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draft [2009/07/15 14:58] ptacek |
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- | ====== | + | ====== |
- | [[Progress Report]] | + | 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, a manual speech reconstruction was done on 92.6% of utterances((Manual speech reconstruction is still in progress.)) and a pilot dialog acts annotation was performed on a sample of 1000 sentences. | ||
+ | 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 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. | ||
- | ====== Description of Czech Companion November Prototype ====== | + | < |
- | 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 | + | The ASR module based on Hidden-Markov models transforms input speech into text, providing a front-end between |
- | 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 | + | 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 |
- | 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(? | + | The dialog is driven by a Dialog Manager component by USFD (originally developed for the English Senior Companion prototype). |
- | pos ? analyzovat, generovat a kontrolovat ' | + | CU has supplied the transition networks covering following topics: retired_person, husband, child, wife, wedding and Christmas. |
+ | Dialogue Manager provides information about the appropriate communicative function along with the sentence that is to be generated to the NLG module. The TTS module integrated with the TID avatar transforms system responses from the text form into the speech and visual (face expressions, gestures) representation. As such, it provides an interface between the demonstrator and the user. | ||
+ | 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' | ||
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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, | ||
- | 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: 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) | ||
- | Handmade DAF covering following topics: Person_Retired, | ||
- | 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 |