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首页> 外文会议>Annual Meeting of the Association for Computational Linguistics;International Joint Conference on Natural Language Processing >Predicting pragmatic discourse features in the language of adults with autism spectrum disorder
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Predicting pragmatic discourse features in the language of adults with autism spectrum disorder

机译：预测自闭症谱系障碍的成人语言语言的语用话语特征

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Individuals with autism spectrum disorder (ASD) experience difficulties in social aspects of communication, but the linguistic characteristics associated with deficits in discourse and pragmatic expression are often difficult to precisely identify and quantify. We are currently collecting a corpus of transcribed natural conversations produced in an experimental setting in which participants with and without ASD complete a number of collaborative tasks with their neurotypical peers. Using this dyadic conversational data, we investigate three pragmatic features - politeness, uncertainty, and informativeness - and present a dataset of utterances annotated for each of these features on a three-point scale. We then introduce ongoing work in developing and training neural models to automatically predict these features, with the goal of identifying the same between-groups differences that are observed using manual annotations. We find the best performing model for all three features is a feedforward neural network trained with BERT embeddings. Our models yield higher accuracy than ones used in previous approaches for deriving these features, with Fl exceeding 0.82 for all three pragmatic features.
机译：具有自闭症谱系障碍（ASD）的个人在沟通社会方面的困难，但与话语和语用表达的缺陷相关的语言特征通常难以精确识别和量化。我们目前正在收集在实验环境中产生的转录自然谈话的语料库，其中参与者与ASD完成了许多与其神经典型的同龄人的协同任务。使用这种二元对话数据，我们调查了三个务实的特征 - 礼貌，不确定性和信息性 - 并在三点刻度上为每个特征注释的话语数据集。然后，我们在开发和培训神经模型方面介绍持续的工作，以便自动预测这些功能，其中目标是使用手动注释观察到的组之间观察到的差异相同。我们发现所有三个功能的表现最佳模型是一种训练伯爵嵌入的前馈神经网络。我们的模型产生比以前的方法用于导出这些功能的方法的更高的准确性，为所有三种语用功能超过0.82。

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