Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme

Joint extraction of entities and relations is an important task in information extraction. To tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem. Then, based on our tagging scheme, we study different end-to-end models to extract entities and their relations directly, without identifying entities and relations separately. We conduct experiments on a public dataset produced by distant supervision method and the experimental results show that the tagging based methods are better than most of the existing pipelined and joint learning methods. What's more, the end-to-end model proposed in this paper, achieves the best results on the public dataset

TAG : Type Auxiliary Guiding for Code Comment Generation

Existing leading code comment generation approaches with the structure-to-sequence framework ignores the type information of the interpretation of the code, e.g., operator, string, etc. However, introducing the type information into the existing framework is non-trivial due to the hierarchical dependence among the type information. In order to address the issues above, we propose a Type Auxiliary Guiding encoder-decoder framework for the code comment generation task which considers the source code as an N-ary tree with type information associated with each node. Specifically, our framework is featured with a Type-associated Encoder and a Type-restricted Decoder which enables adaptive summarization of the source code. We further propose a hierarchical reinforcement learning method to resolve the training difficulties of our proposed framework. Extensive evaluations demonstrate the state-of-the-art performance of our framework with both the auto-evaluated metrics and case studies.Comment: ACL 2020, Accepte

Implementation of Lightweight Convolutional Neural Networks via Layer-Wise Differentiable Compression

Convolutional neural networks (CNNs) have achieved significant breakthroughs in various domains, such as natural language processing (NLP), and computer vision. However, performance improvement is often accompanied by large model size and computation costs, which make it not suitable for resource-constrained devices. Consequently, there is an urgent need to compress CNNs, so as to reduce model size and computation costs. This paper proposes a layer-wise differentiable compression (LWDC) algorithm for compressing CNNs structurally. A differentiable selection operator OS is embedded in the model to compress and train the model simultaneously by gradient descent in one go. Instead of pruning parameters from redundant operators by contrast to most of the existing methods, our method replaces the original bulky operators with more lightweight ones directly, which only needs to specify the set of lightweight operators and the regularization factor in advance, rather than the compression rate for each layer. The compressed model produced by our method is generic and does not need any special hardware/software support. Experimental results on CIFAR-10, CIFAR-100 and ImageNet have demonstrated the effectiveness of our method. LWDC obtains more significant compression than state-of-the-art methods in most cases, while having lower performance degradation. The impact of lightweight operators and regularization factor on the compression rate and accuracy also is evaluated

Attention Round for Post-Training Quantization

At present, the quantification methods of neural network models are mainly divided into post-training quantization (PTQ) and quantization aware training (QAT). Post-training quantization only need a small part of the data to complete the quantification process, but the performance of its quantitative model is not as good as the quantization aware training. This paper presents a novel quantification method called Attention Round. This method gives parameters w the opportunity to be mapped to all possible quantized values, rather than just the two quantized values nearby w in the process of quantization. The probability of being mapped to different quantified values is negatively correlated with the distance between the quantified values and w, and decay with a Gaussian function. In addition, this paper uses the lossy coding length as a measure to assign bit widths to the different layers of the model to solve the problem of mixed precision quantization, which effectively avoids to solve combinatorial optimization problem. This paper also performs quantitative experiments on different models, the results confirm the effectiveness of the proposed method. For ResNet18 and MobileNetV2, the post-training quantization proposed in this paper only require 1,024 training data and 10 minutes to complete the quantization process, which can achieve quantization performance on par with quantization aware training.Comment: 18 pages, 5 figures, 5 table

LA-ICP-MS U-Pb geochronology and clumped isotope constraints on the formation and evolution of an ancient dolomite reservoir: The Middle Permian of northwest Sichuan Basin (SW China)

Recent advances in laser-ablation inductively coupled-plasma mass spectrometry (LA-ICP-MS) in-situ U-Pb radiometric dating and clumped isotope thermometry (Δ) of carbonate minerals provide potential for refining the fluid flow and diagenetic history of carbonate successions. In this study, the Middle Permian dolomites proximal to the Longmenshan fold and thrust belt in northwest Sichuan Basin, southwest China, were investigated using combined U-Pb geochronology, clumped isotope thermometry, and routine isotopic (δC, δO and Sr/Sr) and elemental geochemistry, in an attempt to reveal the possible relevance of carbonate diagenesis and porosity evolution to the basin-scale tectonic/fluid flow events in the framework of absolute time. Overall, formation and evolution of these dolomites temporally correlated well with major tectonic episodes of the Longmenshan fold and thrust belt. Contrary to the previously-assumed volcanic-related model, U-Pb dating and Δ analyses suggest a mid- to late-Triassic replacive dolomitization event (U-Pb ages of 240 ± 12 Ma to 233.8 ± 6.4 Ma) by a hot (Δ temperatures 88– 104 °C) basinal brine, which was likely driven by thrust-related compression of the Longmenshan fold and thrust belt during the Triassic. Replacive dolomitization was immediately followed by cementation of euhedral dolomites (U-Pb age of 228 ± 10 Ma) and blocky calcites (U-Pb ages of 224.8 ± 1.8 Ma to 213.4 ± 3.3 Ma) precipitated from the basinal brine in the same tectonic regime. Afterwards, there was long-term cessation of diagenesis that was coincident with, and thus may well be attributed to, tectono-thermal quiescence during most of the Mesozoic Era and Paleogene. Finally, saddle dolomite cements yield Miocene ages (U-Pb ages of 16.40 ± 0.74 Ma to 12.3 ± 1.2 Ma) and precipitated in pre-existing vugs, representing a hydrothermal fluid flow event (Δ temperatures up to 170 °C) during the reactivation of thrusting. In addition, similarities in U-Pb ages and geochemical signatures between host rocks (replacive dolomites) and the vug-lining dolomite cements suggest that most porosity in these dolomite reservoirs was inherited from the precursor limestones, which probably experienced meteoric leaching during late Permian uplift. This study highlights that there are geodynamic controls on fluid flow and consequent diagenetic evolution of carbonates in tectonically active sedimentary basins. It illustrates that burial alone in this setting is not a sufficient driver for diagenetic alteration and porosity reduction. The combination of U-Pb radiometric dating, clumped isotope thermometry and routine geochemical analyses is a useful approach in refining the diagenetic and porosity evolution history of ancient carbonate successions