7,062 research outputs found
Automatic dialog acts recognition based on sentence structure
This paper deals with automatic dialog acts (DAs) recognition in Czech. Our work focuses on two applications: a multimodal reservation system and an animated talking head for hearing-impaired people. In that context, we consider the following DAs: statements, orders, investigation questions and other questions. The main goal of this paper is to propose, implement and evaluate new approaches to automatic DAs recognition based on sentence structure and prosody. Our system is tested on a Czech corpus that simulates a task of train tickets reservation. With lexical-only information, the classification accuracy is 91 %. We proposed two methods to include sentence structure information, which respectively give 94 % and 95 %. When prosodic information is further considered, the recognition accuracy reaches 96 %
Sentence structure for dialog act recognition in Czech
This paper deals with automatic dialog acts (DAs) recognition in Czech based on sentence structure. We consider the following DAs: statements, orders, yes/no questions and other questions. In our previous works, we have proposed, implemented and evaluated new approaches to automatic DAs recognition based on sentence structure and prosody. The word sequences were manually transcribed. The main goal of this paper is to evaluate the performances of our approaches when these word sequences are unknown and estimated from a speech recognizer. Our system is tested on a Czech corpus that simulates a task of train tickets reservation. When manual transcription is used, classification accuracy without and with sentence structure models is 91 %, 94 % and 95 %. The recognition accuracy reaches 96 % with prosodic combination. When word sequences are estimated from a speech recognizer, the classification score is 88 % without and 91 % and 92 % with sentence structure models. The combination with prosody gives 93 % of accuracy
Automatic Dialog Acts Recognition based on Words Clusters
This paper deals with automatic dialog acts (DAs) recognition in Czech. A Dialog act is defined by J. L. Austin [1] as a meaning of an utterance at the level of illocutionary force. The four following DAs are considered: statements, orders, yes/no questions and other questions. In our previous works, we proposed, implemented and evaluated two new approaches to automatic DAs recognition based on sentence structure. These methods have been validated on a Czech corpus that simulates a task of train tickets reservation. The main goal of this paper is to propose a new approach to solve the problem of lack of training data for automatic DA recognition. This approach clusters the words in the sentence into several groups using maximization of mutual information between two neighbor word classes. The classification accuracy of the unigram model (our baseline approach) is 91 %. The proposed method, a clustered unigram model, reduces the DA error rate by 12
Dialogue Act Recognition via CRF-Attentive Structured Network
Dialogue Act Recognition (DAR) is a challenging problem in dialogue
interpretation, which aims to attach semantic labels to utterances and
characterize the speaker's intention. Currently, many existing approaches
formulate the DAR problem ranging from multi-classification to structured
prediction, which suffer from handcrafted feature extensions and attentive
contextual structural dependencies. In this paper, we consider the problem of
DAR from the viewpoint of extending richer Conditional Random Field (CRF)
structural dependencies without abandoning end-to-end training. We incorporate
hierarchical semantic inference with memory mechanism on the utterance
modeling. We then extend structured attention network to the linear-chain
conditional random field layer which takes into account both contextual
utterances and corresponding dialogue acts. The extensive experiments on two
major benchmark datasets Switchboard Dialogue Act (SWDA) and Meeting Recorder
Dialogue Act (MRDA) datasets show that our method achieves better performance
than other state-of-the-art solutions to the problem. It is a remarkable fact
that our method is nearly close to the human annotator's performance on SWDA
within 2% gap.Comment: 10 pages, 4figure
- …