scholarly journals Copy or Rewrite: Hybrid Summarization with Hierarchical Reinforcement Learning

2020 ◽  
Vol 34 (05) ◽  
pp. 9306-9313
Author(s):  
Liqiang Xiao ◽  
Lu Wang ◽  
Hao He ◽  
Yaohui Jin
Keyword(s):  
Reinforcement Learning ◽  
Information Loss ◽  
The State ◽  
Automatic Evaluation ◽  
Hierarchical Reinforcement Learning ◽  
Human Evaluation ◽  
The Arts ◽  
Hybrid Framework ◽  
End To End ◽  
Abstractive Summarization

Jointly using the extractive and abstractive summarization methods can combine their complementary advantages, generating both informative and concise summary. Existing methods that adopt an extract-then-abstract strategy have achieved impressive results, yet they suffer from the information loss in the abstraction step because they compress all the selected sentences without distinguish. Especially when the whole sentence is summary-worthy, salient content would be lost by compression. To address this problem, we propose HySum, a hybrid framework for summarization that can flexibly switch between copying sentence and rewriting sentence according to the degree of redundancy. In this way, our approach can effectively combine the advantages of two branches of summarization, juggling informativity and conciseness. Moreover, we based on Hierarchical Reinforcement Learning, propose an end-to-end reinforcing method to bridge together the extraction module and rewriting module, which can enhance the cooperation between them. Automatic evaluation shows that our approach significantly outperforms the state-of-the-arts on the CNN/DailyMail corpus. Human evaluation also demonstrates that our generated summaries are more informative and concise than popular models.

scholarly journals Devil in the Details: Towards Accurate Single and Multiple Human Parsing

2019 ◽  
Vol 33 ◽  
pp. 4814-4821 ◽  
Author(s):  
Tao Ruan ◽  
Ting Liu ◽  
Zilong Huang ◽  
Yunchao Wei ◽  
Shikui Wei ◽  
...  
Keyword(s):  
The State ◽  
Future Research ◽  
Context Information ◽  
Global Context ◽  
The Arts ◽  
End To End ◽  
Feature Resolution

Human parsing has received considerable interest due to its wide application potentials. Nevertheless, it is still unclear how to develop an accurate human parsing system in an efficient and elegant way. In this paper, we identify several useful properties, including feature resolution, global context information and edge details, and perform rigorous analyses to reveal how to leverage them to benefit the human parsing task. The advantages of these useful properties finally result in a simple yet effective Context Embedding with Edge Perceiving (CE2P) framework for single human parsing. Our CE2P is end-to-end trainable and can be easily adopted for conducting multiple human parsing. Benefiting the superiority of CE2P, we won the 1st places on all three human parsing tracks in the 2nd Look into Person (LIP) Challenge. Without any bells and whistles, we achieved 56.50% (mIoU), 45.31% (mean APr) and 33.34% (APp0.5) in Track 1, Track 2 and Track 5, which outperform the state-of-the-arts more than 2.06%, 3.81% and 1.87%, respectively. We hope our CE2P will serve as a solid baseline and help ease future research in single/multiple human parsing. Code has been made available at https://github.com/liutinglt/CE2P.

scholarly journals Diversity-Driven Extensible Hierarchical Reinforcement Learning

2019 ◽  
Vol 33 ◽  
pp. 4992-4999 ◽  
Author(s):  
Yuhang Song ◽  
Jianyi Wang ◽  
Thomas Lukasiewicz ◽  
Zhenghua Xu ◽  
Mai Xu
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
State Of The Art ◽  
Experimental Studies ◽  
The State ◽  
Transferable Skills ◽  
Hierarchical Reinforcement Learning ◽  
Multiple Levels

Hierarchical reinforcement learning (HRL) has recently shown promising advances on speeding up learning, improving the exploration, and discovering intertask transferable skills. Most recent works focus on HRL with two levels, i.e., a master policy manipulates subpolicies, which in turn manipulate primitive actions. However, HRL with multiple levels is usually needed in many real-world scenarios, whose ultimate goals are highly abstract, while their actions are very primitive. Therefore, in this paper, we propose a diversitydriven extensible HRL (DEHRL), where an extensible and scalable framework is built and learned levelwise to realize HRL with multiple levels. DEHRL follows a popular assumption: diverse subpolicies are useful, i.e., subpolicies are believed to be more useful if they are more diverse. However, existing implementations of this diversity assumption usually have their own drawbacks, which makes them inapplicable to HRL with multiple levels. Consequently, we further propose a novel diversity-driven solution to achieve this assumption in DEHRL. Experimental studies evaluate DEHRL with nine baselines from four perspectives in two domains; the results show that DEHRL outperforms the state-of-the-art baselines in all four aspects.

