scholarly journals Weakly-Supervised Opinion Summarization by Leveraging External Information

2020 ◽  
Vol 34 (05) ◽  
pp. 9644-9651
Author(s):  
Chao Zhao ◽  
Snigdha Chaturvedi
Keyword(s):  
Explicit Memory ◽  
Domain Knowledge ◽  
State Of The Art ◽  
External Information ◽  
Product Reviews ◽  
Opinion Summarization ◽  
External Sources ◽  
Human Effort ◽  
Weakly Supervised ◽  
The Given

Opinion summarization from online product reviews is a challenging task, which involves identifying opinions related to various aspects of the product being reviewed. While previous works require additional human effort to identify relevant aspects, we instead apply domain knowledge from external sources to automatically achieve the same goal. This work proposes AspMem, a generative method that contains an array of memory cells to store aspect-related knowledge. This explicit memory can help obtain a better opinion representation and infer the aspect information more precisely. We evaluate this method on both aspect identification and opinion summarization tasks. Our experiments show that AspMem outperforms the state-of-the-art methods even though, unlike the baselines, it does not rely on human supervision which is carefully handcrafted for the given tasks.

scholarly journals Partial Label Learning by Semantic Difference Maximization

2019 ◽  
Author(s):  
Lei Feng ◽  
Bo An
Keyword(s):  
State Of The Art ◽  
Ground Truth ◽  
Positive Answer ◽  
Learning Framework ◽  
Novel Approach ◽  
Semantic Difference ◽  
Weakly Supervised ◽  
Partial Label Learning ◽  
The Given ◽  
First Time

Partial label learning is a weakly supervised learning framework, in which each instance is provided with multiple candidate labels while only one of them is correct. Most of the existing approaches focus on leveraging the instance relationships to disambiguate the given noisy label space, while it is still unclear whether we can exploit potentially useful information in label space to alleviate the label ambiguities. This paper gives a positive answer to this question for the first time. Specifically, if two instances do not share any common candidate labels, they cannot have the same ground-truth label. By exploiting such dissimilarity relationships from label space, we propose a novel approach that aims to maximize the latent semantic differences of the two instances whose ground-truth labels are definitely different, while training the desired model simultaneously, thereby continually enlarging the gap of label confidences between two instances of different classes. Extensive experiments on artificial and real-world partial label datasets show that our approach significantly outperforms state-of-the-art counterparts.

scholarly journals CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation

2020 ◽  
Vol 34 (07) ◽  
pp. 10762-10769
Author(s):  
Junsong Fan ◽  
Zhaoxiang Zhang ◽  
Tieniu Tan ◽  
Chunfeng Song ◽  
Jun Xiao
Keyword(s):  
State Of The Art ◽  
Semantic Segmentation ◽  
Cutting Edge ◽  
Single Image ◽  
Heuristic Rules ◽  
Test Set ◽  
Supervised Segmentation ◽  
Great Progress ◽  
Human Effort ◽  
Weakly Supervised

Weakly supervised semantic segmentation with only image-level labels saves large human effort to annotate pixel-level labels. Cutting-edge approaches rely on various innovative constraints and heuristic rules to generate the masks for every single image. Although great progress has been achieved by these methods, they treat each image independently and do not take account of the relationships across different images. In this paper, however, we argue that the cross-image relationship is vital for weakly supervised segmentation. Because it connects related regions across images, where supplementary representations can be propagated to obtain more consistent and integral regions. To leverage this information, we propose an end-to-end cross-image affinity module, which exploits pixel-level cross-image relationships with only image-level labels. By means of this, our approach achieves 64.3% and 65.3% mIoU on Pascal VOC 2012 validation and test set respectively, which is a new state-of-the-art result by only using image-level labels for weakly supervised semantic segmentation, demonstrating the superiority of our approach.

scholarly journals Automatic Construction of Parallel Portfolios via Explicit Instance Grouping

2019 ◽  
Vol 33 ◽  
pp. 1560-1567 ◽  
Author(s):  
Shengcai Liu ◽  
Ke Tang ◽  
Xin Yao
Keyword(s):  
Domain Knowledge ◽  
State Of The Art ◽  
Boolean Satisfiability ◽  
Traveling Salesman ◽  
Problem Instance ◽  
Traveling Salesman Problems ◽  
Automatic Construction ◽  
Parallel Solvers ◽  
Hard Problems ◽  
Human Effort

Exploiting parallelism is becoming more and more important in designing efficient solvers for computationally hard problems. However, manually building parallel solvers typically requires considerable domain knowledge and plenty of human effort. As an alternative, automatic construction of parallel portfolios (ACPP) aims at automatically building effective parallel portfolios based on a given problem instance set and a given rich configuration space. One promising way to solve the ACPP problem is to explicitly group the instances into different subsets and promote a component solver to handle each of them. This paper investigates solving ACPP from this perspective, and especially studies how to obtain a good instance grouping. The experimental results on two widely studied problem domains, the boolean satisfiability problems (SAT) and the traveling salesman problems (TSP), showed that the parallel portfolios constructed by the proposed method could achieve consistently superior performances to the ones constructed by the state-of-the-art ACPP methods, and could even rival sophisticated hand-designed parallel solvers.

