基于Stack-Transformers的移进-归约任务型语义解析
Shift-Reduce Task-Oriented Semantic Parsing with Stack-Transformers
AI总结：
本文提出基于Stack-Transformers的移进-归约任务型语义解析器，融合自底向上和按序转移系统，在Facebook TOP的高、低资源设置中均优于现有方法。
摘要：
如今，Apple Siri和Amazon Alexa等智能语音助手得到了广泛使用。这些任务型对话系统需要一个语义解析模块，以处理用户话语并理解将要执行的动作。该语义解析组件最初通过基于规则或统计的槽位填充方法实现，用于处理简单查询；然而，更复杂话语的出现要求应用移进-归约解析器或序列到序列模型。尽管移进-归约方法最初被认为是最有前景的选择，但序列到序列神经系统的出现已将其推到前沿，成为这一特定任务中性能最高的方法。在本文中，我们推进了任务型对话的移进-归约语义解析研究。我们实现了依赖Stack-Transformers的新型移进-归约解析器。该框架能够在Transformer神经架构上充分建模转移系统，显著提升移进-归约解析性能。此外，我们的方法超越了传统的自顶向下算法：我们将源自成分句法解析的替代性自底向上和按序转移系统纳入任务型解析领域。我们在Facebook TOP基准测试的多个领域上广泛测试了所提方法，在高资源和低资源设置下均优于现有移进-归约解析器以及最先进的序列到序列模型。我们还通过实验证明，按序算法大幅优于常用的自顶向下策略。通过创建创新的转移系统并利用强大神经架构的能力，我们的研究展示了在主要基准测试上移进-归约解析器相比领先序列到序列方法的优越性。
英文摘要：
Intelligent voice assistants, such as Apple Siri and Amazon Alexa, are widely used nowadays. These task-oriented dialogue 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. Although shift-reduce approaches were initially considered the most promising option, the emergence of sequence-to-sequence neural systems has propelled them to the forefront as the highest-performing method for this particular task. In this article, we advance the research on shift-reduce semantic parsing for task-oriented dialogue. We implement novel shift-reduce parsers that rely on Stack-Transformers. This framework allows to adequately model transition systems on the Transformer neural architecture, notably boosting shift-reduce parsing performance. Furthermore, our approach goes beyond the conventional top-down algorithm: we incorporate alternative bottom-up and in-order transition systems derived from constituency parsing into the realm of task-oriented parsing. 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. We also empirically prove that the in-order algorithm substantially outperforms the commonly-used top-down strategy. Through the creation of innovative transition systems and harnessing the capabilities of a robust neural architecture, our study showcases the superiority of shift-reduce parsers over leading sequence-to-sequence methods on the main benchmark.
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