Publication

Improving Semantic Parsing for Task Oriented Dialog

Conversational AI Workshop at NeurIPS 2018


Abstract

Semantic parsing using hierarchical representations has recently been proposed for task oriented dialog with promising results. In this paper, we present three different improvements to the model: contextualized embeddings, ensembling, and pairwise re-ranking based on a language model. We taxonomize the errors possible for the hierarchical representation, such as wrong top intent, missing spans or split spans, and show that the three approaches correct different kinds of errors. The best model combines the three techniques and gives 6.4% better exact match accuracy than the state-of-the-art, with an error reduction of 33%, resulting in a new state-of-the-art result on the Task Oriented Parsing (TOP) dataset.

Related Publications

All Publications

Robust Market Equilibria with Uncertain Preferences

Riley Murray, Christian Kroer, Alex Peysakhovich, Parikshit Shah

AAAI - February 12, 2020

Weak-Attention Suppression For Transformer Based Speech Recognition

Yangyang Shi, Yongqiang Wang, Chunyang Wu, Christian Fuegen, Frank Zhang, Duc Le, Ching-Feng Yeh, Michael L. Seltzer

Interspeech - October 26, 2020

Machine Learning in Compilers: Past, Present, and Future

Hugh Leather, Chris Cummins

FDL - September 14, 2020

Unsupervised Cross-Domain Singing Voice Conversion

Adam Polyak, Lior Wolf, Yossi Adi, Yaniv Taigman

Interspeech - August 8, 2020

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy