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draft [2009/07/14 16:08] ptacek vytvořeno |
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- | ====== Description | + | ====== |
- | 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' | + | The ASR module based on Hidden-Markov models transforms input speech into text, providing a front-end between |
- | typy odpovedi | + | to bridge the gap between dis-fluent spontaneous speech and a standard grammatical sentence. |
- | NLP server s tectomt, ASR/TTS/SR client, connected over network | + | |
- | XXX JPta | + | |
- | advances in Czech NLU (on reconstructed spoken data): 100vet(?) rucne anotovat pos, a-tree, t-tree, IE predicates, | + | The natural language understanding pipeline starts with part-of-speech tagging. Its result is passed on to Maximum Spanning Tree Syntactic parsing module. A tectogrammatical representation of the utterance is constructed once the syntactic parse is available. Annotation of the meaning at tectogrammatical layer is more explicit than its syntactic parse and lends itself for information extraction. |
- | pos ? analyzovat, generovat | + | 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, etc.) that is a useful clue for Dialog Manager decisions. |
- | ===== Speech Reconstruction ===== | + | In addition, when generating the system response, the dialogue manager will pass through the NLG module the information about the appropriate communicative function tag (CF, see the CZ TTS module) along with the sentence that is to be generated. NLG is also used to generate paraphrases of user input sentences.The TTS module integrated with the TID avatar transforms system responses from the text form into the speech and visual (face expressions, |
- | 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 ===== | ||
- | features: XXX Mirek/ | ||
- | performance indicator: accuracy | ||
- | ===== Syntactic Parsing ===== | + | < |
- | features: induce dependencies and labels | + | |
- | performance indicator: f-measure | + | |
- | v tipu je natrenovat MacDonnalda na dialog datech, ten task je do M42, ze bysme | + | |
- | ====== Semantic Parsing ====== | + | The Czech Companion follows the original idea of Reminiscing about the User's Photos, |
- | features: meaning representation with semantic roles (69 labels), coordinations, | + | taking advantage of the data collected in the first phase of the project |
- | performance indicator: f-measure | + | |
- | ===== Information Extraction ===== | + | 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. |
- | features: template based identification | + | |
- | covering predicates from before-mentioned | + | |
- | performance indicator: accuracy | + | |
- | ===== Named Entities Recognition ===== | + | The NLU pipeline, DM, and NLG modules at the NLP Server are implemented using a CU's own TectoMT platform that provides access to a single in-memory data representation through a common API. This eliminates the overhead of a repeated serialization and XML parsing that an Inamode based solution would impose otherwise. |
- | features: detect person names, geographical locations (organizations jsou potreba?) | + | |
- | performance indicator: f-measure | + | |
- | ===== Dialog Act Tagging ===== | + | 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' |
- | features: tagset derived from DAMSL-SWBD, DA is a key feature driving | + | |
- | performance indicator: | + | |
- | ===== Sentiment Analysis ===== | ||
- | features: | ||
- | performance indicator: | ||
- | |||
- | ===== complete system evaluation ===== | ||
- | T5.2.7 tohle zminuje, nick webb to pro nas asi neudela | ||
- | performance indicator: pocet slov ve vypovedich uzivatele(? | ||
- | |||
- | ===== Dialog Manager ===== | ||
- | features: reply types, using (language independed) predicates (prakticky to znamena, ze pojmenuju testy na prechodech v dafech anglicky) | ||
- | performance indicator: rucni hodnoceni prijatelnosti vypovedi | ||
- | |||
- | ===== Natural Language Generation ===== | ||
- | features: variations, underspecified input (dott format), emotional markup (natvrdo v dafech a templatech u hodnoticich vet) | ||
- | performance indicator: BLEU score |