Compatibility between items, such as clothes and shoes, is a major factor among customer's purchasing decisions. However, learning "compatibility" is challenging due to (1) broader notions of compatibility than those of similarity, (2) the asymmetric nature of compatibility, and (3) only a small set of compatible and incompatible items are observed. We propose an end-to-end trainable system to embed each item into a latent vector and project a query item into K compatible prototypes in the same space. These prototypes reflect the broad notions of compatibility. We refer to both the embedding and prototypes as "Compatibility Family." In our learned space, we introduce a novel Projected Compatibility Distance (PCD) function which is differentiable and ensures diversity by aiming for at least one prototype to be close to a compatible item, whereas none of the prototypes are close to an incompatible item. We evaluate our system on a toy dataset, two Amazon product datasets, and Polyvore outfit dataset. Our method consistently achieves state-of-the-art performance. Finally, we show that we can visualize the candidate compatible prototypes using a Metric-regularized Conditional Generative Adversarial Network (MrCGAN), where the input is a projected prototype and the output is a generated image of a compatible item. We ask human evaluators to judge the relative compatibility between our generated images and images generated by CGANs conditioned directly on query items. Our generated images are significantly preferred, with roughly twice the number of votes as others.

In this paper, we investigate four important issues together for explicit discourse relation labelling in Chinese texts: (1) discourse connective extraction, (2) linking ambiguity resolution, (3) relation type disambiguation, and (4) argument boundary identification. In a pipelined Chinese discourse parser, we identify potential connective candidates by string matching, eliminate non-discourse usages from them with a binary classifier, resolve linking ambiguities among connective components by ranking, disambiguate relation types by a multiway classifier, and determine the argument boundaries by conditional random fields. The experiments on Chinese Discourse Treebank show that the F1 scores of 0.7506, 0.7693, 0.7458, and 0.3134 are achieved for discourse usage disambiguation, linking disambiguation, relation type disambiguation, and argument boundary identification, respectively, in a pipelined Chinese discourse parser.

In this paper, we present a question-answering (QA) system as a virtual tutor for students in the 5th and 6th grades. Students ask questions and the QA system gives answers to their questions based on a knowledge base. Teaching materials for history and geography are considered as a knowledge source. Because question log is not available in developing QA systems, multiple choice questions (MCQs) in the learning and testing materials are regarded as a training corpus to learn question types, answer types and keywords for retrieval, where an MCQ consists of a stem and a set of options. Options from the same MCQ are grouped into a cluster. Clusters with common elements are merged into a larger cluster. A cluster is labelled with a nominal element selected from the corresponding stems. We also mine question patterns from the stems for question type analysis in the QA system. Because the questions created by instructors in MCQs and the questions asked by students may be different, we develop a procedure to collect possible questions from students in the 6th grade. In the experiments, we first evaluate the question type classification systems using the MCQ corpus and the student corpus with 5-fold cross validation, respectively. Then we train question type classifiers with the complete MCQ corpus, and test them on the student corpus. The student’s and the instructor’s questions are compared and analyzed.