scholarly journals A novel optimal multi-pattern matching method with wildcards for DNA sequence

2021 ◽  
Vol 29 ◽  
pp. 115-124
Author(s):  
Xinlu Wang ◽  
Ahmed A.F. Saif ◽  
Dayou Liu ◽  
Yungang Zhu ◽  
Jon Atli Benediktsson
Keyword(s):  
Dna Sequence ◽  
Pattern Matching ◽  
Health Informatics ◽  
State Of The Art ◽  
Machine Language ◽  
Data Sets ◽  
Fundamental Issue ◽  
Matching Method ◽  
Dna Sequence Alignment ◽  
The Given

BACKGROUND: DNA sequence alignment is one of the most fundamental and important operation to identify which gene family may contain this sequence, pattern matching for DNA sequence has been a fundamental issue in biomedical engineering, biotechnology and health informatics. OBJECTIVE: To solve this problem, this study proposes an optimal multi pattern matching with wildcards for DNA sequence. METHODS: This proposed method packs the patterns and a sliding window of texts, and the window slides along the given packed text, matching against stored packed patterns. RESULTS: Three data sets are used to test the performance of the proposed algorithm, and the algorithm was seen to be more efficient than the competitors because its operation is close to machine language. CONCLUSIONS: Theoretical analysis and experimental results both demonstrate that the proposed method outperforms the state-of-the-art methods and is especially effective for the DNA sequence.

scholarly journals Question-aware memory network for multi-hop question answering in human–robot interaction

2021 ◽  
Author(s):  
Xinmeng Li ◽  
Mamoun Alazab ◽  
Qian Li ◽  
Keping Yu ◽  
Quanjun Yin
Keyword(s):  
Question Answering ◽  
State Of The Art ◽  
Human Robot Interaction ◽  
Knowledge Graph ◽  
Robot Interaction ◽  
Natural Language Question ◽  
Memory Network ◽  
The Given ◽  
Fine Tune ◽  
Language Question

AbstractKnowledge graph question answering is an important technology in intelligent human–robot interaction, which aims at automatically giving answer to human natural language question with the given knowledge graph. For the multi-relation question with higher variety and complexity, the tokens of the question have different priority for the triples selection in the reasoning steps. Most existing models take the question as a whole and ignore the priority information in it. To solve this problem, we propose question-aware memory network for multi-hop question answering, named QA2MN, to update the attention on question timely in the reasoning process. In addition, we incorporate graph context information into knowledge graph embedding model to increase the ability to represent entities and relations. We use it to initialize the QA2MN model and fine-tune it in the training process. We evaluate QA2MN on PathQuestion and WorldCup2014, two representative datasets for complex multi-hop question answering. The result demonstrates that QA2MN achieves state-of-the-art Hits@1 accuracy on the two datasets, which validates the effectiveness of our model.

scholarly journals Weak Supervision Enhanced Generative Network for Question Generation

2019 ◽  
Author(s):  
Yutong Wang ◽  
Jiyuan Zheng ◽  
Qijiong Liu ◽  
Zhou Zhao ◽  
Jun Xiao ◽  
...  
Keyword(s):  
Question Answering ◽  
Interaction Mechanism ◽  
Generation System ◽  
Dialogue System ◽  
Question Generation ◽  
Weak Supervision ◽  
Weakly Supervised ◽  
The Given ◽  
Automatic Question Generation

Automatic question generation according to an answer within the given passage is useful for many applications, such as question answering system, dialogue system, etc. Current neural-based methods mostly take two steps which extract several important sentences based on the candidate answer through manual rules or supervised neural networks and then use an encoder-decoder framework to generate questions about these sentences. These approaches still acquire two steps and neglect the semantic relations between the answer and the context of the whole passage which is sometimes necessary for answering the question. To address this problem, we propose the Weakly Supervision Enhanced Generative Network (WeGen) which automatically discovers relevant features of the passage given the answer span in a weakly supervised manner to improve the quality of generated questions. More specifically, we devise a discriminator, Relation Guider, to capture the relations between the passage and the associated answer and then the Multi-Interaction mechanism is deployed to transfer the knowledge dynamically for our question generation system. Experiments show the effectiveness of our method in both automatic evaluations and human evaluations.

scholarly journals Centralized Ranking Loss with Weakly Supervised Localization for Fine-Grained Object Retrieval

2018 ◽  
Author(s):  
Xiawu Zheng ◽  
Rongrong Ji ◽  
Xiaoshuai Sun ◽  
Yongjian Wu ◽  
Feiyue Huang ◽  
...  
Keyword(s):  
State Of The Art ◽  
Feature Learning ◽  
Target Object ◽  
Object Retrieval ◽  
Unified Framework ◽  
Fine Grained ◽  
Discriminative Feature ◽  
Triplet Loss ◽  
Weakly Supervised ◽  
Ranking Loss

