Domain-Adapted Retrieval for In-Context Annotation of Pedagogical Dialogue Acts

TL;DR

This paper introduces a domain-adapted retrieval-augmented generation pipeline that improves pedagogical dialogue act annotation by fine-tuning a lightweight embedding model and indexing dialogues at the utterance level, outperforming baselines.

Contribution

It demonstrates that domain-adapted retrieval significantly enhances dialogue act annotation accuracy without fine-tuning large language models.

Findings

Achieves Cohen's κ of 0.526-0.580 on TalkMoves and 0.659-0.743 on Eedi datasets.

Utterance-level indexing is the main factor driving performance gains.

Retrieval corrects systematic label biases and improves rare label detection.

Abstract

Automated annotation of pedagogical dialogue is a high-stakes task where LLMs often fail without sufficient domain grounding. We present a domain-adapted RAG pipeline for tutoring move annotation. Rather than fine-tuning the generative model, we adapt retrieval by fine-tuning a lightweight embedding model on tutoring corpora and indexing dialogues at the utterance level to retrieve labeled few-shot demonstrations. Evaluated across two real tutoring dialogue datasets (TalkMoves and Eedi) and three LLM backbones (GPT-5.2, Claude Sonnet 4.6, Qwen3-32b), our best configuration achieves Cohen's $\kappa$ of 0.526-0.580 on TalkMoves and 0.659-0.743 on Eedi, substantially outperforming no-retrieval baselines ($\kappa = 0.275$-$0.413$ and $0.160$-$0.410$). An ablation study reveals that utterance-level indexing, rather than embedding quality alone, is the primary driver of these gains, with top-1 label match rates improving from 39.7\% to 62.0\% on TalkMoves and 52.9\% to 73.1\% on Eedi under domain-adapted retrieval. Retrieval also corrects systematic label biases present in zero-shot prompting and yields the largest improvements for rare and context-dependent labels. These findings suggest that adapting the retrieval component alone is a practical and effective path toward expert-level pedagogical dialogue annotation while keeping the generative model frozen.