scholarly journals Dictionary-Guided Editing Networks for Paraphrase Generation

2019 ◽  
Vol 33 ◽  
pp. 6546-6553 ◽  
Author(s):  
Shaohan Huang ◽  
Yu Wu ◽  
Furu Wei ◽  
Zhongzhi Luan
Keyword(s):  
Automatic Evaluation ◽  
Word Level ◽  
Human Evaluation ◽  
Original Sentence ◽  
Novel Approach ◽  
Benchmark Datasets ◽  
Source Sentence ◽  
Input Sentence ◽  
Paraphrase Generation ◽  
Selection Of

An intuitive way for a human to write paraphrase sentences is to replace words or phrases in the original sentence with their corresponding synonyms and make necessary changes to ensure the new sentences are fluent and grammatically correct. We propose a novel approach to modeling the process with dictionary-guided editing networks which effectively conduct rewriting on the source sentence to generate paraphrase sentences. It jointly learns the selection of the appropriate word level and phrase level paraphrase pairs in the context of the original sentence from an off-the-shelf dictionary as well as the generation of fluent natural language sentences. Specifically, the system retrieves a set of word level and phrase level paraphrase pairs derived from the Paraphrase Database (PPDB) for the original sentence, which is used to guide the decision of which the words might be deleted or inserted with the soft attention mechanism under the sequence-to-sequence framework. We conduct experiments on two benchmark datasets for paraphrase generation, namely the MSCOCO and Quora dataset. The automatic evaluation results demonstrate that our dictionary-guided editing networks outperforms the baseline methods. On human evaluation, results indicate that the generated paraphrases are grammatically correct and relevant to the input sentence.

Mining Undominated Association Rules Through Interestingness Measures

2014 ◽  
Vol 23 (04) ◽  
pp. 1460011 ◽  
Author(s):  
Slim Bouker ◽  
Rabie Saidi ◽  
Sadok Ben Yahia ◽  
Engelbert Mephu Nguifo
Keyword(s):  
Association Rules ◽  
Threshold Value ◽  
Interestingness Measures ◽  
Novel Approach ◽  
Benchmark Datasets ◽  
Efficient Selection ◽  
Selection Of

The increasing growth of databases raises an urgent need for more accurate methods to better understand the stored data. In this scope, association rules were extensively used for the analysis and the comprehension of huge amounts of data. However, the number of generated rules is too large to be efficiently analyzed and explored in any further process. In order to bypass this hamper, an efficient selection of rules has to be performed. Since selection is necessarily based on evaluation, many interestingness measures have been proposed. However, the abundance of these measures gave rise to a new problem, namely the heterogeneity of the evaluation results and this created confusion to the decision. In this respect, we propose a novel approach to discover interesting association rules without favoring or excluding any measure by adopting the notion of dominance between association rules. Our approach bypasses the problem of measure heterogeneity and unveils a compromise between their evaluations. Interestingly enough, the proposed approach also avoids another non-trivial problem which is the threshold value specification. Extensive carried out experiments on benchmark datasets show the benefits of the introduced approach.

Adaptive One-Class gaussian processes allow accurate prioritization of oncology drug targets

2020 ◽  
Author(s):  
Antonio de Falco ◽  
Zoltan Dezso ◽  
Francesco Ceccarelli ◽  
Luigi Cerulo ◽  
Angelo Ciaramella ◽  
...  
Keyword(s):  
Gaussian Processes ◽  
Drug Targets ◽  
Process Model ◽  
New Drugs ◽  
Supplementary Information ◽  
Novel Approach ◽  
Benchmark Datasets ◽  
Hyperparameter Selection ◽  
The Cost ◽  
Selection Of

Abstract Motivation The cost of drug development has dramatically increased in the last decades, with the number new drugs approved per billion US dollars spent on R&D halving every year or less. The selection and prioritization of targets is one the the most influential decisions in drug discovery. Here we present a Gaussian Process model for the prioritization of drug targets cast as a problem of learning with only positive and unlabeled examples. Results Since the absence of negative samples does not allow standard methods for automatic selection of hyperparameters, we propose a novel approach for hyperparameter selection of the kernel in One Class Gaussian Processes. We compare our methods with state-of-the-art approaches on benchmark datasets and then show its application to druggability prediction of oncology drugs. Our score reaches an AUC 0.90 on a set of clinical trial targets starting from a small training set of 102 validated oncology targets. Our score recovers the majority of known drug targets and can be used to identify novel set of proteins as drug target candidates. Availability Source code implemented in Python is freely available for download at https://github.com/AntonioDeFalco/Adaptive-OCGP. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Integrating Linguistic Knowledge to Sentence Paraphrase Generation

