arXiv Computation and Language
All Learning Has an Emotional Basis, So Does Task-Oriented Dialogue
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
Related stories
arXiv AI
Sep 10
Exposing Weaknesses in Emotion Recognition in Conversations
arXiv:2609.05806v1 Announce Type: new Abstract: Emotion Recognition in Conversations (ERC) aims to identify speakers' emotions in multi-turn dialogue. Accurate emotion recognition can support a wide...
arXiv Computation and Language
Sep 1
EmoTrace: An Emotion Trajectory-Centered Framework for Psychological Support Dialogue Generation
arXiv:2607.23648v2 Announce Type: replace Abstract: Using large language models (LLMs) to assist psychological counseling is an important task in the field of natural language processing. The constru...
arXiv AI
6d ago
Evaluating LLM-based Agents for Multi-Turn Conversations: A Survey
The article surveys evaluation methods for large language model (LLM)-based agents in multi-turn conversations, reviewing nearly 250 scholarly sources through a PRISMA-inspired framework. It introduces two taxonomies: one outlining what to evaluate—task completion, response quality, user experience, memory and context retention, planning, and tool integration—and another detailing how to evaluate, covering annotation-based, automated, hybrid, and self-judging approaches. The framework incorporates both traditional metrics like BLEU and ROUGE and advanced techniques suited to dynamic, interactive dialogues.
arXiv Computation and Language
Aug 25
ToSCA: Leveraging Hierarchical Reinforcement Learning on Temporal and Strategic Abstractions of Conversational Agents
arXiv:2608.21969v1 Announce Type: new Abstract: Humans have multiple levels of temporal abstractions on daily interaction and thinking, such as concept perception and strategic planning. Inspired by...
arXiv AI
Jul 17
Dialogue Summarization with Emotion Dynamics Using Topic- and Participant-Centric Decomposition
arXiv:2607. 14769v1 Announce Type: cross Abstract: Existing text summarization research has focused much on monologic information (e.
arXiv AI
Sep 28
Affective Flow Language Model for Emotional Support Conversation
The paper introduces the Affective Flow Language Model (AFlow), which treats multi‑turn emotional support conversations as an evolving affective utility flow along dialogue trajectories. AFlow searches diverse support paths, estimates utilities of intermediate states, and employs Affective Flow Preference Optimization (AFPO) to propagate downstream preference signals to earlier states, enabling consistent strategy transitions. Experiments on ExTES and ESConv demonstrate improved strategy alignment, response diversity, and generation quality across various model settings.