Deep-learning models for language generation tasks tend to produce repetitive output. Various methods have been proposed to encourage lexical diversity during decoding, but this often comes at a cost to the perceived fluency and adequacy of the outpu
t. In this work, we propose to ameliorate this cost by using an Imitation Learning approach to explore the level of diversity that a language generation model can reliably produce. Specifically, we augment the decoding process with a meta-classifier trained to distinguish which words at any given timestep will lead to high-quality output. We focus our experiments on concept-to-text generation where models are sensitive to the inclusion of irrelevant words due to the strict relation between input and output. Our analysis shows that previous methods for diversity underperform in this setting, while human evaluation suggests that our proposed method achieves a high level of diversity with minimal effect on the output's fluency and adequacy.
It has been shown that training multi-task models with auxiliary tasks can improve the target task quality through cross-task transfer. However, the importance of each auxiliary task to the primary task is likely not known a priori. While the importa
nce weights of auxiliary tasks can be manually tuned, it becomes practically infeasible with the number of tasks scaling up. To address this, we propose a search method that automatically assigns importance weights. We formulate it as a reinforcement learning problem and learn a task sampling schedule based on the evaluation accuracy of the multi-task model. Our empirical evaluation on XNLI and GLUE shows that our method outperforms uniform sampling and the corresponding single-task baseline.
This paper describes the winning system in the End-to-end Pipeline phase for the NLPContributionGraph task. The system is composed of three BERT-based models and the three models are used to extract sentences, entities and triples respectively. Exper
iments show that sampling and adversarial training can greatly boost the system. In End-to-end Pipeline phase, our system got an average F1 of 0.4703, significantly higher than the second-placed system which got an average F1 of 0.3828.
Multilingual pretrained language models are rapidly gaining popularity in NLP systems for non-English languages. Most of these models feature an important corpus sampling step in the process of accumulating training data in different languages, to en
sure that the signal from better resourced languages does not drown out poorly resourced ones. In this study, we train multiple multilingual recurrent language models, based on the ELMo architecture, and analyse both the effect of varying corpus size ratios on downstream performance, as well as the performance difference between monolingual models for each language, and broader multilingual language models. As part of this effort, we also make these trained models available for public use.
These papers aim to study the estimation of the simple linear regression equation
coefficients using the least square method at different sample sizes and different sampling
methods. And so on, the main goal of this research is to try to determine
the optimum size
and the best sampling method for these coefficients. We used experimental data for a
population consist of 2000 students from different schools all over the country. We had
changed the sample size each time and calculate the coefficients and then compare these
coefficients for different sample sizes with their coefficients of the real population; and the
results have been shown that the estimation of the linear regression equation coefficients
are close from the real values of the coefficients of the regression line equation for the
population when the sample size closes the value (325). As it turns out that the Stratified
random sampling with proportional distribution with class sizes gives the best and most
accurate results to estimate linear regression equation with least square method.
The study aimed at identifying the method of joint auditing of the financial
statements and the stages that must be carried out to complete the audit process using the
statistical survey.
To achieve this objective, a field study was carried out th
rough the creation of a
questionnaire distributed to a sample of practicing auditors according to the Association of
Chartered Accountants in Damascus, and then analyzing the results and testing the
hypotheses using SPSS.
And research found the following results: There is the role of the joint audit in
raising the efficiency of the use of a preview qualities in the audit and special evaluation of
test results jointly.
There is a role for joint auditing in raising the efficiency of the use of the
examination of variables in the audit, especially the assessment of relative importance in a
joint manner.
There is a role for joint auditing in raising the efficiency of determining the size of
the sample and the factors affecting it, especially the acceptable risk rate in common.
There is the role of the joint audit in reducing the risk of use of statistical sampling in
audit, special audit is divided between the auditors' joint accounts on the basis of the
applicable auditing or corporate functions business cycles.
This research deals with the study and analysis the issue of planning and designing statistical sampling plan in specifications and how to use in monitoring and control industrial products quality in the light of the relationship between the producer
and the consumer by examining certain quantities of production based on criteria pre defined in order to judgement on the batch viability to reach an acceptable quality level or not.
The acceptance preview concept involves on examination of materials and parts of the external source, product examination in its different parts and final examination for the product by one customer or more, by using American system (MIL-STD-105E) and tables of (Dodge and Roming) which related with statistical sampling plans.
This research shows theoretical and practical how to design statistical sampling plans to adjust the industrial products quality and its application in products quality control of Jood company for assembling electrical tools using statistical program (SPSS).
The study concludes to results confirm that the company doesn't have an application of statistical techniques in quality control and limited to measuring the output quality characteristics in the laboratory without a clear scientific method in examine batches such as designing statistical sampling plan.
Jood company has certificates of Syrian and European standards of quality control so it had to be a recommendation for the company in application the scientific statistical sampling techniques in all production stages and training the staff on its application using specialists in the field of quality with plans showing the place and the time of doing sampling and determine sampling points in different production stages in order to promote the company with its products and provide products almost free of defects which qualifies the company to competition in foreign markets, not local.
This paper seeks to assess the extent to which the auditing profession in Syria
depends on risk assessment. This study starts by analyzing the importance of the risk
approach and its implications for contemporary auditing. The study intends to exam
ine to
what extent, if at all, the Syrian auditing profession takes the assessment of auditing risk
factor into consideration and the correlation, if any, between risk and other related factors
such as business risk, assessment risk, and control risk. To this end, the researcher designs
a questionnaire and sends it to 100 auditors through the Association of Syrian Certified
Accountants. 51 questionnaires are properly filled. To analyze data, SPSS package has
been used. The results have clearly confirmed the hypothesis that the auditing profession in
Syria does not depend on risk assessment.
This study was conducted at the farm of agriculture college (Abu Jarash),
Damascus university to determine the impact of time and depth of adding
phosphorus on it's availability in calcareous soil cultivated with corn during
2011 and 2012 seasons.
Super phosphorus fertilizer was added to the soil at
three different depths (0, 10 and 20 cm), while the control spot left without
fertilizer addition. Soil samples were taken for analysis of available phosphorus
(P) from 12 replicates at different depths 0-10, 10-20, 20-30, 30-40 cm with two
samples for every depth at zero time, 15, 30, 45, 60 and 90 days during the
growing corn.
Results indicated that the available phosphorus increased directly in all
samples after cultivation, with a marked value recorded at all depths after 15
days of cultivation. This concentration began decreasing gradually with a
constant rate in time in all transactions and reached 50% after 90 days of
cultivation. It was also observed that available phosphorus was higher at the
two depths, 0-10 and 10-20 cm and after 15 days of post- corn cultivation and
followed by zero time and it was found that the best concentration was
observed at depth extended from 0 to 20 cm in the treatment of adding
fertilizer at depth of 10 cm and time of sampling was 15 days of cultivation.
