Conversational sentiment analysis (CSA) and emotion-cause pair extraction (ECPE) tasks have attracted increasing attention in recent years. The former aims to predict the sentiment states of speakers in a conversation, and the latter is about extracting emotion-cause clauses in a document. However, one drawback of CSA is that it cannot model the causal reasoning among emotion and neutral utterances from different speakers. In this work, we propose a new task: emotion-cause pair extraction in conversations (ECPEC), which aims to extract pairs of emotional utterances and corresponding cause utterances in conversations. The utterance-level ECPEC task is more challenging since the distance between emotion and cause utterances is larger than that of the clause-level ECPE task. To this end, we build a novel dataset ConvECPE and propose a specifically designed two-step framework for the new ECPEC task. Experimental results on ConvECPE dataset demonstrate the feasibility of the ECPEC task as well as the effectiveness of our framework.

Metaphor is not only a linguistic phenomenon but also reflects the concept projection between source and target domains in human cognition. Previous sequence tagging-based metaphor identification methods could not model the concept projection, resulting in a limitation that the outputs of these models are unexplainable in the predictions of the metaphoricity labels. In this work, we propose the first explainable metaphor identification model, inspired by Conceptual Metaphor Theory. The model is based on statistic learning, a lexical resource, and a novel reward mechanism. Our model can identify the metaphoricity on the word-pair level, and explain the predicted metaphoricity labels via learned concept mappings. The use of the reward mechanism allows the model to learn the optimal concept mappings without knowing their true labels. Our method is also applicable for the concepts that are out of training domains by using the lexical resource. The automatically generated concept mappings demonstrate the implicit human thoughts in metaphoric expressions. Our experiments show the effectiveness of the proposed model in metaphor identification, and concept mapping tasks, respectively.