Dialogue act (DA) classification has been studied for the past two decades
and has several key applications such as workflow automation and conversation
analytics. Researchers have used, to address this problem, various traditional
machine learning models, and more recently deep neural network models such as
hierarchical convolutional neural networks (CNNs) and long short-term memory
(LSTM) networks. In this paper, we introduce a new model architecture,
directed-acyclic-graph LSTM (DAG-LSTM) for DA classification. A DAG-LSTM
exploits the turn-taking structure naturally present in a multi-party
conversation, and encodes this relation in its model structure. Using the STAC
corpus, we show that the proposed method performs roughly 0.8% better in
accuracy and 1.2% better in macro-F1 score when compared to existing methods.
The proposed method is generic and not limited to conversation applications.

本文提出一种新型模型结构 DAG-LSTM 用于 DA 分类，该模型利用了自然存在于多方对话中的转换结构，并将其编码在模型结构中，相对于现有方法，该方法在准确度和宏 F1 得分方面均有 1% 左右的提升。

基于 DAG-LSTMs 的群聊对话行为分类

Dialogue Act Classification in Group Chats with DAG-LSTMs

We have recently seen the emergence of several publicly available Natural
Language Understanding (NLU) toolkits, which map user utterances to structured,
but more abstract, Dialogue Act (DA) or Intent specifications, while making
this process accessible to the lay developer. In this paper, we present the
first wide coverage evaluation and comparison of some of the most popular NLU
services, on a large, multi-domain (21 domains) dataset of 25K user utterances
that we have collected and annotated with Intent and Entity Type specifications
and which will be released as part of this submission. The results show that on
Intent classification Watson significantly outperforms the other platforms,
namely, Dialogflow, LUIS and Rasa; though these also perform well.
Interestingly, on Entity Type recognition, Watson performs significantly worse
due to its low Precision. Again, Dialogflow, LUIS and Rasa perform well on this
task.

比较了不同的 NLU 工具在多个领域中的性能，结果表明在意图分类方面 Watson 表现最佳，而在实体类型识别方面表现不佳。

为构建对话代理评估自然语言理解服务

Benchmarking Natural Language Understanding Services for building  Conversational Agents

Dialogue Act (DA) classification is a challenging problem in dialogue
interpretation, which aims to attach semantic labels to utterances and
characterize the speaker's intention. Currently, many existing approaches
formulate the DA classification problem ranging from multi-classification to
structured prediction, which suffer from two limitations: a) these methods are
either handcrafted feature-based or have limited memories. b) adversarial
examples can't be correctly classified by traditional training methods. To
address these issues, in this paper we first cast the problem into a question
and answering problem and proposed an improved dynamic memory networks with
hierarchical pyramidal utterance encoder. Moreover, we apply adversarial
training to train our proposed model. We evaluate our model on two public
datasets, i.e., Switchboard dialogue act corpus and the MapTask corpus.
Extensive experiments show that our proposed model is not only robust, but also
achieves better performance when compared with some state-of-the-art baselines.

本文提出了一种将 DA 分类问题转化为问答问题并采用改进的动态记忆网络和分层金字塔语句编码器的方法，同时使用对抗训练方法来解决传统训练方法无法正确分类对抗样本的问题，实验结果表明该模型不仅具有鲁棒性，而且相较于一些最新的基准模型表现更优。