Fine-grained object retrieval has attracted extensive research focus recently. Its state-of-the-art schemesare typically based upon convolutional neural network (CNN) features. Despite the extensive progress, two issues remain open. On one hand, the deep features are coarsely extracted at image level rather than precisely at object level, which are interrupted by background clutters. On the other hand, training CNN features with a standard triplet loss is time consuming and incapable to learn discriminative features. In this paper, we present a novel fine-grained object retrieval scheme that conquers these issues in a unified framework. Firstly, we introduce a novel centralized ranking loss (CRL), which achieves a very efficient (1,000times training speedup comparing to the triplet loss) and discriminative feature learning by a ?centralized? global pooling. Secondly, a weakly supervised attractive feature extraction is proposed, which segments object contours with top-down saliency. Consequently, the contours are integrated into the CNN response map to precisely extract features ?within? the target object. Interestingly, we have discovered that the combination of CRL and weakly supervised learning can reinforce each other. We evaluate the performance ofthe proposed scheme on widely-used benchmarks including CUB200-2011 and CARS196. We havereported significant gains over the state-of-the-art schemes, e.g., 5.4% over SCDA [Wei et al., 2017]on CARS196, and 3.7% on CUB200-2011.  

scholarly journals Exemplar Guided Neural Dialogue Generation

2020 ◽  
Author(s):  
Hengyi Cai ◽  
Hongshen Chen ◽  
Yonghao Song ◽  
Xiaofang Zhao ◽  
Dawei Yin
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Training Data ◽  
Small Subset ◽  
Generation Model ◽  
Retrieval Model ◽  
Training Set ◽  
Dialogue Model ◽  
Quantitative Metrics ◽  
The Given

Humans benefit from previous experiences when taking actions. Similarly, related examples from the training data also provide exemplary information for neural dialogue models when responding to a given input message. However, effectively fusing such exemplary information into dialogue generation is non-trivial: useful exemplars are required to be not only literally-similar, but also topic-related with the given context. Noisy exemplars impair the neural dialogue models understanding the conversation topics and even corrupt the response generation. To address the issues, we propose an exemplar guided neural dialogue generation model where exemplar responses are retrieved in terms of both the text similarity and the topic proximity through a two-stage exemplar retrieval model. In the first stage, a small subset of conversations is retrieved from a training set given a dialogue context. These candidate exemplars are then finely ranked regarding the topical proximity to choose the best-matched exemplar response. To further induce the neural dialogue generation model consulting the exemplar response and the conversation topics more faithfully, we introduce a multi-source sampling mechanism to provide the dialogue model with both local exemplary semantics and global topical guidance during decoding. Empirical evaluations on a large-scale conversation dataset show that the proposed approach significantly outperforms the state-of-the-art in terms of both the quantitative metrics and human evaluations.

Communication and Documentation on Parliamentary Websites

2012 ◽  
pp. 60-81
Author(s):  
Enrico Seta
Keyword(s):  
Domain Knowledge ◽  
Information Sources ◽  
External Information ◽  
Shared Services ◽  
Innovative Methods ◽  
Research Activities ◽  
Management Of Resources ◽  
Transformational Government ◽  
Institutional Communication ◽  
Chamber Of Deputies

This chapter describes a new section of the parliamentary website of the Italian Chamber of Deputies launched in November 2009 “Temi dell’attività parlamentare.” In this section, parliamentary records, research material, and hyperlinks to a variety of external information sources are integrated in a unified digital resource structured according to topical indexes. The new website’s section has been conceived as an evolution of documentation and research activities done by the internal staff of the Research Department of the Chamber of Deputies and benefits from the domain knowledge of the parliamentary documentation experts. The chapter discusses future innovation implied by the implementation of the project in the areas of institutional communication, documentation supporting legislators, and their staff internal organization. It also suggests that these innovations relate to the passage from e-government to the conceptual and operational model of “transformational government” (t-government), that emphasizes a citizen-centric delivery of public services, a shared services culture, and innovative methods in management of resources and skills in public administration.

Communication and Documentation on Parliamentary Websites

2012 ◽  
pp. 103-124
Author(s):  
Enrico Seta
Keyword(s):  
Domain Knowledge ◽  
Information Sources ◽  
External Information ◽  
Shared Services ◽  
Innovative Methods ◽  
Research Activities ◽  
Management Of Resources ◽  
Transformational Government ◽  
Institutional Communication ◽  
Chamber Of Deputies

This chapter describes a new section of the parliamentary website of the Italian Chamber of Deputies launched in November 2009 “Temi dell’attività parlamentare.” In this section, parliamentary records, research material, and hyperlinks to a variety of external information sources are integrated in a unified digital resource structured according to topical indexes. The new website’s section has been conceived as an evolution of documentation and research activities done by the internal staff of the Research Department of the Chamber of Deputies and benefits from the domain knowledge of the parliamentary documentation experts. The chapter discusses future innovation implied by the implementation of the project in the areas of institutional communication, documentation supporting legislators, and their staff internal organization. It also suggests that these innovations relate to the passage from e-government to the conceptual and operational model of “transformational government” (t-government), that emphasizes a citizen-centric delivery of public services, a shared services culture, and innovative methods in management of resources and skills in public administration.

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