scholarly journals CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning

2020 ◽  
Vol 34 (05) ◽  
pp. 9507-9514 ◽  
Author(s):  
Daojian Zeng ◽  
Haoran Zhang ◽  
Qianying Liu
Keyword(s):  
Detailed Analysis ◽  
State Of The Art ◽  
Effective Model ◽  
Model Structure ◽  
Entity Extraction ◽  
Learning Framework ◽  
Task Learning ◽  
Current State ◽  
Significant Attention

Joint extraction of entities and relations has received significant attention due to its potential of providing higher performance for both tasks. Among existing methods, CopyRE is effective and novel, which uses a sequence-to-sequence framework and copy mechanism to directly generate the relation triplets. However, it suffers from two fatal problems. The model is extremely weak at differing the head and tail entity, resulting in inaccurate entity extraction. It also cannot predict multi-token entities (e.g. Steven Jobs). To address these problems, we give a detailed analysis of the reasons behind the inaccurate entity extraction problem, and then propose a simple but extremely effective model structure to solve this problem. In addition, we propose a multi-task learning framework equipped with copy mechanism, called CopyMTL, to allow the model to predict multi-token entities. Experiments reveal the problems of CopyRE and show that our model achieves significant improvement over the current state-of-the-art method by 9% in NYT and 16% in WebNLG (F1 score). Our code is available at https://github.com/WindChimeRan/CopyMTL

scholarly journals Learning to Incorporate Structure Knowledge for Image Inpainting

2020 ◽  
Author(s):  
Jie Yang ◽  
Zhiquan Qi ◽  
Yong Shi
Keyword(s):  
Structure Learning ◽  
State Of The Art ◽  
Image Inpainting ◽  
Image Completion ◽  
Image Structure ◽  
Learning Framework ◽  
Task Learning ◽  
Pyramid Structure ◽  
Benchmark Datasets ◽  
Structure Knowledge

This paper develops a multi-task learning framework that attempts to incorporate the image structure knowledge to assist image inpainting, which is not well explored in previous works. The primary idea is to train a shared generator to simultaneously complete the corrupted image and corresponding structures --- edge and gradient, thus implicitly encouraging the generator to exploit relevant structure knowledge while inpainting. In the meantime, we also introduce a structure embedding scheme to explicitly embed the learned structure features into the inpainting process, thus to provide possible preconditions for image completion. Specifically, a novel pyramid structure loss is proposed to supervise structure learning and embedding. Moreover, an attention mechanism is developed to further exploit the recurrent structures and patterns in the image to refine the generated structures and contents. Through multi-task learning, structure embedding besides with attention, our framework takes advantage of the structure knowledge and outperforms several state-of-the-art methods on benchmark datasets quantitatively and qualitatively.

scholarly journals Learning to Incorporate Structure Knowledge for Image Inpainting

2020 ◽  
Vol 34 (07) ◽  
pp. 12605-12612 ◽  
Author(s):  
Jie Yang ◽  
Zhiquan Qi ◽  
Yong Shi
Keyword(s):  
Structure Learning ◽  
State Of The Art ◽  
Image Inpainting ◽  
Image Completion ◽  
Image Structure ◽  
Learning Framework ◽  
Task Learning ◽  
Pyramid Structure ◽  
Benchmark Datasets ◽  
Structure Knowledge

This paper develops a multi-task learning framework that attempts to incorporate the image structure knowledge to assist image inpainting, which is not well explored in previous works. The primary idea is to train a shared generator to simultaneously complete the corrupted image and corresponding structures — edge and gradient, thus implicitly encouraging the generator to exploit relevant structure knowledge while inpainting. In the meantime, we also introduce a structure embedding scheme to explicitly embed the learned structure features into the inpainting process, thus to provide possible preconditions for image completion. Specifically, a novel pyramid structure loss is proposed to supervise structure learning and embedding. Moreover, an attention mechanism is developed to further exploit the recurrent structures and patterns in the image to refine the generated structures and contents. Through multi-task learning, structure embedding besides with attention, our framework takes advantage of the structure knowledge and outperforms several state-of-the-art methods on benchmark datasets quantitatively and qualitatively.

scholarly journals MLRDA: A Multi-Task Semi-Supervised Learning Framework for Drug-Drug Interaction Prediction

2019 ◽  
Author(s):  
Xu Chu ◽  
Yang Lin ◽  
Yasha Wang ◽  
Leye Wang ◽  
Jiangtao Wang ◽  
...  
Keyword(s):  
Supervised Learning ◽  
State Of The Art ◽  
Multiple Drug ◽  
Prediction Methods ◽  
Learning Models ◽  
Preventable Hospitalizations ◽  
Interaction Prediction ◽  
Learning Framework ◽  
Task Learning ◽  
Real World Datasets

