TSAM : A Two-Stream Attention Model for Causal Emotion Entailment (2022)

Abstract: Causal Emotion Entailment (CEE) aims to discover the potential causes behind an emotion in a conversational utterance. Previous works formalize CEE as independent utterance pair classification problems, with emotion and speaker information neglected. From a new perspective, this paper considers CEE in a joint framework. We classify multiple utterances synchronously to capture the correlations between utterances in a global view and propose a Two-Stream Attention Model (TSAM) to effectively model the speaker’s emotional influences in the conversational history. Specifically, the TSAM comprises three modules: Emotion Attention Network (EAN), Speaker Attention Network (SAN), and interaction module. The EAN and SAN incorporate emotion and speaker information in parallel, and the subsequent interaction module effectively interchanges relevant information between the EAN and SAN via a mutual BiAffine transformation. Extensive experimental results demonstrate that our model achieves new State-Of-The-Art (SOTA) performance and outperforms baselines remarkably.

## introduction with the recent proliferation of open conversational data on social media platforms, such as twitter and facebook, emotion analysis in conversations (eac) has become a popular research topic in the field of natural language processing (nlp). most of the existing works on eac mainly focus on emotion recognition in conversations (erc), i.e., recognizing emotion labels of utterances (e.g., happy, sad, etc.) @xcite @xcite . however, @xcite point out that these studies lack further reasoning about emotions, such as understanding the figure 1 : example conversations sampled from the benchmark dataset @xcite stimuli and the cause of the emotion. since recognizing emotion cause in conversations (rec-con) holds the potential to improve the interpretability and performance of affect-based models, @xcite put forward a new promising task, named reccon, which includes two different sub-tasks: causal span extraction (cse) at word/phrase level and causal emotion entailment (cee) at utterance level. due to the simplicity and sufficiency describing emotion causes at the utterance level, we focus on the cee task in this paper, whose goal is to predict which particular utterances in the conversational history contain the cause of non-neutral emotion in the target utterance. compared to the emotion cause extraction (ece) in news articles @xcite , cee is particularly challenging due to the informal expression style and the intermingling dynamic among interlocutors. @xcite consider cee as a set of independent utterance pair classification problems and neglect the emotion and speaker information in the conversational history. thus, they can neither capture the correlations between contextual utterances in a global view nor model the speaker's emotional influences, namely the intra-speaker and inter-speaker emotional influences. 1 intra-speaker emotional influences mean that the cause of the emotion is primarily due to the speaker's stable mood induced from previous dialogue turns. as shown in figure 1 (a) , utterance 1 establishes the concept that speaker a (s a ) likes winter, which triggers a happy mood for future utterances 3 and 5. inter-speaker emotional influences mean that the emotion of the target speaker is induced from an event mentioned or emotion revealed by other speakers. as figure 1 (b) shows, s b 's happy emotion may be triggered by the event "getting married" mentioned by s a , or by the fact that s a is excited about getting married. to remedy this defect, we tackle cee in a joint framework. we classify multiple utterances synchronously to capture the correlations between contextual utterances and propose a tsam to effectively model the speaker's emotional influences in the conversational history. specifically, the tsam contains three modules: ean, san, and interaction module. the ean provides utterance-toemotion interactions to incorporate emotion information by performing attention over emotion embeddings. the san represents different speaker relations between utterances in a graph, which provides utterance-to-utterance interactions to incorporate speaker information by performing attention over the speaker relation graph. these two modules incorporate emotion and speaker information in parallel. moreover, inspired by @xcite , the interaction module interchanges relevant information between the ean and san through a mutual biaffine transformation. finally, the entire tsam can be stacked in multiple layers to refine iteratively and interchange emotion and speaker information. erc recently, due to the proliferation of publicly available conversational datasets @xcite @xcite @xcite , there is a growing number of studies on erc @xcite @xcite @xcite @xcite @xcite @xcite @xcite . although substantial progress has been made in erc, these studies lack further reasoning about emotions, such as understanding the stimuli or the cause of an emotion expressed by a speaker @xcite . reccon for further reasoning about emotions, @xcite propose a new task named reccon, which contains two different sub-tasks: cse at word/phrase level and cee at utterance level. @xcite formalize cee as a set of independent utterance pair classification problems, neglecting the emotion and speaker information in the conversational history. specifically, they pair a target utterance with each utterance in its conversational history and determine whether the utterance san ... .. . ... .. . ## task definition we first define the task of cee formally. for a target utterance u t , i.e., the t th utterance in the conversation, the goal of cee is to predict which particular utterances in the conversational history l(u t ) = (u 1 , u 2 , ..., u t ) are responsible for the non-neutral emotion in the target utterance. u i is set as a positive example if it contains the cause of non-neutral emotion in the target utterance and a negative example otherwise, where @xmath0, ..., t. the independent utterance pair classification framework @xcite performs t independent classifications, each of which takes (u t , u i ) as in-put. therefore, it fails to capture the correlations between contextual utterances in a global view. on the contrary, the proposed joint classification framework only performs one joint classification with l(u t ) as input, which makes it possible to capture the correlations between contextual utterances. ## method the proposed model consists of three components: the contextual utterance representation, the tsam, and the cause prediction modules. the whole architecture of our model is illustrated in figure 2 . ## conclusion and future work for the first time, we tackle cee in a joint framework. we classify multiple utterances synchronously to capture the correlations between contextual utterances in a global view and propose a tsam to effectively model the speaker's emotional influences. experimental results on the benchmark dataset show that our model significantly outperforms the sota model, and further analysis verifies the effectiveness of each component. this paper points out a new reliable route for followup works: incorporating the emotion and speaker information explicitly and modeling the speaker's emotional influences effectively can bring enormous benefits for the tasks similar to cee. in the future, we would explore three aspects: (1) learn emotion recognition and emotion cause recognition in conversations jointly; (2) apply our model to other similar tasks which need to incorporate the speaker and emotion information; (3) incorporate commonsense knowledge into the model explicitly to address situations when the underlying emotion cause is latent.