View publication

In-context learning with Large Language Models (LLMs) has emerged as a promising avenue of research in Dialog State Tracking (DST). However, the best-performing in-context learning methods involve retrieving and adding similar examples to the prompt, requiring access to labeled training data. Procuring such training data for a wide range of domains and applications is time-consuming, expensive, and, at times, infeasible. While zero-shot learning requires no training data, it significantly lags behind the few-shot setup. Thus, 'Can we efficiently generate synthetic data for any dialogue schema to enable few-shot prompting'? Addressing this question, we propose SynthDST, a data generation framework tailored for DST, utilizing LLMs. Our approach only requires the dialogue schema and a few hand-crafted dialogue templates to synthesize natural, coherent, and free-flowing dialogues with DST annotations. Few-shot learning using data from SynthDST results in 4-5% improvement in Joint Goal Accuracy over the zero-shot baseline on MultiWOZ 2.1 and 2.4. Remarkably, our few-shot learning approach recovers nearly 98% of the performance compared to the few-shot setup using human-annotated training data

Related readings and updates.

Learning to Detect Novel and Fine-Grained Acoustic Sequences Using Pretrained Audio Representations

This work investigates pre-trained audio representations for few shot Sound Event Detection. We specifically address the task of few shot detection of novel acoustic sequences, or sound events, with semantically meaningful temporal structure without assuming access to non-target audio. We develop procedures for pre-training suitable representations and methods that transfer them to our few shot learning scenario. Our experiments evaluate the…
See paper details

Conversational Semantic Parsing for Dialog State Tracking

We consider a new perspective on dialog state tracking (DST), the task of estimating a user's goal through the course of a dialog. By formulating DST as a semantic parsing task over hierarchical representations, we can incorporate semantic compositionality, cross-domain knowledge sharing and co-reference. We present TreeDST, a dataset of 27 thousand conversations annotated with tree-structured dialog states and system acts. We describe an…
See paper details