DiaRelay: Relaying Dialogue Context with a Constant-Size Memory for Emotion Recognition in Conversation

• URL: http://arxiv.org/abs/2608.22745v1
• Date: Mon, 24 Aug 2026 03:09:15 GMT
• Title: DiaRelay: Relaying Dialogue Context with a Constant-Size Memory for Emotion Recognition in Conversation
• Abstract summary: Emotion Recognition in Conversation (ERC) requires models to identify subtle emotional cues that are often distributed across distant dialogue turns.<n>We propose a lightweight adapter, DiaRelay, to enable LLMs to explicitly maintain a dialogue-level memory for accurate ERC.<n>We show that DiaRelay can achieve SOTA weighted F1 and accuracy on MELD while obtaining competitive results on IEMOCAP with only an extra 7.1M trainable parameters.
• Score: 27.460834617156028
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Emotion Recognition in Conversation (ERC) requires models to identify subtle emotional cues that are often distributed across distant dialogue turns. Existing methods typically incorporate dialogue history through a fixed context window. However, short windows discard potentially useful long-range evidence, while enlarging the window repeatedly re-encodes overlapping utterances, increases computational and memory costs, and may introduce irrelevant context. Moreover, commonly used parameter-efficient adaptation methods, such as LoRA, mainly introduce fixed low-rank transformations in the feature space and do not explicitly maintain a dialogue-level state or condition their transformations on the evolving conversational context. To address these limitations, we propose a lightweight adapter, DiaRelay, to enable LLMs to explicitly maintain a dialogue-level memory for accurate ERC. Based on LoRA, DiaRelay introduces two extra tightly collaborative components, Selective Relay Memory Transition and Dual-axis Relay Memory Read. Selective Relay Memory Transition progressively aggregates useful historical evidence into a bounded relay memory and propagates it across successive utterance predictions. This allows earlier emotional cues to influence later predictions after they leave the local context window, without re-encoding the complete dialogue history or expanding the backbone context length. Dual-axis Relay Memory Read uses the propagated memory to dynamically modulate low-rank feature transformations, enabling context-dependent representation adaptation without test-time gradient updates. Extensive experiments show that DiaRelay can achieve SOTA weighted F1 and accuracy on MELD while obtaining competitive results on IEMOCAP with only an extra 7.1M trainable parameters, indicating the effectiveness and generalizability of our DiaRelay in enhancing LLM-based emotional understanding.

Related papers

• CueMem: Cue-Guided Context Reconstruction for Long-Term Conversational Memory [38.76918926805768]
  CueMem is a cue-guided framework that treats extracted memory records as retrieval cues.<n>It reconstructs query-relevant dialogue context from their source turns.<n>Experiments on LoCoMo and LongMemEval show that CueMem consistently outperforms representative long-term memory baselines.
  arXiv  Detail & Related papers  (2026-09-11T02:27:22Z)
• Ontology Memory-Augmented ASR Correction for Long Text-Speech Interleaved Conversations [49.31192454394301]
  We propose a memory-augmented ASR correction framework for long text-speech interleaved conversations.<n>We show that our method improves over direct correction in 9 out of 10 paired backbone-setting combinations.
  arXiv  Detail & Related papers  (2026-06-11T15:18:32Z)
• CALMem : Application-Layer Dual Memory for Conversational AI [0.0]
  CALMem is an application-layer dual memory architecture that gives conversational assistants virtually effective context without any modification to the underlying model.<n>A token-budget-adaptive injection mechanism, called the MOIM, automatically retrieves and injects relevant past context each turn.<n>We describe the architecture, design decisions, and performance characteristics, and analyse the trade-offs that guided each implementation choice.
  arXiv  Detail & Related papers  (2026-05-20T05:23:57Z)
• Rethinking How to Remember: Beyond Atomic Facts in Lifelong LLM Agent Memory [88.81430082035617]
  LLM agents require a memory system that can faithfully store, efficiently retrieve, and deeply reason over accumulated dialogue history.<n>We propose TriMem, which maintains three coexisting representation granularities, including raw dialogue segments anchored by source identifiers for storage fidelity.
  arXiv  Detail & Related papers  (2026-05-19T15:05:06Z)
• PERMA: Benchmarking Personalized Memory Agents via Event-Driven Preference and Realistic Task Environments [72.02445514666428]
  We introduce PERMA, a benchmark designed to evaluate persona consistency over time beyond static preference recall.<n>PerMA consists of temporally ordered interaction events spanning multiple sessions and domains, with preference-related queries inserted over time.<n> Experiments demonstrate that by linking related interactions, advanced memory systems can extract more precise preferences and reduce token consumption.
  arXiv  Detail & Related papers  (2026-03-24T14:04:11Z)
• Evoking User Memory: Personalizing LLM via Recollection-Familiarity Adaptive Retrieval [59.295767860331004]
  RF-Mem is a familiarity uncertainty-guided dual-path memory retriever.<n>It embeds human-like dual-process recognition into the retriever.<n>It consistently outperforms one-shot retrieval and full-context reasoning under fixed budget and latency constraints.
  arXiv  Detail & Related papers  (2026-03-10T06:31:44Z)
• SGMem: Sentence Graph Memory for Long-Term Conversational Agents [14.89396085814917]
  We introduce SGMem (Sentence Graph Memory), which represents dialogue as sentence-level graphs within chunked units.<n>We show that SGMem consistently improves accuracy and outperforms strong baselines in long-term conversational question answering.
  arXiv  Detail & Related papers  (2025-09-25T14:21:44Z)
• In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents [70.12342024019044]
  Large Language Models (LLMs) have made significant progress in open-ended dialogue, yet their inability to retain and retrieve relevant information limits their effectiveness.<n>We propose Reflective Memory Management (RMM), a novel mechanism for long-term dialogue agents, integrating forward- and backward-looking reflections.<n>RMM shows more than 10% accuracy improvement over the baseline without memory management on the LongMemEval dataset.
  arXiv  Detail & Related papers  (2025-03-11T04:15:52Z)
• Recursively Summarizing Enables Long-Term Dialogue Memory in Large Language Models [30.48902594738911]
  Given a long conversation, large language models (LLMs) fail to recall past information and tend to generate inconsistent responses.<n>We propose to generate summaries/ memory using large language models (LLMs) to enhance long-term memory ability.
  arXiv  Detail & Related papers  (2023-08-29T04:59:53Z)
• SCM: Enhancing Large Language Model with Self-Controlled Memory Framework [54.33686574304374]
  Large Language Models (LLMs) are constrained by their inability to process lengthy inputs, resulting in the loss of critical historical information.<n>We propose the Self-Controlled Memory (SCM) framework to enhance the ability of LLMs to maintain long-term memory and recall relevant information.
  arXiv  Detail & Related papers  (2023-04-26T07:25:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.