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We propose a learning approach for turn-level spoken language understanding,which facilitates a user to speak one or more utterances compositionally in aturn for completing a task (e.g., voice ordering). A typical pipelined approachfor these understanding tasks requires non-trivial annotation effort fordeveloping its multiple components. Also, the pipeline is difficult to port toa new domain or scale up. To address these problems, we propose an end-to-endstatistical model with weak supervision. We employ randomized beam search withmemory augmentation (RBSMA) to solve complicated problems for which longpromising trajectories are usually difficult to explore. Furthermore,considering the diversity of problem complexity, we explore automatedcurriculum learning (CL) for weak supervision to accelerate exploration andlearning. We evaluate the proposed approach on real-world user logs of acommercial voice ordering system. Results demonstrate that when trained on asmall number of end-to-end annotated sessions collected with low cost, ourmodel performs comparably to the deployed pipelined system, saving thedevelopment labor over an order of magnitude. The RBSMA algorithm improves thetest set accuracy by 7.8% relative compared to the standard beam search.Automated CL leads to better generalization and further improves the test setaccuracy by 5% relative.

Automated Curriculum Learning for Turn-level Spoken Language Understanding with Weak Supervision