Drug-drug interactions (DDIs) are a major cause of preventable hospitalizations and deaths. Recently, researchers in the AI community try to improve DDI prediction in two directions, incorporating multiple drug features to better model the pharmacodynamics and adopting multi-task learning to exploit associations among DDI types. However, these two directions are challenging to reconcile due to the sparse nature of the DDI labels which inflates the risk of overfitting of multi-task learning models when incorporating multiple drug features. In this paper, we propose a multi-task semi-supervised learning framework MLRDA for DDI prediction. MLRDA effectively exploits information that is beneficial for DDI prediction in unlabeled drug data by leveraging a novel unsupervised disentangling loss CuXCov. The CuXCov loss cooperates with the classification loss to disentangle the DDI prediction relevant part from the irrelevant part in a representation learnt by an autoencoder, which helps to ease the difficulty in mining useful information for DDI prediction in both labeled and unlabeled drug data. Moreover, MLRDA adopts a multi-task learning framework to exploit associations among DDI types. Experimental results on real-world datasets demonstrate that MLRDA significantly outperforms state-of-the-art DDI prediction methods by up to 10.3% in AUPR.

scholarly journals Unsupervised Learning of Monocular Depth and Ego-Motion using Conditional PatchGANs

2019 ◽  
Author(s):  
Madhu Vankadari ◽  
Swagat Kumar ◽  
Anima Majumder ◽  
Kaushik Das
Keyword(s):  
High Frequency ◽  
State Of The Art ◽  
Image Sequence ◽  
Input Image ◽  
Performance Comparison ◽  
Structural Defects ◽  
Learning Framework ◽  
Current State ◽  
Input And Output ◽  
Monocular Depth

This paper presents a new GAN-based deep learning framework for estimating absolute scale awaredepth and ego motion from monocular images using a completely unsupervised mode of learning.The proposed architecture uses two separate generators to learn the distribution of depth and posedata for a given input image sequence. The depth and pose data, thus generated, are then evaluated bya patch-based discriminator using the reconstructed image and its corresponding actual image. Thepatch-based GAN (or PatchGAN) is shown to detect high frequency local structural defects in thereconstructed image, thereby improving the accuracy of overall depth and pose estimation. Unlikeconventional GANs, the proposed architecture uses a conditioned version of input and output of thegenerator for training the whole network. The resulting framework is shown to outperform all existing deep networks in this field and beating the current state-of-the-art method by 8.7% in absoluteerror and 5.2% in RMSE metric. To the best of our knowledge, this is first deep network based modelto estimate both depth and pose simultaneously using a conditional patch-based GAN paradigm.The efficacy of the proposed approach is demonstrated through rigorous ablation studies and exhaustive performance comparison on the popular KITTI outdoor driving dataset.

scholarly journals Non-Symmetric Gyroscope Skewed Pyramids

Aerospace ◽  
2019 ◽  
Vol 6 (9) ◽  
pp. 98 ◽  
Author(s):  
Zachary Lewis ◽  
Joshua Ten Eyck ◽  
Kyle Baker ◽  
Eryn Culton ◽  
Jonathan Lang ◽  
...  
Keyword(s):  
Recent Literature ◽  
State Of The Art ◽  
Physical Space ◽  
The Novel ◽  
Figures Of Merit ◽  
Control Moment ◽  
Current State ◽  
Control Moment Gyroscopes ◽  
Continuous Surface ◽  
Significant Attention

The novel contribution in this manuscript is an expansion of the current state-of-the-art in the geometric installation of control moment gyroscopes beyond the benchmark symmetric skewed arrays and the four asymmetric arrays presented in recent literature. The benchmark pyramid symmetrically skewed at 54.73 degrees mandates significant attention to singularity avoidance, escape, and penetration, while the most recent four asymmetric arrays are strictly useful in instances where space is available to mount at least one gyro orthogonal to the others. Skewed arrays of gyros and the research-benchmark are introduced, followed by the present-day box-90 and “roof” configurations, where the roof configuration is the first prevalently used asymmetric geometry. Six other asymmetric options in the most recent literature are introduced, where four of the six options are obviously quite useful. From this inspiration, several dozen discrete options for asymmetric installations are critically evaluated using two figures of merit: maximum momentum (saturation) and maximum singularity-free momentum. Furthermore, continuous surface plots are presented to provide readers with countless (infinite) options for geometric installations. The manuscript firmly establishes many useful options for engineers who learn that the physical space on their spacecraft is insufficient to permit standard installations.

scholarly journals Learning from Easy to Complex: Adaptive Multi-Curricula Learning for Neural Dialogue Generation

2020 ◽  
Vol 34 (05) ◽  
pp. 7472-7479
Author(s):  
Hengyi Cai ◽  
Hongshen Chen ◽  
Cheng Zhang ◽  
Yonghao Song ◽  
Xiaofang Zhao ◽  
...  
Keyword(s):  
State Of The Art ◽  
Data Driven ◽  
Generation Model ◽  
Dialogue Systems ◽  
Multiple Perspectives ◽  
Learning Efficiency ◽  
Learning Framework ◽  
Current State ◽  
Efficiency And Effectiveness ◽  
Complex Adaptive

