Competitive Interactions of Two Species of Freshwater Turtles, a Generalist Omnivore and an Herbivore, Under Low Resource Conditions

Herpetologica ◽  
2010 ◽  
Vol 66 (3) ◽  
pp. 259-268 ◽  
Author(s):  
Matthew J. Aresco
Keyword(s):  
Competitive Interactions ◽  
Freshwater Turtles ◽  
Low Resource ◽  
Resource Conditions

Improving thai-lao neural machine translation with similarity lexicon

2021 ◽  
pp. 1-10
Author(s):  
Zhiqiang Yu ◽  
Yuxin Huang ◽  
Junjun Guo
Keyword(s):  
Machine Translation ◽  
Semantic Information ◽  
Neural Machine Translation ◽  
Low Resource ◽  
Translation Quality ◽  
Decoder Architecture ◽  
Baseline System ◽  
Input Sentence ◽  
Resource Conditions ◽  
Language Pair

It has been shown that the performance of neural machine translation (NMT) drops starkly in low-resource conditions. Thai-Lao is a typical low-resource language pair of tiny parallel corpus, leading to suboptimal NMT performance on it. However, Thai and Lao have considerable similarities in linguistic morphology and have bilingual lexicon which is relatively easy to obtain. To use this feature, we first build a bilingual similarity lexicon composed of pairs of similar words. Then we propose a novel NMT architecture to leverage the similarity between Thai and Lao. Specifically, besides the prevailing sentence encoder, we introduce an extra similarity lexicon encoder into the conventional encoder-decoder architecture, by which the semantic information carried by the similarity lexicon can be represented. We further provide a simple mechanism in the decoder to balance the information representations delivered from the input sentence and the similarity lexicon. Our approach can fully exploit linguistic similarity carried by the similarity lexicon to improve translation quality. Experimental results demonstrate that our approach achieves significant improvements over the state-of-the-art Transformer baseline system and previous similar works.

scholarly journals One for “All”: a unified model for fine-grained sentiment analysis under three tasks

2021 ◽  
Vol 7 ◽  
pp. e816
Author(s):  
Heng-yang Lu ◽  
Jun Yang ◽  
Cong Hu ◽  
Wei Fang
Keyword(s):  
Sentiment Analysis ◽  
Data Augmentation ◽  
Language Model ◽  
Unified Model ◽  
Training Data ◽  
Low Resource ◽  
Fine Grained ◽  
Questions And Answers ◽  
Resource Conditions ◽  
Media Data

Background Fine-grained sentiment analysis is used to interpret consumers’ sentiments, from their written comments, towards specific entities on specific aspects. Previous researchers have introduced three main tasks in this field (ABSA, TABSA, MEABSA), covering all kinds of social media data (e.g., review specific, questions and answers, and community-based). In this paper, we identify and address two common challenges encountered in these three tasks, including the low-resource problem and the sentiment polarity bias. Methods We propose a unified model called PEA by integrating data augmentation methodology with the pre-trained language model, which is suitable for all the ABSA, TABSA and MEABSA tasks. Two data augmentation methods, which are entity replacement and dual noise injection, are introduced to solve both challenges at the same time. An ensemble method is also introduced to incorporate the results of the basic RNN-based and BERT-based models. Results PEA shows significant improvements on all three fine-grained sentiment analysis tasks when compared with state-of-the-art models. It also achieves comparable results with what the baseline models obtain while using only 20% of their training data, which demonstrates its extraordinary performance under extreme low-resource conditions.

Neural Text Simplification in Low-Resource Conditions Using Weak Supervision

2019 ◽  
Author(s):  
Alessio Palmero Aprosio ◽  
Sara Tonelli ◽  
Marco Turchi ◽  
Matteo Negri ◽  
Mattia A. Di Gangi
Keyword(s):  
Weak Supervision ◽  
Low Resource ◽  
Text Simplification ◽  
Resource Conditions

scholarly journals Comparison of Fault Simulation Over Custom Kernel Module Using Various Techniques

2021 ◽  
Vol 5 (3) ◽  
pp. 73-83
Author(s):  
Muhammad Ejaz Sandhu
Keyword(s):  
Standard Method ◽  
File System ◽  
Fault Injection ◽  
Fault Simulation ◽  
Linux Kernel ◽  
Device Drivers ◽  
Low Resource ◽  
Kernel Module ◽  
Resource Conditions ◽  
Artificial Introduction

To test the behavior of the Linux kernel module, device drivers and file system in a faulty situation, scientists tried to inject faults in different artificial environments. Since the rarity and unpredictability of such events are pretty high, thus the localization and detection of Linux kernel, device drivers, file system modules errors become unfathomable. ‘Artificial introduction of some random faults during normal tests’ is the only known approach to such mystifying problems. A standard method for performing such experiments is to generate synthetic faults and study the effects. Various fault injection frameworks have been analyzed over the Linux kernel to simulate such detection. The following paper highlights the comparison of different approaches and techniques used for such fault injection to test Linux kernel modules that include simulating low resource conditions and detecting memory leaks. The frameworks chosen to be used in these experiments are; Linux Text Project (LTP), KEDR, Linux Fault-Injection (LFI), and SCSI. 

Findings of the WMT 2019 Shared Task on Parallel Corpus Filtering for Low-Resource Conditions

2019 ◽  
Author(s):  
Philipp Koehn ◽  
Francisco Guzmán ◽  
Vishrav Chaudhary ◽  
Juan Pino
Keyword(s):  
Shared Task ◽  
Parallel Corpus ◽  
Low Resource ◽  
Resource Conditions

Diagnosis of neonatal sepsis in low resource settings: C-reactive protein or procalcitonin?

2016 ◽  
Vol 03 (02) ◽  
pp. 079-083
Author(s):  
Lawrence Mbuagbaw ◽  
Francisca Monebenimp ◽  
Bolaji Obadeyi ◽  
Grace Bissohong ◽  
Marie-Thérèse Obama ◽  
...  
Keyword(s):  
Neonatal Sepsis ◽  
C Reactive Protein ◽  
Low Resource Settings ◽  
Low Resource ◽  
Reactive Protein
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