There has been much interest in rumor detection using deep learning models in recent years.A well-known limitation of deep learning models is that they tend to learn superficial patterns, which restricts their generalization ability.We find that this is also true for cross-topic rumor detection.In this paper, we propose a method inspired by the "mixture of experts" paradigm.We assume that the prediction of the rumor class label given an instance is dependent on the topic distribution of the instance.After deriving a vector representation for each topic, given an instance, we derive a "topic mixture" vector for the instance based on its topic distribution.This topic mixture is combined with the vector representation of the instance itself to make rumor predictions.Our experiments show that our proposed method can outperform two baseline debiasing methods in a cross-topic setting.In a synthetic setting when we removed topic-specific words, our method also works better than the baselines, showing that our method does not rely on superficial features.

Crowdfunding is an emerging online fundraising mechanism for creators to launch campaigns (projects) to solicit funds or expand their influence. Tracking the dynamics, i.e., daily funding amounts can be of great help to campaign creators as well as contributors. Previous works on this subject either fit the fluctuations of time-series with predefined stochastic process or apply a regularization term to constrain learned tendencies, resulting in limited generalization abilities. Patterns of funding-amount sequences in crowdfunding are often exclusive and non-linear, making previous predictors suboptimal. To tackle this problem, we propose a novel method based on synthesized bases which can be composed into arbitrary patterns. Concretely, we build a large set of candidate basis from which we select based on reliability, diversity and latent structures. We use representations of sequences in this basis space as a predictor, and adopt a dual-graph to exploit neighbouring information to enhance its prediction quality. Experimental results demonstrate the effectiveness of our method.

Cross-lingual representation is a technique that can both represent different languages in the same latent vector space and enable the knowledge transfer across languages. To learn such representations, most of existing works require parallel sentences with word-level alignments and assume that aligned words have similar Bag-of-Words (BoW) contexts. However, due to differences in grammar structures among different languages, the contexts of aligned words in different languages may appear at different positions of the sentence. To address this issue of different syntactics across different languages, we propose a model of bilingual word embeddings integrating syntactic dependencies (DepBiWE) by producing dependency parse-trees which encode the accurate relative positions for the contexts of aligned words. In addition, a new method is proposed to learn bilingual word embeddings from dependency-based contexts and BoW contexts jointly. Extensive experimental results on a real world dataset clearly validate the superiority of the proposed model DepBiWE on various natural language processing (NLP) tasks.