Shujian Huang

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Neural probabilistic parsers are attractive for their capability of automatic feature combination and small data sizes. A transition-based greedy neural parser has given better accuracies over its linear counterpart. We propose a neural probabilistic structured-prediction model for transition-based dependency parsing, which integrates search and learning.(More)
Recommender systems (RSs) provide an effective way of alleviating the information overload problem by selecting personalized choices. Online social networks and user-generated content provide diverse sources for recommendation beyond ratings, which present opportunities as well as challenges for traditional RSs. Although social matrix factorization (Social(More)
The dominant practice of statistical machine translation (SMT) uses the same Chinese word segmentation specification in both alignment and translation rule induction steps in building Chinese-English SMT system, which may suffer from a suboptimal problem that word segmentation better for alignment is not necessarily better for translation. To tackle this,(More)
Most previous work treats the solution for pronouns and noun phrases either in two separate processes or in a single process. We argue that resolving them in two processes may result in the loss of potential useful information for each process. However, resolving them in a single process is also problematic. These two types of mentions have very different(More)
We propose a novel reranking method to extend a deterministic neural dependency parser. Different to conventional k-best reranking, the proposed model integrates search and learning by utilizing a dynamic action revising process, using the reranking model to guide modification for the base outputs and to rerank the candidates. The dynamic reranking model(More)
Recommender systems usually make personalized recommendation with user-item interaction ratings, implicit feedback and auxiliary information. Matrix factorization is the basic idea to predict a personalized ranking over a set of items for an individual user with the similarities among users and items. In this paper, we propose a novel matrix factorization(More)
In this paper, we investigate the language model (LM) adaptation issue for Statistical Machine Translation (SMT). In order to overcome the weight bias on the LM obtained from the development data, a simple but effective method is proposed to adapt the LM for diverse test datasets by employing the cross entropy of translation hypotheses as a metric to(More)
Interactive machine translation (IMT) is a method which uses human-computer interactions to improve the quality of MT. Traditional IMT methods employ a left-to-right order for the interactions, which is difficult to directly modify critical errors at the end of the sentence. In this paper, we propose an IMT framework in which the interaction is decomposed(More)
Neural parsers have benefited from automatically labeled data via dependencycontext word embeddings. We investigate training character embeddings on a word-based context in a similar way, showing that the simple method significantly improves state-of-the-art neural word segmentation models, beating tritraining baselines for leveraging autosegmented data.
Digital libraries suffer from the overload problem, which makes the researchers have to spend much time to find relevant papers. Fortunately, recommender system can help to find some relevant papers for researchers automatically according to their browsed papers. Previous paper recommendation methods are either citation-based or contentbased. In this paper,(More)