, I submitted my MS thesis on Emotion Recognition in Conversation (ERC). The model, EmoNet , achieved a Weighted F1 of 39.18 on EmoryNLP — competitive with the public PapersWithCode leaderboard at the time, sitting between TUCORE-GCN_RoBERTa (39.24) and S+PAGE (39.14), and improving over my chosen baseline, CoMPM, by +1.81 F1 .

Two years later, I returned to look at where the field is now. The leaderboard is unrecognizable. The top entries are no longer encoder-only models with clever attention heads — they’re LLaMA-2–7B-based systems with LoRA fine-tuning and retrieval-augmented prompting : InstructERC, CKERC, BiosERC, LaERC-S. The methods are different. The compute is different. The mindset is different.

And yet — when I read these new papers carefully, the core ideas I proposed in EmoNet show up inside them, just implemented at a different layer of the stack.…