Progress Report - dal jsem to na zvlastni stranku, abysme si nelezli do zeli

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's do the topology and handle the states transitions, post november work),
typy odpovedi a zpusob jejich implementace,
NLP server s tectomt, ASR/TTS/SR client, connected over network
XXX JPta

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

features: omit filler phrases, remove irrelevant speech events, handle false starts, repetitions, and corrections, polish word ordering
performance indicator: BLEU score between actual output and manually reconstructed sentences from corpora (T5.2.1), baseline: Moses with default settings

features: coverage of photo-pal domain, domain adapted tagger (XXX prida nam Jarka OOV slova co najdeme, bude PDTSC rucne oznackovane - do listopadu?)
performance indicator: OOV rate, accuracy

features: induce dependencies and labels
performance indicator: accuracy (correctly induced edges, labels)
v tipu je natrenovat MacDonnalda na dialog datech, ten task je do M42, ted ne.

features: assignment of semantic roles (69 roles), coordinations, argument structure, partial ellipsis resolution, pronominal anaphora resolution, post parsing detection of ungrammatical edges (caused by long utterances)
performance indicator: accuracy (correctly induced edges, labels)

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

features: detect person names, geographical locations, organizations
performance indicator: f-measure

features: domain tailored tagset (variation of DAMSL-SWBD)
performance indicator: accuracy

features: reply types, using (language independed) predicates (prakticky to znamena, ze pojmenuju testy na prechodech v dafech anglicky)
Manually created DAFs covering following topics: Person_Retired, Person_in_productive_age, Child, Husband, Wife, Wedding, Death, Christmas, Handle_stalled_dialog
performance indicator: acceptability - manual evaluation of actions selected by DM

features: morphological adjustments, generating paraphrases for hard-coded utterances, underspecified input (dott format), passing-through emotional markup (natvrdo v dafech a templatech u hodnoticich vet)
performance indicator: BLEU score

features: adapted to domain (McD trained on manual PDTSC trees)
performance indicator: accuracy (correctly induced edges, labels)

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 reply utterances, post-session questionare

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?