  Intelligent voice assistants, such as Apple Siri and Amazon Alexa, are widely
used nowadays. These task-oriented dialog systems require a semantic parsing
module in order to process user utterances and understand the action to be
performed. This semantic parsing component was initially implemented by
rule-based or statistical slot-filling approaches for processing simple
queries; however, the appearance of more complex utterances demanded the
application of shift-reduce parsers or sequence-to-sequence models. While
shift-reduce approaches initially demonstrated to be the best option, recent
efforts on sequence-to-sequence systems pushed them to become the
highest-performing method for that task. In this article, we advance the
research on shift-reduce semantic parsing for task-oriented dialog. In
particular, we implement novel shift-reduce parsers that rely on
Stack-Transformers. These allow to adequately model transition systems on the
cutting-edge Transformer architecture, notably boosting shift-reduce parsing
performance. Additionally, we adapt alternative transition systems from
constituency parsing to task-oriented parsing, and empirically prove that the
in-order algorithm substantially outperforms the commonly-used top-down
strategy. Finally, we extensively test our approach on multiple domains from
the Facebook TOP benchmark, improving over existing shift-reduce parsers and
state-of-the-art sequence-to-sequence models in both high-resource and
low-resource settings.