scholarly journals Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning

2019 ◽  
Vol 33 ◽  
pp. 2547-2554 ◽  
Author(s):  
Ziyu Yao ◽  
Xiujun Li ◽  
Jianfeng Gao ◽  
Brian Sadler ◽  
Huan Sun
Keyword(s):  
Reinforcement Learning ◽  
Semantic Parsing ◽  
Rule Based ◽  
Important Type ◽  
Hierarchical Reinforcement Learning ◽  
Human Evaluation ◽  
Correct Program

Given a text description, most existing semantic parsers synthesize a program in one shot. However, it is quite challenging to produce a correct program solely based on the description, which in reality is often ambiguous or incomplete. In this paper, we investigate interactive semantic parsing, where the agent can ask the user clarification questions to resolve ambiguities via a multi-turn dialogue, on an important type of programs called “If-Then recipes.” We develop a hierarchical reinforcement learning (HRL) based agent that significantly improves the parsing performance with minimal questions to the user. Results under both simulation and human evaluation show that our agent substantially outperforms non-interactive semantic parsers and rule-based agents.1

Digital economy: conceptual and strategic aspects of development

2020 ◽  
pp. 40-52
Author(s):  
D. Volkov
Keyword(s):  
Public Administration ◽  
Digital Technologies ◽  
Digital Economy ◽  
The State ◽  
Long Wave ◽  
End To End ◽  
Advanced Development ◽  
Digital Sphere

The article proves the need to "return" the state to the economy in order to implement digital mobilization and form a new mechanism of public administration, including the article analyzes the key conditions for Russia’s transition to the path of "advanced development", reveals not only the content of the levels of the digital sphere, but also its end-to-end digital technologies, all the challenges and threats generated by the development of the digital economy, examines the need and possibility of Russia’s movement to the sixth technological order, provides an algorithm for the transition to the phase of a new long wave (the big or Kondratiev cycle).

Location- and Person-Independent Activity Recognition with WiFi, Deep Neural Networks, and Reinforcement Learning

2021 ◽  
Vol 2 (1) ◽  
pp. 1-25
Author(s):  
Yongsen Ma ◽  
Sheheryar Arshad ◽  
Swetha Muniraju ◽  
Eric Torkildson ◽  
Enrico Rantala ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Reinforcement Learning ◽  
Activity Recognition ◽  
Deep Neural Networks ◽  
State Machine ◽  
Recognition Algorithm ◽  
The State ◽  
Neural Architecture ◽  
Learning Agent

In recent years, Channel State Information (CSI) measured by WiFi is widely used for human activity recognition. In this article, we propose a deep learning design for location- and person-independent activity recognition with WiFi. The proposed design consists of three Deep Neural Networks (DNNs): a 2D Convolutional Neural Network (CNN) as the recognition algorithm, a 1D CNN as the state machine, and a reinforcement learning agent for neural architecture search. The recognition algorithm learns location- and person-independent features from different perspectives of CSI data. The state machine learns temporal dependency information from history classification results. The reinforcement learning agent optimizes the neural architecture of the recognition algorithm using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). The proposed design is evaluated in a lab environment with different WiFi device locations, antenna orientations, sitting/standing/walking locations/orientations, and multiple persons. The proposed design has 97% average accuracy when testing devices and persons are not seen during training. The proposed design is also evaluated by two public datasets with accuracy of 80% and 83%. The proposed design needs very little human efforts for ground truth labeling, feature engineering, signal processing, and tuning of learning parameters and hyperparameters.

scholarly journals Automatische Evaluation der Humanübersetzung: BLEU vs. METEOR

2020 ◽  
Vol 65 (1) ◽  
pp. 181-205
Author(s):  
Hye-Yeon Chung
Keyword(s):  
Automatic Evaluation ◽  
Human Evaluation ◽  
The Individual

AbstractHuman evaluation (HE) of translation is generally considered to be valid, but it requires a lot of effort. Automatic evaluation (AE) which assesses the quality of machine translations can be done easily, but it still requires validation. This study addresses the questions of whether and how AE can be used for human translations. For this purpose AE formulas and HE criteria were compared to each other in order to examine the validity of AE. In the empirical part of the study, 120 translations were evaluated by professional translators as well as by two representative AE-systems, BLEU/ METEOR, respectively. The correlations between AE and HE were relatively high at 0.849** (BLEU) and 0.862** (METEOR) in the overall analysis, but in the ratings of the individual texts, AE and ME exhibited a substantial difference. The AE-ME correlations were often below 0.3 or even in the negative range. Ultimately, the results indicate that neither METEOR nor BLEU can be used to assess human translation at this stage. But this paper suggests three possibilities to apply AE to compromise the weakness of HE.

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