泛自閉症研究指出，由於口語表達能力的遲緩，跟典型孩童相比，自閉症孩童較無法敘說一個流暢的故事，因此在診斷自閉症孩童時，衡量其口語表達流暢度以成為一個診斷的重要指標。然而流暢度的評量，不是需要費時的人工標註，就是得用專家專門設計出的特徵來當指標，因此，本篇研究提出一種自動化直接資料導向的流暢度特徵學習架構，利用長短期記憶模型的遺忘閘導出語意流暢度的特徵，同時我們也利用自閉症觀察診斷量表中的評分細項來測試我們的流暢度特徵，結果上，利用我們提出的語意流暢度特徵來辨識自閉症小孩與典型小孩的任務上能夠達到92%的高準確率，對照傳統上使用語法、語詞使用頻率、潛在語義模型分析等地模型有顯著的提升。這篇論文也進一步隨機打亂字序及句子順序，使典型小孩說故事內容變得不流暢的方式，來驗證我們提出的流暢度的意義，降維並將資料樣本可視化分析後證明我們的提取的特徵含有流暢度的概念。

Autistic children are less able to tell a fluent story than typical children, so measuring verbal fluency becomes an important indicator when diagnosing autistic children. Fluency assessment, however, needs time-consuming manual tagging, or using expert specially designed characteristics as indicators, therefore, this study proposes a coherence representation learned by directly data-driven architecture, using the forget gate of long short-term memory model to export lexical coherence representation, at the same time, we also use the ADOS coding related to the evaluation of narration to test our proposed representation. Our proposed lexical coherence representation performs high accuracy of 92% on the task of identifying children with autism from typically development. Comparing with the traditional measurement of grammar, word frequency, and latent semantic analysis model, there is a significant improvement. This paper also further randomly shuffles the word order and sentence order, making the typical child's story content become disfluent. By visualizing the data samples after dimension reduction, we further observe the distribution of these fluent, disfluent, and those artificially disfluent data samples. We found the artificially disfluent typical samples would move closer to disfluent autistic samples which prove that our extracted features contain the concept of coherency.