The problem with dialogue datasets

Most dialogue datasets used to train and evaluate language models contain only text. A speaker label. A message. Sometimes a sentiment tag. That is the standard format. And for many tasks it is fine. But if you are building systems that need to reason about people — not just respond to them — text alone is not enough. What is actually missing Real conversations are not just sequences of messages. They are driven by internal state that never appears in the transcript: Beliefs about the other person that evolve with each exchange Goals behind each message (seek validation, assert control, repair trust) Relationship dynamics that shift across the conversation: trust, tension, connection Psychological identity that shapes how someone communicates under pressure When a speaker says: "I'm not upset about the meeting, I'm upset you didn't tell me earlier." The text is visible. But what drove that message is not: Their belief that the other person withholds information (confidence: 0.74) A goa