DARER : Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognition (2022)

Abstract: The task of joint dialog sentiment classification (DSC) and act recognition (DAR) aims to simultaneously predict the sentiment label and act label for each utterance in a dialog. In this paper, we put forward a new framework which models the explicit dependencies via integrating prediction-level interactions other than semantics-level interactions, more consistent with human intuition.Besides, we propose a speaker-aware temporal graph (SATG) and a dual-task relational temporal graph (DRTG) to introduce temporal relations into dialog understanding and dual-task reasoning. To implement our framework, we propose a novel model dubbed DARER, which first generates the context-, speaker- and temporal-sensitive utterance representations via modeling SATG, then conducts recurrent dual-task relational reasoning on DRTG, in which process the estimated label distributions act as key clues in prediction-level interactions.Experiment results show that DARER outperforms existing models by large margins while requiring much less computation resource and costing less training time.Remarkably, on DSC task in Mastodon, DARER gains a relative improvement of about 25% over previous best model in terms of F1, with less than 50% parameters and about only 60% required GPU memory.

## introduction dialog sentiment classification (dsc) and dialog act recognition (dar) are two challenging tasks in dialog systems @xcite . dsc aims to predict the sentiment label of each utterance in a dialog, while dar aims to predict the act label. recently, researchers have discovered that these two tasks are correlative and they can assist each other @xcite . an example is shown in in previous works, different models are proposed to model the correlations between the two tasks. @xcite propose a multi-task model in which the two tasks share a single encoder. @xcite @xcite try to model the semantics-level interactions of the two tasks. the framework of previous models is shown in fig. 1 (a) . for dialog understanding, co-gat @xcite applies graph attention network (gat) @xcite over an undirected disconnected graph which consists of isolated speaker-specific full-connected subgraphs. therefore, it suffers from the issue that the inter-speaker interactions cannot be modeled, and the temporal relations between utterances are omitted. for dual-task reasoning, on the one hand, previous works only consider the parameter sharing and semantics-level interactions, while the label information is not integrated into the dual-task interactions. consequently, the explicit dependencies between the two tasks cannot be captured and previous dual-task reasoning processes are inconsistent with human intuition, which leverages the label information as crucial clues. on the other hand, previous works do not consider the temporal relations between utterances in dual-task reasoning, while in which they play a key role. in this paper, we try to address the above issues by introducing temporal relations and leveraging label information. to introduce temporal relations, we design a speaker-aware temporal graph (satg) for dialog understanding, and a dual-task reasoning temporal graph (drtg) for dual-task relational reasoning. intuitively, different speakers' semantic states will change as the dialog goes, and these semantic state transitions trigger different sentiments and acts. satg is designed to model the speakeraware semantic states transitions, which provide essential indicative semantics for both tasks. since the temporal relation is a key factor in dual-task reasoning, drtg is designed to integrate inner-and inter-task temporal relations, making the dual-task reasoning process more rational and effective. to leverage label information, we propose a new framework, as shown in fig. 1 (b ). except for semantics-level interactions, it integrates several kinds of prediction-level interactions. first, selfinteractions of sentiment predictions and act predictions. in both tasks, there are prediction-level correlations among the utterances in a dialog. in the dsc task, the sentiment state of each speaker tends to be stable until the utterances from others trigger the changes @xcite . in the dar task, there are different patterns (e.g., questions-inform and directives-commissives) reflecting the interactions between act labels @xcite . second, interactions between the predictions and semantics. intuitively, the predictions can offer feedback to semantics, which can rethink then reversely help revise the predictions. third, prediction-prediction interactions between dsc and dar, which model the explicit dependencies. however, since our objective is to predict the labels of both tasks, there is no ground-truth label available for prediction-level interactions. to this end, we design a recurrent dual-task reasoning mechanism that leverages the label distributions estimated in the previous step as prediction clues of the current step for producing new predictions. in this way, the label distributions of both tasks are gradually improved along the step. to implement our framework, we propose a novel dual-task temporal relational recurrent reasoning network (darer), which includes three modules. the dialog understanding module conducts relation-specific graph transformations (rsgt) over satg to generate context-, speakerand temporal-sensitive utterance representations. the initial estimation module outputs the initial label information fed to the recurrent dual-task reasoning module, in which rsgt operates on drtg to conduct dual-task relational reasoning. moreover, we design logic-heuristic training objectives to force dsc and dar to prompt each other in the recurrent dual-task reasoning process gradually. experiments on public datasets show that darer significantly outperforms existing models. and further improvements can be obtained by utilizing pre-trained language models as the utterance encoder. besides, compared with the previous best model, darer reduces the number of parameters, required gpu memory, and training time. the source code of darer is publicly available at https://github.com/xingbowen714/ darer . ## methodology given a dialog consisting of n utterances: @xmath0, u 2 , ..., u n ), our objective is to predict both the dialog sentiment labels y @xmath1, ..., y s n and the dialog act labels y @xmath2, ..., y a n in a single run. before delving into the details of darer, we start with our designed satg and drtg. rij 1 2 3 4 5 6 7 8 ## related works dialog sentiment classification @xcite @xcite @xcite and dialog act recognition @xcite @xcite are both utterance-level classification tasks. recently, it has been found that these two tasks are correlative, and they can work together to indicate the speaker's more comprehensive intentions @xcite . with the development of wellannotated corpora, @xcite , in which both the act label and sentiment label of each utterance are provided, several mod-els have been proposed to tackle the joint dialog sentiment classification and act recognition task. @xcite propose a multi-task framework based on a shared encoder that implicitly models the dual-task correlations. @xcite integrate the identifications of dialog acts, predictors and sentiments into a unified model. to explicitly model the mutual interactions between the two tasks, @xcite propose a stacked co-interactive relation layer and @xcite propose a context-aware dynamic convolution network to capture the crucial local context. more recently, @xcite propose co-gat, which applies graph attentions on a fully-connected undirected graph consisting of two groups of nodes corresponding to the two tasks, respectively. this work is different from previous works on three aspects. first, we model the inner-and interspeaker temporal dependencies for dialog understanding. second, we model the cross-and selftask temporal dependencies for dual-task reasoning; third, we achieve prediction-level interactions in which the estimated label distributions act as important and explicit clues other than semantics. ## conclusion and future work in this paper, we present a new framework that integrates prediction-level interactions to leverage estimated label distribution as explicit and important clues other than implicit semantics. besides, we design the satg and drtg to introduce temporal relations into dialog understanding and dualtask reasoning. moreover, we propose a novel model named darer to allow temporal information, label information, and semantics to work together to let dsc and dar gradually promote each other, which is further forced by the proposed logic-heuristic training objective. experimental results demonstrate the superiority of our method, which not only surpasses previous models on performances by a large margin but also significantly economizes computation resources. our work brings two insights for dialog understanding and multi-task reasoning in dialog systems: (1) exploiting the temporal relations between utterances for reasoning; (2) leveraging estimated label distributions to capture explicit correlations;. in the future, we will apply our method to other multi-task learning scenarios in dialog systems.