The Czech version of Companion deals with the Reminiscing about User's Photos scenario taking advantage of date 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's do the topology and handle the states transitions, post november work),
typy odpovedi a zpusob jejich implementace, rucne vyrobene dafy pro nasledujici topics: Person_Retired, Person_in_productive_age, Child, Husband, Wife, Wedding, Christmas, Handle_stalled_dialog
NLP server s tectomt, ASR/TTS/SR client, connected over network
XXX JPta

advances in Czech NLU (on reconstructed spoken data): 300-500vet(?) rucne anotovat pos, a-tree, t-tree, IE predicates, Named Entities, DA pro eval in-domain testy after Nov.
pos ? analyzovat, generovat a kontrolovat 'jen' kde je rozdil ve forme?

features: improved language models, real-time speaker adaptation
performance indicator: WER

features: omit filler phrases, irrelevant speech events, false starts, repetitions, corrections, syntax smoothing (WO,
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

features: XXX Mirek/Johanka
performance indicator: accuracy

features: induce dependencies and labels
performance indicator: f-measure
v tipu je natrenovat MacDonnalda na dialog datech, ten task je do M42, ted ne.

features: meaning representation with semantic roles (69 roles), coordinations, argument structure, partial ellipsis resolution, pronominal anaphora resolution,
performance indicator: f-measure

features: template based identification of predicates
covering predicates from before-mentioned set of DAFs.
performance indicator: accuracy

features: detect person names, geographical locations (organizations myslim nepotrebne)
performance indicator: f-measure

features: tagset derived from DAMSL-SWBD, DA is a key feature driving the decision, what to say next.
performance indicator: accuracy

features: za tohle bych vydaval klasifikator, co rozhoduje ,jestli se rekne 'To je smutné/veselé'. Tem adjektivum rucne priradim negative/positive sentiment.
performance indicator: f-measure

T5.2.7 tohle zminuje, nick webb to pro nas asi neudela
performance indicator: number of tokens in user utterances, post-session questionare

features: reply types, using (language independed) predicates (prakticky to znamena, ze pojmenuju testy na prechodech v dafech anglicky)
performance indicator: rucni hodnoceni prijatelnosti vybrane akce

features: variations, underspecified input (dott format), emotional markup (natvrdo v dafech a templatech u hodnoticich vet)
performance indicator: BLEU score