A Unified Framework for Emotion Identification and Generation in Dialogues (2023.eacl-srw)
Copied to clipboard
| Challenge: | Social chatbots have gained immense popularity and can be used to develop and promote social chatbot applications. |
| Approach: | They propose a multi-task framework that jointly identifies the emotion of a given dialogue and generates response in accordance to the identified emotion. |
| Outcome: | The proposed framework outperforms current state-of-the-art models with classification and generation loss. |
Similar Papers
Modelling Context Emotions using Multi-task Learning for Emotion Controlled Dialog Generation (2021.eacl-main)
Copied to clipboard
| Challenge: | Recent research has tackled this task using neural generative methods by augmenting emotion classes with the input sequences. |
| Approach: | They propose to use a self-attention based encoder and a decoder with dot product attention mechanism to generate a viable response with a specified emotion. |
| Outcome: | The proposed model outperforms baselines on automatic evaluation measures such as F1 and BLEU scores, thus resulting in more fluent and adequate responses. |
Multi-Task Learning of Generation and Classification for Emotion-Aware Dialogue Response Generation (2021.naacl-srw)
Copied to clipboard
| Challenge: | Existing models for human-like interaction with humans are not expected to improve the accuracy of emotion recognition, but instead focus on generating emotion-aware responses. |
| Approach: | They propose a neural response generation model with multi-task learning of generation and classification, focusing on emotion. |
| Outcome: | The proposed model makes generated responses more emotionally aware. |
A Taxonomy of Empathetic Response Intents in Human Social Conversations (2020.coling-main)
Copied to clipboard
| Challenge: | Open-domain conversational agents or chatbots are becoming increasingly popular in the natural language processing community. |
| Approach: | They aim to combine dialogue act/intent modelling and neural response generation to produce a large-scale taxonomy for empathetic response intents. |
| Outcome: | The proposed method improves the response quality of chatbots and makes them more controllable and interpretable. |
E-CORE: Emotion Correlation Enhanced Empathetic Dialogue Generation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Empathy is a desirable human trait that improves the emotional perceptivity in emotion-bonding social activities. |
| Approach: | They propose a framework that integrates emotion correlation learning, utilization, and supervising. |
| Outcome: | The proposed framework improves empathetic perception and expression on a humanized dialogue dataset. |
EmpDG: Multi-resolution Interactive Empathetic Dialogue Generation (2020.coling-main)
Copied to clipboard
| Challenge: | Existing work on empathetic dialogue generation fails to capture the nuances of human emotion and consider the potential of user feedback. |
| Approach: | They propose a multi-resolution adversarial model - EmpDG - to generate more empathetic responses by exploiting both coarse-grained dialogue-level and fine-grounded token-level emotions. |
| Outcome: | The proposed model outperforms the state-of-the-art models in both content quality and emotion perceptivity. |
ECC: Synergizing Emotion, Cause and Commonsense for Empathetic Dialogue Generation (2025.coling-main)
Copied to clipboard
| Challenge: | Empathy improves human-machine dialogue systems by enhancing the user's experience. |
| Approach: | They propose a framework that leverages specialized encoders to capture the key features of emotion, cause, and commonsense and collaboratively models these through a Conditional Variational Auto-Encoder. |
| Outcome: | Empirical results show that the framework outperforms baseline models and offers a robust solution for empathetic dialogue generation. |
Automatic Dialogue Generation with Expressed Emotions (N18-2)
Copied to clipboard
| Challenge: | a growing interest in neural dialogue generation systems is focusing on generating human-like responses based on past utterances . despite efforts, few consider putting restrictions on the response itself . authors present three models that concatenate the desired emotion with the source input . |
| Approach: | They propose three models that concatenate the desired emotion with the source input or push the emotion in the decoder. |
| Outcome: | The proposed model is more efficient than the previous models, but it lacks the emotion vector. |
Enhancing Emotional Support Conversations: A Framework for Dynamic Knowledge Filtering and Persona Extraction (2025.coling-main)
Copied to clipboard
| Challenge: | Existing dialogue models struggle to interpret context accurately due to irrelevant or misclassified knowledge, limiting their effectiveness in real-world scenarios. |
| Approach: | They propose a framework that dynamically filters relevant commonsense knowledge and extracts personalized information to improve empathetic dialogue generation. |
| Outcome: | The proposed framework outperforms existing models in coherence, emotional understanding, and response relevance on the ESConv dataset. |
Multi-Party Empathetic Dialogue Generation: A New Task for Dialog Systems (2022.acl-long)
Copied to clipboard
| Challenge: | Existing work on empathetic dialogues focused on the two-party scenario, but multi-party dialogues are pervasive in reality. |
| Approach: | They propose a multi-party empathetic dialogue generation task that uses a static-dynamic model to explore emotion and sensibility. |
| Outcome: | The proposed task is based on a model with static sensibility and dynamic emotion . it achieves state-of-the-art performance in multi-party empathetic dialogue learning . |
Constructing Emotional Consensus and Utilizing Unpaired Data for Empathetic Dialogue Generation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models for dialogue empathy focus on the emotion flow in one direction, from context to response. |
| Approach: | They propose a dual-generative model to construct emotional consensus and use unpaired data to produce pseudo paired empathetic samples. |
| Outcome: | The proposed model outperforms baseline models in producing coherent and empathetic responses. |