Modeling Local Contexts for Joint Dialogue Act Recognition and Sentiment Classification with Bi-channel Dynamic Convolutions (2020)

Abstract: In this paper, we target improving the joint dialogue act recognition (DAR) and sentiment classification (SC) tasks by fully modeling the local contexts of utterances. First, we employ the dynamic convolution network (DCN) as the utterance encoder to capture the dialogue contexts. Further, we propose a novel context-aware dynamic convolution network (CDCN) to better leverage the local contexts when dynamically generating kernels. We extended our frameworks into bi-channel version (i.e., BDCN and BCDCN) under multi-task learning to achieve the joint DAR and SC. Two channels can learn their own feature representations for DAR and SC, respectively, but with latent interaction. Besides, we suggest enhancing the tasks by employing the DiaBERT language model. Our frameworks obtain state-of-the-art performances against all baselines on two benchmark datasets, demonstrating the importance of modeling the local contexts.

## intorduction dialogue act recognition (dar) aims to detect speaker's intentions (e.g., question, agreement or statement) in each utterance, which can facilitate dialog systems to produce appropriate responses @xcite . recent studies have further revealed that simultaneously recognizing the dialog act and detecting the sentiment in dialog can result in better grasping of speaker's intention @xcite . these two tasks are closely relevant, i.e., they mutually promote each other by being jointly performed. on the one hand, the dar provides clues for sentiment classification (sc). in return, the sentiment transitions also can benefit dialogue act prediction. taking the utterances in ## related work dialogue act recognition (dar) plays an important role in building intelligent and interactive conversation systems and producing appropriate responses. existing researches for dar can be summarized into two categories. initial works make a prediction for each dialogue utterance independently by traditional statistical classifiers with hand-crafted discrete features @xcite @xcite @xcite . later, researchers treat dar as a sequence labeling problem and apply neural network methods to reach significant improvements @xcite @xcite @xcite . on the other hand, capturing the sentiment polarity or opinions according to the texts, aka. sentiment classification has long been a heated research topic in nlp community @xcite @xcite . it has been revealed recently that the dialogue act recognition and sentiment classification can be closely related, and the joint learning of these two tasks is more beneficial @xcite @xcite . in this work, we follow these works by employing the joint scheme of the two tasks. differently, we aim to better capture the local contexts of dialogue utterances for task improvements. this work also relates to the cnn models. cnn is prominent on retrieving the local features among texts or discourses @xcite @xcite , functioning by capturing the n-gram patterns within input contents @xcite . a large body of studies considers retrofit cnn to yield better task performances. for example, @xcite apply depth-wise separable convolutions to neural machine translation, which perform convolutions independently over every channel dimension. based on the work of @xcite , @xcite introduce dynamic convolutions. they define a number of convolution heads where each head shares kernel over every dimension computed at each time step. in this work, we take advances of the cnn-like models for sufficiently mining the local context information in dialogue utterances. based on the dynamic convolutions, we newly propose context-aware dynamic convolutions, which is expected to give the enhanced capability of local feature extraction for our task. ## framework we cast both the dar and sc as a sequence labeling problem. given a dialogue c with a sequence of utterances @xmath0, u 2 , . . . , u t }, where t is the length of the sequence, the objective of dar is to predict the corresponding dialogue act labels in sc, the goal is to determine the utterance sequence to the corresponding sentiment labels y @xmath1, y s 2 , . . . , y s t }. our framework is built based on either the dynamic convolution network (dcn) or context-aware dynamic convolution network (cdcn). we achieve the joint tasks of dar and sc by extending these networks into bi-channel version (i.e., bdcn or bcdcn), where each subtask takes one channel but with latent interactions. these two tasks can be jointly trained under the multi-task learning framework. as illustrated in figure 2 (a), the overall architecture of our framework consists of three tiers. at the input layer, the bilstm encodes the input utterance texts into representations. then at the utterance encoder layer, the stacked multi-layer bdcn (or bcdcn) captures the context representations for dar and sc, respectively. finally, at the output layer, the model predicts labels for two tasks separately. ## diabert: pre-trained contextualized utterance representation in §3.1 we use a look-up table for getting the initial word embedding. pre-trained contextualized word representations from bert language model @xcite have brought great benefits to a wide range of downstream nlp tasks @xcite . the very recent work @xcite demonstrates that using bert brings large-scale task improvements for dar and sc. with maximum two sentence pieces, while often one dialog can comprise far more than two utterance sentences. directly using bert can lead to discourse information incoherence. to this end, we leverage a dialogue-level (discourse-level) bert-based encoder diabert @xcite to take the whole dialogue and output the complete representation. in bert there can be only one @xcite token for splitting at most a pair of utterance sentences, while diabert treats each utterance as a segment, each with a [cls] token at the start of the utterance for distinguishing different utterance, as illustrated in figure 2(b) . diabert entails token-level and dialog-level output features. ## conclusion in this work, we proposed to improve the joint dialogue act recognition (dar) and sentiment classification (sc) by fully modeling the local contexts of utterances. based on the dynamic convolution network, we proposed a context-aware dynamic convolution network for better leveraging the local dialogue contexts when generating convolution kernels. we extended our frameworks into bi-channel version under multi-task learning for the joint dar and sc. our models obtained state-of-the-art performances on two benchmark datasets, demonstrating the significance of modeling the local contexts for the joint task.