智能虛拟助手(IVA)通過語音分類(SUC)實作呼叫路由中的輕松會話，SUC是一種特殊的口語了解形式。建構一個SUC系統需要大量的監督域内資料，這些資料并不總是可用的。在本文中，我們引入了一種無監督口語話語分類方法(USUC)，該方法不需要任何域内資料，除了意圖示簽和每個意圖的幾個段落短語。USUC由一個KNN分類器(K=1)和一個在大量無監督客戶服務語料庫上訓練的複雜嵌入模型組成。在所有的嵌入模型中，我們證明Elmo在USUC中是最有效的。但是，Elmo模型太慢了，不能在運作時用于調用路由。為了解決這一問題，我們首先離線計算單gram和雙gram的嵌入向量，并建立n個gram及其對應的嵌入向量的查找表。然後，我們使用這個表在運作時計算句子嵌入向量，以及不可見的n-gram的後退技術。實驗結果表明，在不需要監督資料的情況下，USUC将分類錯誤率從32.9%降低到27.0%，優于傳統的話語分類方法。此外，我們的查找和後退技術将處理速度從每秒16個話語提高到每秒118個話語。
原文标題：Unsupervised spoken utterance classification
原文内容：Intelligent virtual assistant (IVA) enables effortless conversations in call routing through spoken utterance classification (SUC) which is a special form of spoken language understanding (SLU). Building an SUC system requires a large amount of supervised in-domain data that is not always available. In this paper, we introduce an unsupervised spoken utterance classification approach (USUC) that does not require any in-domain data except for the intent labels and a few para-phrases per intent. USUC is consisting of a KNN classifier (K=1) and a complex embedding model trained on a large amount of unsupervised customer service corpus. Among all embedding models, we demonstrate that Elmo works best for USUC. However, an Elmo model is too slow to be used at run-time for call routing. To resolve this issue, first we compute the uni- and bi-gram embedding vectors offline and we build a lookup table of n-grams and their corresponding embedding vector. Then we use this table to compute sentence embedding vectors at run-time, along with back-off techniques for unseen n-grams. Experiments show that USUC outperforms the traditional utterance classification methods by reducing the classification error rate from 32.9% to 27.0% without requiring supervised data. Moreover, our lookup and back-off technique increases the processing speed from 16 utterance per second to 118 utterance per second.
自由式會話分類.pdf