Word embeddings have been widely used and proven to be effective in many natural language processing and text modeling tasks. It is obvious that one ambiguous word could have very different semantics in various contexts, which is called polysemy. Most existing works aim at generating only one single embedding for each word while a few works build a limited number of embeddings to present different meanings for each word. However, it is hard to determine the exact number of senses for each word as the word meaning is dependent on contexts. To address this problem, we propose a novel Adaptive Probabilistic Word Embedding (APWE) model, where the word polysemy is defined over a latent interpretable semantic space. Specifically, at first each word is represented by an embedding in the latent semantic space and then based on the proposed APWE model, the word embedding can be adaptively adjusted and updated based on different contexts to obtain the tailored word embedding. Empirical comparisons with state-of-the-art models demonstrate the superiority of the proposed APWE model.

It is difficult to train a personalized task-oriented dialogue system because the data collected from each individual is often insufficient. Personalized dialogue systems trained on a small dataset is likely to overfit and make it difficult to adapt to different user needs. One way to solve this problem is to consider a collection of multiple users as a source domain and an individual user as a target domain, and to perform transfer learning from the source domain to the target domain. By following this idea, we propose a PErsonalized Task-oriented diALogue (PETAL) system, a transfer reinforcement learning framework based on POMDP, to construct a personalized dialogue system. The PETAL system first learns common dialogue knowledge from the source domain and then adapts this knowledge to the target domain. The proposed PETAL system can avoid the negative transfer problem by considering differences between the source and target users in a personalized Q-function. Experimental results on a real-world coffee-shopping data and simulation data show that the proposed PETAL system can learn optimal policies for different users, and thus effectively improve the dialogue quality under the personalized setting.

Crowdsourcing is an effective method for collecting labeled data for various data mining tasks. It is critical to ensure the veracity of the produced data because responses collected from different users may be noisy and unreliable. Previous works solve this veracity problem by estimating both the user ability and question difficulty based on the knowledge in each task individually. In this case, each single task needs large amounts of data to provide accurate estimations. However, in practice, budgets provided by customers for a given target task may be limited, and hence each question can be presented to only a few users where each user can answer only a few questions. This data sparsity problem can cause previous approaches to perform poorly due to the overfitting problem on rare data and eventually damage the data veracity. Fortunately, in real-world applications, users can answer questions from multiple historical tasks. For example, one can annotate images as well as label the sentiment of a given title. In this paper, we employ transfer learning, which borrows knowledge from auxiliary historical tasks to improve the data veracity in a given target task. The motivation is that users have stable characteristics across different crowdsourcing tasks and thus data from different tasks can be exploited collectively to estimate users' abilities in the target task. We propose a hierarchical Bayesian model, TLC (Transfer Learning for Crowdsourcing), to implement this idea by considering the overlapping users as a bridge. In addition, to avoid possible negative impact, TLC introduces task-specific factors to model task differences. The experimental results show that TLC significantly improves the accuracy over several state-of-the-art non-transfer-learning approaches under very limited budget in various labeling tasks.