Current state-of-the-art neural dialogue systems are mainly data-driven and are trained on human-generated responses. However, due to the subjectivity and open-ended nature of human conversations, the complexity of training dialogues varies greatly. The noise and uneven complexity of query-response pairs impede the learning efficiency and effects of the neural dialogue generation models. What is more, so far, there are no unified dialogue complexity measurements, and the dialogue complexity embodies multiple aspects of attributes—specificity, repetitiveness, relevance, etc. Inspired by human behaviors of learning to converse, where children learn from easy dialogues to complex ones and dynamically adjust their learning progress, in this paper, we first analyze five dialogue attributes to measure the dialogue complexity in multiple perspectives on three publicly available corpora. Then, we propose an adaptive multi-curricula learning framework to schedule a committee of the organized curricula. The framework is established upon the reinforcement learning paradigm, which automatically chooses different curricula at the evolving learning process according to the learning status of the neural dialogue generation model. Extensive experiments conducted on five state-of-the-art models demonstrate its learning efficiency and effectiveness with respect to 13 automatic evaluation metrics and human judgments.

scholarly journals No AI Is an Island: The Case for Teaming Intelligence

AI Magazine ◽  
2019 ◽  
Vol 40 (1) ◽  
pp. 16-28 ◽  
Author(s):  
Matthew Johnson ◽  
Alonso Vera
Keyword(s):  
Crucial Role ◽  
Intelligent Systems ◽  
State Of The Art ◽  
General Structure ◽  
Technological System ◽  
Full Potential ◽  
Current State ◽  
The World ◽  
Collaborative Skills ◽  
Significant Attention

The purpose of this article is to draw attention to an aspect of intelligence that has not yet received significant attention from the AI community, but that plays a crucial role in a technology’s effectiveness in the world, namely teaming intelligence. We propose that Al will reach its full potential only if, as part of its intelligence, it also has enough teaming intelligence to work well with people. Although seemingly counterintuitive, the more intelligent the technological system, the greater the need for collaborative skills. This paper will argue why teaming intelligence is important to AI, provide a general structure for AI researchers to use in developing intelligent systems that team well, assess the current state of the art and, in doing so, suggest a path forward for future AI systems. This is not a call to develop a new capability, but rather, an approach to what AI capabilities should be built, and how, so as to imbue intelligent systems with teaming competence.

scholarly journals Neural Simile Recognition with Cyclic Multitask Learning and Local Attention

2020 ◽  
Vol 34 (05) ◽  
pp. 9515-9522 ◽  
Author(s):  
Jiali Zeng ◽  
Linfeng Song ◽  
Jinsong Su ◽  
Jun Xie ◽  
Wei Song ◽  
...  
Keyword(s):  
Recent Work ◽  
State Of The Art ◽  
Source Code ◽  
Multitask Learning ◽  
Learning Framework ◽  
Current State ◽  
Sentence Classification ◽  
Component Extraction ◽  
Simple Parameter ◽  
Parameter Sharing

Simile recognition is to detect simile sentences and to extract simile components, i.e., tenors and vehicles. It involves two subtasks: simile sentence classification and simile component extraction. Recent work has shown that standard multitask learning is effective for Chinese simile recognition, but it is still uncertain whether the mutual effects between the subtasks have been well captured by simple parameter sharing. We propose a novel cyclic multitask learning framework for neural simile recognition, which stacks the subtasks and makes them into a loop by connecting the last to the first. It iteratively performs each subtask, taking the outputs of the previous subtask as additional inputs to the current one, so that the interdependence between the subtasks can be better explored. Extensive experiments show that our framework significantly outperforms the current state-of-the-art model and our carefully designed baselines, and the gains are still remarkable using BERT. Source Code of this paper are available on https://github.com/DeepLearnXMU/Cyclic.

Behavioral Genetics: Concepts for Research and Practice in Language Development and Disorders

1995 ◽  
Vol 38 (5) ◽  
pp. 1126-1142 ◽  
Author(s):  
Jeffrey W. Gilger
Keyword(s):  
Language Development ◽  
Behavioral Genetics ◽  
State Of The Art ◽  
Genetic Research ◽  
Great Promise ◽  
Behavioral Genetic ◽  
Fine Grained ◽  
Future Goals ◽  
Current State ◽  
Research Designs

This paper is an introduction to behavioral genetics for researchers and practioners in language development and disorders. The specific aims are to illustrate some essential concepts and to show how behavioral genetic research can be applied to the language sciences. Past genetic research on language-related traits has tended to focus on simple etiology (i.e., the heritability or familiality of language skills). The current state of the art, however, suggests that great promise lies in addressing more complex questions through behavioral genetic paradigms. In terms of future goals it is suggested that: (a) more behavioral genetic work of all types should be done—including replications and expansions of preliminary studies already in print; (b) work should focus on fine-grained, theory-based phenotypes with research designs that can address complex questions in language development; and (c) work in this area should utilize a variety of samples and methods (e.g., twin and family samples, heritability and segregation analyses, linkage and association tests, etc.).

Schizophrenia Research: Current State of the Art

1976 ◽  
Vol 21 (7) ◽  
pp. 497-498
Author(s):  
STANLEY GRAND
Keyword(s):  
State Of The Art ◽  
Current State
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