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- | ====== | + | ====== |
- | [[Progress Report]] | + | The ASR module based on Hidden-Markov models transforms input speech into text, providing a front-end between the user and the Czech demonstrator. The ASR output is smoothed into a form close to standard written text using statistical machine translation in order to |
+ | to bridge the gap between dis-fluent spontaneous speech and a standard grammatical sentence. | ||
+ | The natural language understanding pipeline starts with part-of-speech tagging. Its result is passed on to Maximum Spanning Tree Syntactic parsing module. Tectogrammatical representation of the utterance is constructed once the syntactic parse is available. Annotation of the meaning of a sentence at tectogrammatical layer is more explicit than its syntactic parse and lends itself for information extraction. | ||
+ | The Named Entity Recognition module then marks personal names and geographical locations. C5 based Dialog Act classifier combines lexical and morphological features to assess the type of user utterance (such as question, acknowledgement, | ||
+ | The Czech Companion follows the original idea of Reminiscing about the User's Photos, | ||
+ | taking advantage of the data collected in the first phase of the project (using a Wizard-of-Oz setting). The full recorded corpora was transcribed, | ||
- | ====== Description | + | The architecture is the same as in the English version, i.e. a set of modules communicating through the Inamode (TID) backbone. However, the set of modules is different, see Figure 1. Regarding the physical settings, the Czech version runs on two notebook computers connected by a local network. One serves as a Speech Client, running modules dealing with ASR, TTS and ECA; the other one as an NLP Server. |
- | The Czech version of the Companion deals with the Reminiscing about User' | + | The NLU pipeline, DM, and NLG modules at the NLP Server are implemented using a CU' |
- | The dialog | + | The Knowledge Base consists of objects (persons, events, photos) that model the information acquainted in the course of dialog. Those objects also provide |
- | Our DAFs covering selected topics contain not only Companion replies mined from the corpora, but also new human-authored assessments, | ||
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- | ===== Automatic Speech Recognition (WP 5.1)===== | ||
- | features: improved language models, real-time speaker adaptation | ||
- | performance indicator: WER | ||
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- | ===== Speech Reconstruction (WP 5.2) ===== | ||
- | 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) ===== | ||
- | features: coverage of photo-pal domain, domain adapted tagger **(XXX prida nam Jarka OOV slova co najdeme - Mirek uz vyrabi list, bude PDTSC rucne oznackovane - do listopadu? | ||
- | performance indicator: OOV rate, accuracy | ||
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- | ===== Syntactic Parsing (WP 5.2) ===== | ||
- | features: induce dependencies and labels | ||
- | performance indicator: accuracy (correctly induced edges, labels) | ||
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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 (WP 5.2) ===== | ||
- | features: template based identification of predicates | ||
- | covering predicates from before-mentioned set of DAFs. | ||
- | performance indicator: accuracy | ||
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- | ===== Named Entities Recognition (WP 5.2) ===== | ||
- | features: detect person names, geographical locations, organization names | ||
- | performance indicator: f-measure | ||
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- | ===== Dialog Act Tagging (WP 5.2) ===== | ||
- | features: domain tailored tag-set (variation of DAMSL-SWBD) | ||
- | performance indicator: accuracy | ||
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- | ===== Dialog Manager (WP 5.3) ===== | ||
- | features: integrated DAF-based dialog manager from previous English prototype, | ||
- | manual creation of DAFs covering following topics: Person_retired, | ||
- | performance indicator: acceptability - manual evaluation of actions selected by DM | ||
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- | ===== Natural Language Generation (WP 5.4) ===== | ||
- | features: adding of functional words, morphological adjustments, | ||
- | performance indicator: BLEU score | ||
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- | ===== Emotional TTS (WP 5.5) ===== | ||
- | features: emotions will be expressed implicitly, through the usage of communicative functions; new female voice database was recorded for this purposes | ||
- | performance indicator: listening tests | ||
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- | ===== Emotional Avatar Integration (WP 5.5) ===== | ||
- | features: new Czech female voice with affective features will be integrated with the TID avatar | ||
- | performance indicator: subjective evaluation of the naturalness and the ability to convey emotions (small-scale, | ||
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- | --------------cut---------------- | ||
- | ====== AZ PO LISTOPADU (NENI SOUCASTI ZPRAVY PRO PO) ====== | ||
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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 ' |