While large pre-trained language models accumulate a lot of knowledge in
their parameters, it has been demonstrated that augmenting it with
non-parametric retrieval-based memory has a number of benefits from accuracy
improvements to data efficiency for knowledge-focused tasks, such as question
answering. In this paper, we are applying retrieval-based modeling ideas to the
problem of multi-domain task-oriented semantic parsing for conversational
assistants. Our approach, RetroNLU, extends a sequence-to-sequence model
architecture with a retrieval component, used to fetch existing similar
examples and provide them as an additional input to the model. In particular,
we analyze two settings, where we augment an input with (a) retrieved nearest
neighbor utterances (utterance-nn), and (b) ground-truth semantic parses of
nearest neighbor utterances (semparse-nn). Our technique outperforms the
baseline method by 1.5% absolute macro-F1, especially at the low resource
setting, matching the baseline model accuracy with only 40% of the data.
Furthermore, we analyze the nearest neighbor retrieval component's quality,
model sensitivity and break down the performance for semantic parses of
different utterance complexity.

使用检索式建模的思想，为会话助手的多领域任务定向语义解析问题引入了 RetroNLU 方法，通过在序列到序列模型中扩展检索部件，我们的技术在语义解析的不同语言表达复杂度下均比基线更为出色，尤其是在数据稀缺环境下，只需 40％的数据即可达到基线模型准确度的匹配，超越基线方法 1.5％的绝对宏 - F1。