Conventional interactive machine translation typically requires a human translator to validate every generated target word, even though most of them are correct in the advanced neural machine translation (NMT) scenario. Previous studies have exploited confidence approaches to address the intensive human involvement issue, which request human guidance only for a few number of words with low confidences. However, such approaches do not take the history of human involvement into account, and optimize the models only for the translation quality while ignoring the cost of human involvement. In response to these pitfalls, we propose a novel interactive NMT model, which explicitly accounts the history of human involvements and particularly is optimized towards two objectives corresponding to the translation quality and the cost of human involvement, respectively. Specifically, the model jointly predicts a target word and a decision on whether to request human guidance, which is based on both the partial translation and the history of human involvements. Since there is no explicit signals on the decisions of requesting human guidance in the bilingual corpus, we optimize the model with the reinforcement learning technique which enables our model to accurately predict when to request human guidance. Simulated and real experiments show that the proposed model can achieve higher translation quality with similar or less human involvement over the confidence-based baseline.

In online karaoke, the decision process in choosing a song is different from that in music radio, because users usually prefer songs that meet their vocal competence besides their tastes. Traditional music recommendation methods typically model users' personalized preference for songs in terms of content and style. However, this can be improved by considering the degree of matching the vocal competence (e.g. pitch, volume, and rhythm) of users to the vocal requirements of songs. To this end, in this paper, we develop a karaoke recommender system by incorporating vocal competence. Along this line, we propose a joint modeling method named CBNTF by exploiting the mutual enhancement between non-negative tensor factorization (NTF) and support vector machine (SVM). Specifically, we first extract vocal (i.e., pitch, volume, and rhythm) ratings of a user for a song from his/her singing records. Since these vocal ratings encode users' vocal competence from three aspects, we treat these vocal ratings as a tensor, exploit an NTF method, and learn the latent features of users' vocal metrics. These factorized features are simultaneously fed into an SVM classifier and then we use the trained classifier to predict the overall rating of a user with respect to a song. In addition, we propose an enhanced objective function to exploit the mutual enhancement between NTF and SVM, and devise an effective method to solve this objective as a coupled least-squares optimization problem via a maximum margin framework. With the estimated model, we compute the similarity between users and songs in terms of pitch, volume and rhythm and recommend songs to users. Finally, we conduct extensive experiments with real-world online karaoke data. The results demonstrate the effectiveness of our method.