Dialogue Act Classification in Team Communication for Robot Assisted Disaster Response
Dialogue Act Classification in Team Communication for Robot Assisted Disaster Response
复制标题
机器人辅助救灾团队沟通中的对话行为分类
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Ivana Kruijff
中科院分区：
文献类型：
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作者：
Tatiana Anikina;Ivana Kruijff
我们提出了我们在机器人辅助灾难响应领域的人-人团队通信语料库中对对话行为进行分类的结果。我们根据ISO 24617-2标准方案对对话行为进行了注释，并使用FastText线性分类器以及几种神经架构进行了实验，包括具有不同类型嵌入、上下文和注意力机制的前馈、递归和卷积神经模型。最好的性能是通过本文中提出的“Divide & Merge”架构实现的，该架构使用可训练的GloVe嵌入和结构化对话历史。该模型分别从当前话语和先前上下文中学习，然后将生成的两个表示组合起来。10折交叉验证的平均准确率为79.8%，F-评分为71.8%。
We present the results we obtained on the classification of dialogue acts in a corpus of human-human team communication in the domain of robot-assisted disaster response. We annotated dialogue acts according to the ISO 24617-2 standard scheme and carried out experiments using the FastText linear classifier as well as several neural architectures, including feed-forward, recurrent and convolutional neural models with different types of embed-dings, context and attention mechanism. The best performance was achieved with a ”Divide & Merge” architecture presented in the paper, using trainable GloVe embeddings and a structured dialogue history. This model learns from the current utterance and the preceding context separately and then combines the two generated representations. Average accuracy of 10-fold cross-validation is 79.8%, F-score 71.8%.