2020 ◽  
Vol 34 (05) ◽  
pp. 8368-8375
Author(s):  
Zibo Lin ◽  
Ziran Li ◽  
Ning Ding ◽  
Hai-Tao Zheng ◽  
Ying Shen ◽  
...  
Keyword(s):  
State Of The Art ◽  
Experimental Results ◽  
Linguistic Knowledge ◽  
Position Information ◽  
Encoding Scheme ◽  
Human Evaluation ◽  
Task Architecture ◽  
The Given ◽  
Paraphrase Generation ◽  
Selection Of

Paraphrase generation aims to rewrite a text with different words while keeping the same meaning. Previous work performs the task based solely on the given dataset while ignoring the availability of external linguistic knowledge. However, it is intuitive that a model can generate more expressive and diverse paraphrase with the help of such knowledge. To fill this gap, we propose Knowledge-Enhanced Paraphrase Network (KEPN), a transformer-based framework that can leverage external linguistic knowledge to facilitate paraphrase generation. (1) The model integrates synonym information from the external linguistic knowledge into the paraphrase generator, which is used to guide the decision on whether to generate a new word or replace it with a synonym. (2) To locate the synonym pairs more accurately, we adopt an incremental encoding scheme to incorporate position information of each synonym. Besides, a multi-task architecture is designed to help the framework jointly learn the selection of synonym pairs and the generation of expressive paraphrase. Experimental results on both English and Chinese datasets show that our method significantly outperforms the state-of-the-art approaches in terms of both automatic and human evaluation.

Collecting Words: A Clinical Example of a Morphology-Focused Orthographic Intervention

2020 ◽  
Vol 51 (3) ◽  
pp. 544-560 ◽  
Author(s):  
Kimberly A. Murphy ◽  
Emily A. Diehm
Keyword(s):  
Written Language ◽  
Morphological Knowledge ◽  
English Orthography ◽  
Word Level ◽  
Novel Approach ◽  
Language Pathology ◽  
The One ◽  
Critical Intervention ◽  
Reading And Spelling ◽  
Reading And Spelling Difficulties

Purpose Morphological interventions promote gains in morphological knowledge and in other oral and written language skills (e.g., phonological awareness, vocabulary, reading, and spelling), yet we have a limited understanding of critical intervention features. In this clinical focus article, we describe a relatively novel approach to teaching morphology that considers its role as the key organizing principle of English orthography. We also present a clinical example of such an intervention delivered during a summer camp at a university speech and hearing clinic. Method Graduate speech-language pathology students provided a 6-week morphology-focused orthographic intervention to children in first through fourth grade ( n = 10) who demonstrated word-level reading and spelling difficulties. The intervention focused children's attention on morphological families, teaching how morphology is interrelated with phonology and etymology in English orthography. Results Comparing pre- and posttest scores, children demonstrated improvement in reading and/or spelling abilities, with the largest gains observed in spelling affixes within polymorphemic words. Children and their caregivers reacted positively to the intervention. Therefore, data from the camp offer preliminary support for teaching morphology within the context of written words, and the intervention appears to be a feasible approach for simultaneously increasing morphological knowledge, reading, and spelling. Conclusion Children with word-level reading and spelling difficulties may benefit from a morphology-focused orthographic intervention, such as the one described here. Research on the approach is warranted, and clinicians are encouraged to explore its possible effectiveness in their practice. Supplemental Material https://doi.org/10.23641/asha.12290687

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.

