Generative Conversational Networks

Abstract

Inspired by recent work in meta-learning and generative teaching networks, we propose a framework called Generative Conversational Networks, in which conversational agents learn to generate their own labelled training data (given some seed data) and then train themselves from that data to perform a given task. We use reinforcement learning to optimize the data generation process where the reward signal is the agent's performance on the task. The task can be any language-related task, from intent detection to full task-oriented conversations. In this work, we show that our approach is able to generalise from seed data and performs well in limited data and limited computation settings, with significant gains for intent detection and slot tagging across multiple datasets: ATIS, TOD, SNIPS, and Restau-rants8k. We show an average improvement of 35% in intent detection and 21% in slot tagging over a baseline model trained from the seed data. We also conduct an analysis of the novelty of the generated data and provide generated examples for intent detection, slot tagging, and non-goal oriented conversations.

PLACES: Prompting Language Models for Social Conversation Synthesis

“…However, prompt-based augmentation strategies are uncontrolled forms of generation, which may result in generation mistakes for labeled datasets (Sahu et al, 2022;Chen et al, 2022;Meng et al, 2022). In contrast, other recent studies have instead proposed language augmentation strategies that use complex, highly-controlled frameworks that often involve fine-tuning generators (Papangelis et al, 2021;Kulhánek et al, 2021;. Such complex augmentation frameworks require larger amounts of seed data to maintain a ground-truth language distribution (Rosenbaum et al, 2022b;Kim et al, 2021b), and are more costly than prompting PLMs (Chen et al, 2022).…”