entities Recognition | ||
+ | features: detect person names, geographical locations, organizations | ||
performance indicator: f-measure | performance indicator: f-measure | ||
- | ===== Dialog Act Tagging ===== | ||
- | features: tagset derived from DAMSL-SWBD, DA is a key feature driving | ||
- | performance indicator: | ||
- | ===== Sentiment Analysis ===== | ||
- | features: | ||
- | performance indicator: | ||
- | ===== Complete System Evaluation | + | ===== Dialog Act Tagging (WP 5.2) ===== |
- | T5.2.7 tohle zminuje, nick webb to pro nas asi neudela | + | features: domain tailored tagset (variation of DAMSL-SWBD) |
- | performance indicator: | + | performance indicator: |
- | ===== Dialog Manager ===== | + | |
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+ | ===== Dialog Manager | ||
features: reply types, using (language independed) predicates (prakticky to znamena, ze pojmenuju testy na prechodech v dafech anglicky) | features: reply types, using (language independed) predicates (prakticky to znamena, ze pojmenuju testy na prechodech v dafech anglicky) | ||
- | performance indicator: | + | Manually created DAFs covering following topics: Person_Retired, |
+ | performance indicator: | ||
- | ===== Natural Language Generation ===== | + | |
- | features: | + | |
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+ | ===== Natural Language Generation | ||
+ | features: | ||
performance indicator: BLEU score | performance indicator: BLEU score | ||
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+ | ====== AZ PO LISTOPADU ====== | ||
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+ | ===== Syntactic Parsing (WP 5.2) ===== | ||
+ | features: adapted to domain (McD trained on manual PDTSC trees) | ||
+ | performance indicator: accuracy (correctly induced edges, labels) | ||
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+ | ===== Sentiment Analysis (WP 5.2) ===== | ||
+ | features: za tohle bych vydaval klasifikator, | ||
+ | performance indicator: f-measure | ||
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+ | ===== Complete System Evaluation ===== | ||
+ | T5.2.7 tohle zminuje, nick webb to pro nas asi neudela | ||
+ | performance indicator: number of tokens in user reply utterances, post-session questionare | ||
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+ | ===== advances ===== | ||
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+ | advances in Czech NLU (on reconstructed spoken data): 300-500vet(? | ||
+ | pos ? analyzovat, generovat a kontrolovat ' |