Task-oriented dialog models typically leverage complex neural architectures
and large-scale, pre-trained Transformers to achieve state-of-the-art
performance on popular natural language understanding benchmarks. However,
these models frequently have in excess of tens of millions of parameters,
making them impossible to deploy on-device where resource-efficiency is a major
concern. In this work, we show that a simple convolutional model compressed
with structured pruning achieves largely comparable results to BERT on ATIS and
Snips, with under 100K parameters. Moreover, we perform acceleration
experiments on CPUs, where we observe our multi-task model predicts intents and
slots nearly 63x faster than even DistilBERT.

本研究使用结构化剪枝方法对卷积模型进行压缩，比 BERT 模型的性能差异不大，模型参数少于 100K，适用于移动设备，并在 CPU 上比 DistilBERT 快 63 倍。

任务导向对话中的自然语言理解加速

Accelerating Natural Language Understanding in Task-Oriented Dialog

End-to-end task-oriented dialog models have achieved promising performance on
collaborative tasks where users willingly coordinate with the system to
complete a given task. While in non-collaborative settings, for example,
negotiation and persuasion, users and systems do not share a common goal. As a
result, compared to collaborate tasks, people use social content to build
rapport and trust in these non-collaborative settings in order to advance their
goals. To handle social content, we introduce a hierarchical intent annotation
scheme, which can be generalized to different non-collaborative dialog tasks.
Building upon TransferTransfo (Wolf et al. 2019), we propose an end-to-end
neural network model to generate diverse coherent responses. Our model utilizes
intent and semantic slots as the intermediate sentence representation to guide
the generation process. In addition, we design a filter to select appropriate
responses based on whether these intermediate representations fit the designed
task and conversation constraints. Our non-collaborative dialog model guides
users to complete the task while simultaneously keeps them engaged. We test our
approach on our newly proposed ANTISCAM dataset and an existing
PERSUASIONFORGOOD dataset. Both automatic and human evaluations suggest that
our model outperforms multiple baselines in these two non-collaborative tasks.

本文中，我们介绍了一种基于任务导向对话模型的非协作式对话模型，该模型使用一种分层意图注释方案来处理社交内容并设计了一种过滤器来筛选生成的回复，我们在两个非协作式任务数据集上测试了我们的模型，结果表明它优于多个基准模型。