Development of a Performance-Related Framework for Asphalt Mixture Design for the Illinois Tollway

2021 ◽  
pp. 036119812110148
Author(s):  
Behnam Jahangiri ◽  
Punyaslok Rath ◽  
Hamed Majidifard ◽  
William G. Buttlar
Keyword(s):  
Asphalt Mixture ◽  
Field Performance ◽  
Compact Tension ◽  
T Test ◽  
Novel Approach ◽  
Final Adjustment ◽  
Development And Validation ◽  
High Degree ◽  
Selection Of

Various agencies have begun to research and introduce performance-related specifications (PRS) for the design of modern asphalt paving mixtures. The focus of most recent studies has been directed toward simplified cracking test development and evaluation. In some cases, development and validation of PRS has been performed, building on these new tests, often by comparison of test values to accelerated pavement test studies and/or to limited field data. This study describes the findings of a comprehensive research project conducted at Illinois Tollway, leading to a PRS for the design of mainline and shoulder asphalt mixtures. A novel approach was developed, involving the systematic establishment of specification requirements based on: 1) selection of baseline values based on minimally acceptable field performance thresholds; 2) elevation of thresholds to account for differences between short-term lab aging and expected long-term field aging; 3) further elevation of thresholds to account for variability in lab testing, plus variability in the testing of field cores; and 4) final adjustment and rounding of thresholds based on a consensus process. After a thorough evaluation of different candidate cracking tests in the course of the project, the Disk-shaped Compact Tension—DC(T)—test was chosen to be retained in the Illinois Tollway PRS and to be presented in this study for the design of crack-resistant mixtures. The DC(T) test was selected because of its high degree of correlation with field results and its excellent repeatability. Tailored Hamburg rut depth and stripping inflection point thresholds were also established for mainline and shoulder mixes.

scholarly journals Gradient-based selection of cross sections: a novel approach for optimal frame structure design

2017 ◽  
Vol 56 (5) ◽  
pp. 959-972 ◽  
Author(s):  
Christian Krogh ◽  
Mathias H. Jungersen ◽  
Erik Lund ◽  
Esben Lindgaard
Keyword(s):  
Cross Sections ◽  
Structure Design ◽  
Frame Structure ◽  
Novel Approach ◽  
Gradient Based ◽  
Selection Of

scholarly journals A novel approach for rapid high-throughput selection of recombinant functional rat monoclonal antibodies

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Qin Chen ◽  
Shengping Qiu ◽  
Huanhuan Li ◽  
Chaolong Lin ◽  
Yong Luo ◽  
...  
Keyword(s):  
Monoclonal Antibodies ◽  
High Throughput ◽  
Novel Approach ◽  
Selection Of

scholarly journals DRr-Net: Dynamic Re-Read Network for Sentence Semantic Matching

2019 ◽  
Vol 33 ◽  
pp. 7442-7449 ◽  
Author(s):  
Kun Zhang ◽  
Guangyi Lv ◽  
Linyuan Wang ◽  
Le Wu ◽  
Enhong Chen ◽  
...  
Keyword(s):  
Natural Language ◽  
Small Region ◽  
Psychological Research ◽  
Attention Mechanism ◽  
Semantic Relations ◽  
Semantic Matching ◽  
Close Attention ◽  
Original Sentence ◽  
Benchmark Datasets ◽  
Sentence Matching

Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks such as Natural Language Inference (NLI) and Paraphrase Identification (PI). Among all matching methods, attention mechanism plays an important role in capturing the semantic relations and properly aligning the elements of two sentences. Previous methods utilized attention mechanism to select important parts of sentences at one time. However, the important parts of the sentence during semantic matching are dynamically changing with the degree of sentence understanding. Selecting the important parts at one time may be insufficient for semantic understanding. To this end, we propose a Dynamic Re-read Network (DRr-Net) approach for sentence semantic matching, which is able to pay close attention to a small region of sentences at each step and re-read the important words for better sentence semantic understanding. To be specific, we first employ Attention Stack-GRU (ASG) unit to model the original sentence repeatedly and preserve all the information from bottom-most word embedding input to up-most recurrent output. Second, we utilize Dynamic Re-read (DRr) unit to pay close attention to one important word at one time with the consideration of learned information and re-read the important words for better sentence semantic understanding. Extensive experiments on three sentence matching benchmark datasets demonstrate that DRr-Net has the ability to model sentence semantic more precisely and significantly improve the performance of sentence semantic matching. In addition, it is very interesting that some of finding in our experiments are consistent with the findings of psychological research.

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