Learning embedding layers (for classes, words, items, etc.) is a key component of lots of applications, ranging from natural language processing, recommendation systems to electronic health records, etc.However, the frequency of real-world items follows a long-tail distribution in these applications, causing naive training methods perform poorly on the rare items. A line of previous works address this problem by transferring the knowledge from the frequent items to rare items by introducing an auxiliary transfer loss. However, when defined improperly, the transfer loss may introduce harmful biases and deteriorate the performance.In this work, we propose a harmless transfer learning framework that limits the impact of the potential biases in both the definition and optimization of the transfer loss. On the definition side, we reduce the bias in transfer loss by focusing on the items to which information from high-frequency items can be efficiently transferred. On the optimization side, we leverage a lexicographic optimization framework to efficiently incorporate the information of the transfer loss without hurting the minimization of the main prediction loss function. Our method serves as a plug-in module and significantly boosts the performance on a variety of NLP and recommendation system tasks.

It has been shown that named entity recognition (NER) could benefit from incorporating the long-distance structured information captured by dependency trees. However, dependency trees built by tools usually have a certain percentage of errors. Under such circumstances, how to better use relevant structured information while ignoring irrelevant or wrong structured information from the dependency trees to improve NER performance is still a challenging research problem. In this paper, we propose the Attention and Edge-Label guided Graph Convolution Network (AELGCN) model. Then, we integrate it into BiLSTM-CRF to form BiLSTM-AELGCN-CRF model. We design an edge-aware node joint update module and introduce a node-aware edge update module to explore hidden in structured information entirely and solve the wrong dependency label information to some extent. After two modules, we apply attention-guided GCN, which automatically learns how to attend to the relevant structured information selectively. We conduct extensive experiments on several standard datasets across four languages and achieve better results than previous approaches. Through experimental analysis, it is found that our proposed model can better exploit the structured information on the dependency tree to improve the recognition of long entities.

Reasoning over Temporal Knowledge Graphs (TKGs) aims to predict future facts based on given history. One of the key challenges for prediction is to learn the evolution of facts. Most existing works focus on exploring evolutionary information in history to obtain effective temporal embeddings for entities and relations, but they ignore the variation in evolution patterns of facts, which makes them struggle to adapt to future data with different evolution patterns. Moreover, new entities continue to emerge along with the evolution of facts over time. Since existing models highly rely on historical information to learn embeddings for entities, they perform poorly on such entities with little historical information. To tackle these issues, we propose a novel Temporal Meta-learning framework for TKG reasoning, MetaTKG for brevity. Specifically, our method regards TKG prediction as many temporal meta-tasks, and utilizes the designed Temporal Meta-learner to learn evolutionary meta-knowledge from these meta-tasks. The proposed method aims to guide the backbones to learn to adapt quickly to future data and deal with entities with little historical information by the learned meta-knowledge. Specially, in temporal meta-learner, we design a Gating Integration module to adaptively establish temporal correlations between meta-tasks. Extensive experiments on four widely-used datasets and three backbones demonstrate that our method can greatly improve the performance.

In this work we addressed the problem of capturing sequential information contained in longitudinal electronic health records (EHRs). Clinical notes, which is a particular type of EHR data, are a rich source of information and practitioners often develop clever solutions how to maximise the sequential information contained in free-texts. We proposed a systematic methodology for learning from chronological events available in clinical notes. The proposed methodological path signature framework creates a non-parametric hierarchical representation of sequential events of any type and can be used as features for downstream statistical learning tasks. The methodology was developed and externally validated using the largest in the UK secondary care mental health EHR data on a specific task of predicting survival risk of patients diagnosed with Alzheimer’s disease. The signature-based model was compared to a common survival random forest model. Our results showed a 15.4% increase of risk prediction AUC at the time point of 20 months after the first admission to a specialist memory clinic and the signature method outperformed the baseline mixed-effects model by 13.2 %.

State-of-the-art NLP models can often be fooled by human-unaware transformations such as synonymous word substitution. For security reasons, it is of critical importance to develop models with certified robustness that can provably guarantee that the prediction is can not be altered by any possible synonymous word substitution. In this work, we propose a certified robust method based on a new randomized smoothing technique, which constructs a stochastic ensemble by applying random word substitutions on the input sentences, and leverage the statistical properties of the ensemble to provably certify the robustness. Our method is simple and structure-free in that it only requires the black-box queries of the model outputs, and hence can be applied to any pre-trained models (such as BERT) and any types of models (world-level or subword-level). Our method significantly outperforms recent state-of-the-art methods for certified robustness on both IMDB and Amazon text classification tasks. To the best of our knowledge, we are the first work to achieve certified robustness on large systems such as BERT with practically meaningful certified accuracy.