Emotion Recognition in Conversation with Commonsense Knowledge

Kernkonzepte

The author proposes the CKERC framework to enhance emotion recognition by incorporating commonsense knowledge into large language models, achieving state-of-the-art results on popular datasets.

Zusammenfassung

Emotion recognition in conversation is crucial for improving human-machine interactions. The CKERC framework leverages historical utterances and commonsense knowledge to enhance speaker information mining and achieve superior performance on emotion recognition tasks.
The dialogue emotion classification task is influenced by various factors such as dialogue context, speaker identity, and multi-party scenarios. Introducing more modal information can improve emotional understanding but may lead to dataset construction challenges.
Previous works have introduced commonsense into emotion recognition tasks, but CKERC stands out by utilizing historical utterances and large language models to generate accurate commonsense information for improved emotional responses.
By replacing speaker identification with interlocutor commonsense identification, CKERC addresses the challenge of insufficient speaker information mining during conversations. This approach leads to competitive performance on emotion recognition tasks across different datasets.

Statistiken

Based on the different history utterances,speaker generates different commonsense information for the same utterance"yeah." So, the same utterance "yeah." in the different conversation expresses different emotion.
The IEMOCAP dataset annotates 6 types of emotions: happy, sad, neutral, angry, excited, and frustrated.
The MELD dataset collected a total of 13118 dialogues covering 7 different emotional categories (anger, disgust, fear, happiness, neutrality, sadness, and surprise).
The EmoryNLP dataset includes 97 episodes with 12606 utterances and seven emotion labels: neutral, joyful, peaceful, powerful, scared, mad and sad.

Zitate

"The development of large language models has transformed natural language processing tasks."
"Our work introduces historical conversation information to predict common sense for current sentences."
"CKERC achieves state-of-the-art performance by leveraging commonsense knowledge in emotion recognition."

Wichtige Erkenntnisse aus

CKERC

by Yumeng Fu um arxiv.org 03-13-2024

https://arxiv.org/pdf/2403.07260.pdf

Tiefere Fragen

How can incorporating historical conversation data improve emotional understanding

Incorporating historical conversation data can significantly enhance emotional understanding in various ways. Firstly, by analyzing past interactions, patterns of emotional responses and triggers can be identified, leading to a more nuanced comprehension of how emotions evolve within conversations. This historical context provides valuable insights into the underlying reasons for certain emotional expressions, helping to interpret current emotions more accurately.
Moreover, historical conversation data allows for the tracking of emotional trajectories over time. By observing how emotions fluctuate and influence each other across multiple dialogues, a deeper understanding of the dynamics of emotional interactions can be gained. This longitudinal perspective enables better prediction and recognition of emotions based on past behaviors and reactions.
Additionally, historical conversation data serves as a rich source of contextual information that aids in interpreting ambiguous or subtle emotional cues. Understanding the background behind specific emotional expressions helps in disambiguating complex emotions and differentiating between similar states such as frustration and anger or excitement and happiness.
Overall, incorporating historical conversation data enriches emotional understanding by providing context, identifying patterns, tracking trajectories, and clarifying ambiguities in emotion recognition tasks.

What are the implications of introducing more modal information into emotion recognition tasks

Introducing more modal information into emotion recognition tasks has several implications for enhancing the accuracy and depth of emotion analysis:

Enhanced Contextual Understanding: Modal information such as audio or video inputs adds layers of context to textual conversations. Audio cues like tone of voice or intonation provide additional signals about speakers' emotions that text alone may not capture fully.

Improved Multi-Modality Analysis: Incorporating multiple modalities allows for a holistic view of communication channels (e.g., speech content along with facial expressions), enabling a comprehensive assessment of speakers' emotions.

Increased Complexity Handling: Multi-modal approaches help address challenges related to handling complex scenarios where textual content alone might not convey the full spectrum of emotions present in a conversation.

Better Emotional Inference: Leveraging modal information alongside text improves emotion inference accuracy by capturing non-verbal cues that are crucial for understanding speaker sentiments comprehensively.

By integrating diverse modalities into emotion recognition tasks, models gain access to richer sources of information that lead to more robust analyses with improved accuracy.

How does CKERC's approach differ from previous methods that incorporate commonsense knowledge

CKERC's approach differs from previous methods incorporating commonsense knowledge primarily through its utilization of historical conversational data:

Historical Conversation Integration: CKERC leverages historical utterances from previous texts to generate commonsense knowledge about current utterances' contexts effectively bridging past interactions with present ones.

Speaker Information Mining Enhancement: Unlike prior methods focusing solely on common sense extraction at token levels or word-based searches without considering discourse history impact on generating current session knowledge; CKERC precisely accounts for this influence resulting in accurate generation of implicit speaker-related details essential for effective emotion recognition.

Discourse-Level Common Sense Extraction: While some approaches extract common sense based only on individual discourses lacking specific background support; CKERC's discourse-level strategy ensures precise meaning expression through detailed consideration of both current and historical dialogue information.

4 .Contextual Relevance Emphasis: By emphasizing contextual relevance derived from both immediate dialogue contexts and past interactions; CKERC's method offers a comprehensive approach to commonsense integration that enhances overall performance in emotion recognition tasks by capturing the subtle nuances embedded within conversational exchanges efficiently.