Summary of Mixed-session Conversation with Egocentric Memory, by Jihyoung Jang et al.
Mixed-Session Conversation with Egocentric Memory
by Jihyoung Jang, Taeyoung Kim, Hyounghun Kim
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this research paper, the authors aim to bridge the gap between current dialogue systems and real-world conversations by introducing a new system called Mixed-Session Conversation (MSC). MSC is designed to facilitate dynamic, continuous interactions involving multiple partners in a multi-session setup. To implement this system, the authors propose a new dataset called MiSC, which consists of 6 consecutive sessions with four speakers per episode. They also introduce a novel dialogue model, Egocentric Memory Enhanced Mixed-Session Conversation Agent (EMMA), which enables seamless continuity in subsequent interactions by retaining memories from the main speaker’s perspective. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re having a conversation with friends that starts and stops over several days. You might mention something earlier in the day, and then pick it up again later without repeating what you said before. That’s kind of what this paper is all about: making computers have conversations like humans do. The researchers created a new way for computers to talk called Mixed-Session Conversation (MSC). MSC lets computers have conversations with different people over several sessions, just like we do when chatting with friends. They also built a special computer program called EMMA that helps the conversation flow smoothly from one session to another. |
