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draft [2009/07/15 12:39] ptacek |
draft [2009/09/30 20:41] ptacek |
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- | ====== | + | ====== |
+ | 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 bridge the gap between dis-fluent spontaneous speech and a standard grammatical sentence. | ||
- | Hi Marc, | + | Results of the part-of-speech tagging are passed on to the 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. The Named Entity Recognition module then marks personal names and geographical locations. A Dialog Act classifier combines lexical and morphological features to assess the type of user utterance (such as question, acknowledgement, |
- | ... | ||
- | Re: progress: there is progress in the following: | + | In addition, when generating the system response, the dialogue manager will pass through the NLG module the information about the appropriate communicative function 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, |
- | - speech re-training for the collected dialogue data | ||
- | - additional dialogue transription for ASR is ongoing (WP52.? T5.2.1) | ||
- | - DM has been transferred from USFD to Prague (WP5.3) | ||
- | being extensively tested | ||
- | - DAF editor transfer is complete (WP5.3) | ||
- | - Sample dialogues (specifically aimed at the demo) | ||
- | are ready - issues are being resolved between CU/ZCU | ||
- | - DAFs are being prepared for the SC-CZ scenario AND | ||
- | the sample dialogues | ||
- | - DA set is being prepared, also based on the sample dialogues (WP5.2) | ||
- | - preliminary DA tagger working (WP5.2) | ||
- | - integration work is ongoing (CU/ZCU, internally at CU) | ||
- | but no functioning full demo yet | ||
- | I hope this is OK for the progress report. Pavel (I.) might add more specifics regarding the ASR and especially TTS progress. | + | < |
- | Best, | + | 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, | ||
- | -- Jan | + | 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. |
- | ====== Description | + | 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 |
- | The Czech version | + | The Knowledge Base consists |
- | photopal domena, nahranej korpus, ze na to sou dafy (reusing SHEFF DM intergrated through Inamode Relayer | + | |
- | 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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- | ===== Speech Reconstruction ===== | ||
- | 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, ted ne. | ||
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- | ===== Semantic Parsing ===== | ||
- | features: meaning representation with semantic roles (69 roles), coordinations, | ||
- | performance indicator: f-measure | ||
- | |||
- | ===== Information Extraction ===== | ||
- | features: template based identification of predicates | ||
- | covering predicates from before-mentioned set of DAFs. | ||
- | performance indicator: accuracy | ||
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- | ===== Named Entities Recognition ===== | ||
- | features: detect person names, geographical locations (organizations myslim nepotrebne) | ||
- | performance indicator: f-measure | ||
- | |||
- | ===== Dialog Act Tagging ===== | ||
- | 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 ===== | ||
- | 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: pocet slov ve vypovedich uzivatele(? | ||
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- | ===== 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 vybrane akce | ||
- | |||
- | ===== Natural Language Generation ===== | ||
- | features: variations, underspecified input (dott format), emotional markup (natvrdo v dafech a templatech u hodnoticich vet) | ||
- | performance indicator: BLEU score |