Recognizing Emotion Cause in Conversations (2012.11820v4)

Abstract: We address the problem of recognizing emotion cause in conversations, define two novel sub-tasks of this problem, and provide a corresponding dialogue-level dataset, along with strong Transformer-based baselines. The dataset is available at https://github.com/declare-lab/RECCON. Introduction: Recognizing the cause behind emotions in text is a fundamental yet under-explored area of research in NLP. Advances in this area hold the potential to improve interpretability and performance in affect-based models. Identifying emotion causes at the utterance level in conversations is particularly challenging due to the intermingling dynamics among the interlocutors. Method: We introduce the task of Recognizing Emotion Cause in CONversations with an accompanying dataset named RECCON, containing over 1,000 dialogues and 10,000 utterance cause-effect pairs. Furthermore, we define different cause types based on the source of the causes, and establish strong Transformer-based baselines to address two different sub-tasks on this dataset: causal span extraction and causal emotion entailment. Result: Our Transformer-based baselines, which leverage contextual pre-trained embeddings, such as RoBERTa, outperform the state-of-the-art emotion cause extraction approaches Conclusion: We introduce a new task highly relevant for (explainable) emotion-aware artificial intelligence: recognizing emotion cause in conversations, provide a new highly challenging publicly available dialogue-level dataset for this task, and give strong baseline results on this dataset.

• The paper introduces the RECCON dataset to isolate emotion causes in dialogues without relying on external context.
• It employs state-of-the-art Transformer models like RoBERTa and SpanBERT for both causal span extraction and emotion entailment tasks.
• Experimental results demonstrate robust baseline performance while highlighting the complexity of syntactic and semantic emotional cues.

Recognizing Emotion Cause in Conversations

The task of recognizing the causes behind emotions expressed in conversational language is a nuanced and complex challenge within the field of NLP. The work discussed in the paper titled "Recognizing Emotion Cause in Conversations" contributes significantly to the exploration of this domain by proposing a novel dataset called RECCON, which is dedicated to the task of identifying emotion causes within dialogue. The research introduces two sub-tasks—causal span extraction and causal emotion entailment—along with robust baseline models using state-of-the-art Transformer architectures like RoBERTa and SpanBERT.

Dataset Creation and Characteristics

The RECCON dataset is synthesized from two prominent conversational datasets: IEMOCAP and DailyDialog. Notably, each conversational instance is analyzed to extract utterance-level emotion causes without relying on external sources, marking a distinction from prior datasets that necessitated external context. IEMOCAP involves longer and more complex dialogues demanding intricate annotation, whereas DailyDialog offers simpler, shorter conversational contexts but poses challenges due to its class imbalance, primarily skewed towards the neutral sentiment. This diversity enriches the dataset with various scenarios, expanding its applicability and fostering more robust models.

Baseline Models and Tasks

The paper outlines two primary subtasks: causal span extraction and causal emotion entailment. For causal span extraction, the goal is to identify textual segments from dialogues that evoke specific emotions in an utterance, whereas causal emotion entailment focuses on associating utterances with their emotional causes within contextual histories.

1. Causal Span Extraction: This task uses methodologies similar to machine reading comprehension from datasets like SQuAD. The use of Transformer models like RoBERTa and SpanBERT enables the addressing of both syntactic and semantic complexities within dialogues.
2. Causal Emotion Entailment: Here, the task is posited as a classification problem to decide whether a given utterance is the causal counterpart to the emotion expressed in another. This involves both pairwise and contextually aware classification strategies.

The experimental results showed that while the proposed models, enhanced by Transformers, provide strong performances, effectively handling the entanglement of emotional cues across dialogues remains a challenge, demonstrating substantial room for improvement.

Implications and Future Directions

The research advances understanding of emotion causes in dialogue-driven systems such as chatbots, virtual assistants, and sentiment analysis tools. These systems benefit from improved interpretability and performance when they can ascertain not just what emotions are present, but also the causal relationships underpinning them.

Future exploration could aim at enhancing the reasoning capabilities of models to better mimic human-like emotional understanding and causation, leveraging both syntactic and pragmatic communication nuances. Integrating multi-modal data, such as audio and visual information from conversational agents, might also enrich the context beyond textual inputs alone, improving emotion cause recognition accuracy.

In conclusion, recognizing emotion causes in conversations is crucial for advancing affect-driven AI applications. This paper provides a foundational dataset and strong baselines for continued exploration and highlights the need for complex reasoning capabilities integrated into computational linguistic frameworks.