Tagging based relational triple extraction methods are attracting growing research attention recently. However, most of these methods take a unidirectional extraction framework that first extracts all subjects and then extracts objects and relations simultaneously based on the subjects extracted. This framework has an obvious deficiency that it is too sensitive to the extraction results of subjects. To overcome this deficiency, we propose a bidirectional extraction framework based method that extracts triples based on the entity pairs extracted from two complementary directions. Concretely, we first extract all possible subject-object pairs from two paralleled directions. These two extraction directions are connected by a shared encoder component, thus the extraction features from one direction can flow to another direction and vice versa. By this way, the extractions of two directions can boost and complement each other. Next, we assign all possible relations for each entity pair by a biaffine model. During training, we observe that the share structure will lead to a convergence rate inconsistency issue which is harmful to performance. So we propose a share-aware learning mechanism to address it. We evaluate the proposed model on multiple benchmark datasets. Extensive experimental results show that the proposed model is very effective and it achieves state-of-the-art results on all of these datasets. Moreover, experiments show that both the proposed bidirectional extraction framework and the share-aware learning mechanism have good adaptability and can be used to improve the performance of other tagging based methods. The source code of our work is available at: https://github.com/neukg/BiRTE.

Tagging based methods are one of the mainstream methods in relational triple extraction. However, most of them suffer from the class imbalance issue greatly. Here we propose a novel tagging based model that addresses this issue from following two aspects. First, at the model level, we propose a three-step extraction framework that can reduce the total number of samples greatly, which implicitly decreases the severity of the mentioned issue. Second, at the intra-model level, we propose a confidence threshold based cross entropy loss that can directly neglect some samples in the major classes. We evaluate the proposed model on NYT and WebNLG. Extensive experiments show that it can address the mentioned issue effectively and achieves state-of-the-art results on both datasets. The source code of our model is available at: https://github.com/neukg/ConCasRTE.

Neural conversation models have shown great potentials towards generating fluent and informative responses by introducing external background knowledge.Nevertheless, it is laborious to construct such knowledge-grounded dialogues, and existing models usually perform poorly when transfer to new domains with limited training samples.Therefore, building a knowledge-grounded dialogue system under the low-resource setting is a still crucial issue.In this paper, we propose a novel threestage learning framework based on weakly supervised learning which benefits from large scale ungrounded dialogues and unstructured knowledge base.To better cooperate with this framework, we devise a variant of Transformer with decoupled decoder which facilitates the disentangled learning of response generation and knowledge incorporation.Evaluation results on two benchmarks indicate that our approach can outperform other state-of-the-art methods with less training data, and even in zero-resource scenario, our approach still performs well.

Table filling based relational triple extraction methods are attracting growing research interests due to their promising performance and their abilities on extracting triples from complex sentences.However, this kind of methods are far from their full potential because most of them only focus on using local features but ignore the global associations of relations and of token pairs, which increases the possibility of overlooking some important information during triple extraction.To overcome this deficiency, we propose a global feature-oriented triple extraction model that makes full use of the mentioned two kinds of global associations.Specifically, we first generate a table feature for each relation.Then two kinds of global associations are mined from the generated table features.Next, the mined global associations are integrated into the table feature of each relation.This "generate-mine-integrate" process is performed multiple times so that the table feature of each relation is refined step by step.Finally, each relation's table is filled based on its refined table feature, and all triples linked to this relation are extracted based on its filled table.We evaluate the proposed model on three benchmark datasets.Experimental results show our model is effective and it achieves state-of-the-art results on all of these datasets.The source code of our work is available at: https://github.com/neukg/GRTE.

Biomedical event extraction aims to detect fine-grained interactions between biomedical entities in biomedical texts, and has become a research hotspot for researchers in the biomedical field. As the first step in biomedical event extraction, biomedical event trigger identification plays an important role in the whole process. Although researchers have proposed many methods, the performance of existing methods is not desirable due to the reliance on a large number of labeled training samples and the need for expert knowledge in the biomedical field. To treat this issue, we model the biomedical event trigger identification as a few-shot learning problem. Specifically, we utilize a knowledge-aware attention layer to obtain a rich informative representation for entities, and improve the derived prototypes accordingly by prototypical network. In addition, the module of relation network is introduced to train a more reasonable distance function for trigger type prediction. The results demonstrate the effectiveness of the proposed framework according to F1-score.

Energy-efficient countermeasures to side-channel attacks are required for Internet of Things hardware. This paper proposes a special hiding technique for the substitution operation in block ciphers, which equalizes the power consumption of a circuit by appropriate feedforward compensation and is called power-aware hiding (PAH). A hybrid application configuration, in which PAH is applied to the S-boxes while the left linear operations are protected with a general masking method, is proposed as well. This solution not only has higher energy efficiency but can also be implemented automatically in a semicustom manner. The Advanced Encryption Standard VLSI adopting this solution was implemented and manufactured in 180-nm technology as a demonstration. The implementation issues regarding the countermeasures are discussed in this paper. Testing shows that the chip has a throughput up to 1.175 Gb/s with 18.1-mW power consumption and its number of measurements to disclosure is 13.4 million.