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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 marks personal names and geographical locations. | ||
+ | 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 Companion deals with the Reminiscing about User' | + | The NLU pipeline, DM, and NLG modules at the NLP Server are implemented using a CU' |
- | 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, | + | |
- | photopal domena, nahranej korpus, ze na to sou dafy (reusing SHEFF DM intergrated through Inamode Relayer | + | The Knowledge Base consists of objects (persons, events, photos) that model the information acquainted in the course of dialog. Those objects also provide a very basic reasoning (e.g. accounting for the link between date of birth and age properties). Each object' |
- | typy odpovedi | + | |
- | 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 ' | ||
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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 5.1 ???) ===== | ||
- | 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 | ||
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- | ===== Morphology Analyzer and POS tagging (WP 5.2) ===== | ||
- | features: XXX Mirek/ | ||
- | performance indicator: accuracy | ||
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- | ===== Syntactic Parsing (WP 5.2) ===== | ||
- | features: induce dependencies and labels | ||
- | performance indicator: f-measure | ||
- | v tipu je natrenovat MacDonnalda na dialog datech, ten task je do M42, ted ne. | ||
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- | ===== Semantic Parsing (WP 5.2) ===== | ||
- | features: meaning representation with semantic roles (69 roles), coordinations, | ||
- | performance indicator: f-measure | ||
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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 (organizations myslim nepotrebne) | ||
- | performance indicator: f-measure | ||
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- | ===== Dialog Act Tagging (WP 5.2) ===== | ||
- | features: tagset derived from DAMSL-SWBD, DA is a key feature driving the decision, what to say next. | ||
- | performance indicator: accuracy | ||
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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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- | ===== Dialog Manager (WP 5.3) ===== | ||
- | 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 | ||
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- | ===== Natural Language Generation (WP 5.4) ===== | ||
- | features: variations, underspecified input (dott format), emotional markup (natvrdo v dafech a templatech u hodnoticich vet) | ||
- | performance indicator: BLEU score |