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draft [2009/07/15 15:14] ptacek |
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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 bridge the gap between dis-fluent spontaneous speech and a standard grammatical sentence. |
+ | Results of part-of-speech tagging are 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. 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, | ||
- | ====== Description | + | 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 |
- | 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 is same as of the English version, i.e. set of modules communicating through the Inamode Relayer (TID) backbone; | ||
- | 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 for expected pomdp DM from UOX (reuse states, let pomdp' | + | < |
- | typy odpovedi a zpusob jejich implementace, | + | |
- | NLP server s tectomt, ASR/TTS/SR client, connected over network | + | |
- | XXX JPta | + | |
- | advances in Czech NLU (on reconstructed spoken | + | The Czech Companion follows the original idea of Reminiscing about the User's Photos, |
- | pos ? analyzovat, generovat | + | taking advantage of the data collected in the first phase of the project |
+ | 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. | ||
+ | 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, 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 PRIDA NAM JARKA SLOVA CO NAJDEME?, JEN V PRIPADE RUCNI ANOTACE COMPANIONS-PDTSC DO LISTOPADU | ||
- | performance indicator: OOV rate, 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 |