3 accepted papers
Recently the performance of long-term time series forecasting has been greatly improved by deep models. In this paper, we propose a general framework called Multi-scale Moving Transformation (MMT) that can be applied to the state-of-the-art models of time series forecasting tasks. Via comparative ex…
Conversational text-to-SQL is designed to translate multi-turn natural language questions into their corresponding SQL queries. Most advanced conversational text-to-SQL methods are incompatible with generative pre-trained language models (PLMs), such as T5. In this paper, we present a two-stage unif…
Unsupervised constrained text generation aims to generate text under a given set of constraints without any supervised data. Current state-of-the-art methods stochastically sample edit positions and actions, which may cause unnecessary search steps. In this paper, we propose PMCTG to improve effecti…