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.
Recently, approaches based on deep learning methods improved many state-of-the-art techniques in NLP including DAR accuracy on open-domain conversations @cite_39 @cite_24 @cite_23 @cite_14 @cite_38 . @cite_39 used a mixture of CNN and RNN. CNNs were used to extract local features from each utterance and RNNs were used to create a general view of the whole dialogue. @cite_6 design a deep neural network model that benefits from pre-trained word embeddings combined with a variation of the RNN structure for the DA classification task. @cite_23 also investigated the performance of using standard RNN and CNN on DA classification and got the cutting edge results on the MRDA corpus using CNN. @cite_38 proposes a model based on CNNs and RNNs that incorporates preceding short texts as context to classify current DAs. @cite_24 combine heterogeneous information with conditional random fields for Chinese dialogue act recognition. @cite_14 build a hierarchical encoder with CRF to learn multiple levels of utterance and act dependencies.
@cite_38 @cite_14 @cite_6 @cite_39 @cite_24 @cite_23
2297405797 2573626026 2964106094 580138839
Recent approaches based on artificial neural networks (ANNs) have shown promising results for short-text classification. However, many short texts occur in sequences (e.g., sentences in a document or utterances in a dialog), and most existing ANN-based systems do not leverage the preceding short texts when classifying a subsequent one. In this work, we present a model based on recurrent neural networks and convolutional neural networks that incorporates the preceding short texts. Our model achieves state-of-the-art results on three different datasets for dialog act prediction. This paper applies a deep long-short term memory (LSTM) structure to classify dialogue acts in open-domain conversations. The compositionality of meaning extends beyond the single sentence. Just as words combine to form the meaning of sentences, so do sentences combine to form the meaning of paragraphs, dialogues and general discourse. We introduce both a sentence model and a discourse model corresponding to the two levels of compositionality. The sentence model adopts convolution as the central operation for composing semantic vectors and is based on a novel hierarchical convolutional neural network. The discourse model extends the sentence model and is based on a recurrent neural network that is conditioned in a novel way both on the current sentence and on the current speaker. The discourse model is able to capture both the sequentiality of sentences and the interaction between different speakers. Without feature engineering or pretraining and with simple greedy decoding, the discourse model coupled to the sentence model obtains state of the art performance on a dialogue act classification experiment. Dialogue act (DA) recognition is a fundamental step for computers to understand natural-language dialogues because it can reflect the intention of a speaker. However, it is difficult to adapt traditional machine learning models to the dialogue act recognition task due to the heterogeneous features, statistical dependence between the DA tags, and complex relationship between features and the DA tags. In this paper, we propose a new model which combines heterogeneous deep neural networks with conditional random fields (HDNN-CRF) to solve this problem. The proposed model has two main advantages. First, the heterogeneous deep neural networks (HDNN) model, which is extended from the deep neural networks (DNN), retains the powerful ability of representation learning and adds a new skill of dealing with heterogeneous features effectively. Second, the conditional random fields (CRF) can capture the statistical dependence between the DA tags which carries important information to determine the DA tag of the current utterance. To verify the effectiveness of the proposed model, we conduct several experiments on a Chinese corpus, called CASIA-CASSIL corpus. Ten kinds of features are extracted from the utterances. In the experiment, we give some quantitative analysis of these kinds of features. What's more, when comparing classification accuracies of the proposed model and some other models, the proposed model has achieved the best performance. HighlightsThe challenge of dialogue act recognition is discussed.Multi-modal deep neural networks are combined with conditional random fields for dialogue act recognition.Numerical experiments are described and good performance is observed.