This paper deals with multi-lingual dialogue act (DA) recognition. The proposed approaches are based on deep neural networks and use word2vec embeddings for word representation. Two multi-lingual models are proposed for this task. The first approach uses one general model trained on the embeddings from all available languages. The second method trains the model on a single pivot language and a linear transformation method is used to project other languages onto the pivot language. The popular convolutional neural network and LSTM architectures with different set-ups are used as classifiers. To the best of our knowledge this is the first attempt at multi-lingual DA recognition using neural networks. The multi-lingual models are validated experimentally on two languages from the Verbmobil corpus.
Several lexical models are proposed including Bayesian approaches such as n-gram language models @cite_23 @cite_4 @cite_3 and also non-Bayesian methods -- semantic classification trees @cite_12 , transformation-based learning (TBL) @cite_0 or memory-based learning @cite_13 . Syntactic features that are created using a full parse tree are considered for instance in @cite_14 . Prosodic information is often used to provide additional clues to recognize DAs as presented for instance in @cite_27 . Dialogue history (the sequence of DAs) can been used to predict the most probable next dialogue act and it can be modeled for example by hidden Markov models @cite_4 or Bayesian networks @cite_28 .
@cite_14 @cite_4 @cite_28 @cite_3 @cite_0 @cite_27 @cite_23 @cite_13 @cite_12
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This work studies the usefulness of syntactic information in the context of automatic dialogue act recognition in Czech. Several pieces of evidence are presented in this work that support our claim that syntax might bring valuable information for dialogue act recognition. In particular, a parallel is drawn with the related domain of automatic punctuation generation and a set of syntactic features derived from a deep parse tree is further proposed and successfully used in a Czech dialogue act recognition system based on conditional random fields. We finally discuss the possible reasons why so few works have exploited this type of information before and propose future research directions to further progress in this area. We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as STATEMENT, QUESTION, BACKCHANNEL, AGREEMENT, DISAGREEMENT, and APOLOGY. Our model detects and predicts dialogue acts based on lexical, collocational, and prosodic cues, as well as on the discourse coherence of the dialogue act sequence. The dialogue model is based on treating the discourse structure of a conversation as a hidden Markov model and the individual dialogue acts as observations emanating from the model states. Constraints on the likely sequence of dialogue acts are modeled via a dialogue act n-gram. The statistical dialogue grammar is combined with word n-grams, decision trees, and neural networks modeling the idiosyncratic lexical and prosodic manifestations of each dialogue act. We develop a probabilistic integration of speech recognition with dialogue modeling, to improve both speech recognition and dialogue act classification accuracy. Models are trained and evaluated using a large hand-labeled database of 1,155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We achieved good dialogue act labeling accuracy (65 based on errorful, automatically recognized words and prosody, and 71 based on word transcripts, compared to a chance baseline accuracy of 35 and human accuracy of 84 ) and a small reduction in word recognition error. We explore the two related tasks of dialog act (DA) segmentation and DA classification for speech from the ICSI Meeting Corpus. We employ simple lexical and prosodic knowledge sources, and compare results for human-transcribed versus automatically recognized words. Since there is little previous work on DA segmentation and classification in the meeting domain, our study provides baseline performance rates for both tasks. We introduce a range of metrics for use in evaluation, each of which measures different aspects of interest. Results show that both tasks are difficult, particularly for a fully automatic system. We find that a very simple prosodic model aids performance over lexical information alone, especially for segmentation. Both tasks, but particularly word-based segmentation, are degraded by word recognition errors. Finally, while classification results for meeting data show some similarities to previous results for telephone conversations, findings also suggest a potential difference with respect to the effect of modeling DA context. For the task of recognizing dialogue acts, we are applying the Transformation-Based Learning (TBL) machine learning algorithm. To circumvent a sparse data problem, we extract values of well-motivated features of utterances, such as speaker direction, punctuation marks, and a new feature, called dialogue act cues, which we find to be more effective than cue phrases and word n-grams in practice. We present strategies for constructing a set of dialogue act cues automatically by minimizing the entropy of the distribution of dialogue acts in a training corpus, filtering out irrelevant dialogue act cues, and clustering semantically-related words. In addition, to address limitations of TBL, we introduce a Monte Carlo strategy for training efficiently and a committee method for computing confidence measures. These ideas are combined in our working implementation, which labels held-out data as accurately as any other reported system for the dialogue act tagging task. Identifying whether an utterance is a statement, question, greeting, and so forth is integral to effective automatic understanding of natural dialog. Little is known, however, about how such dialog acts (DAs) can be automatically classified in truly natural conversation. This study asks whether current approaches, which use mainly word information, could be improved by adding prosodic information.The study is based on more than 1000 conversations from the Switchboard corpus. DAs were hand-annotated, and prosodic features (duration, pause, F0, energy, and speaking rate) were automatically extracted for each DA. In training, decision trees based on these features were inferred; trees were then applied to unseen test data to evaluate performance. Performance was evaluated for prosody models alone, and after combining the prosody models with word information—either from true words or from the output of an automatic speech recognizer.For an overall classification task, as well as three subtasks, prosody made s... Ligand-binding, dimerized polypeptide fusions comprising two polypeptide chains are disclosed. Each of the polypeptide chains comprises a ligand-binding domain of either a growth factor receptor or a hormone receptor joined to an immunoglobulin constant region polypeptide. Dimerization of the polypeptide fusion results in enhancement of biological activity. The immunoglobulin constant region polypeptide may comprise an immunoglobulin heavy chain constant region domain and hinge region. This paper presents automatic methods for the classification of dialog acts. In the verbmobil application (speech-to-speech translation of face-to-face dialogs) maximally 50 of the utterances are analyzed in depth and for the rest, shallow processing takes place. The dialog component keeps track of the dialog with this shallow processing. For the classification of utterances without in depth processing two methods are presented: Semantic Classification Trees and Polygrams. For both methods the classification algorithm is trained automatically from a corpus of labeled data. The novel idea with respect to SCTs is the use of dialog state dependent CTs and with respect to Polygrams it is the use of competing language models for the classification of dialog acts.