Conversational Help for Task Completion and Feature Discovery in Personal AssistantsThis work was presented at 2nd Workshop on Humanizing AI (HAI) at IJCAI’19 in Macao, China.
Table 2: Optimal Hyper-Parameters for various deep learning models, as tuned on the Validation Set
['Parameter', 'LSTM', 'BiLSTM', 'C-BiLSTM']
For detecting help queries, we experimented with both hand-crafted features based as well as deep learning based classifiers. We used word-based unigram and bigram features to evaluate two widely used traditional machine learning algorithms – Support Vector Machine (SVM) and Gradient Boosted Decision Trees (XGBoost). We found that XGBoost yielded slightly better recall at the cost of lower precision in classifying help queries. Among the deep learning based classifiers, we evaluated the most popular classifiers including CNN based classifier, LSTM and BiLSTM based classifiers and compared them with C-BiLSTM model for detecting help queries. We used DSSM word embedding query matrix as the input features for all the deep learning based classifiers.