In this paper, we introduce a new embedding-based metric relying on trainable ranking models to evaluate the semantic accuracy of neural data-to-text generators. This metric is especially well suited to semantically and factually assess the performan
ce of a text generator when tables can be associated with multiple references and table values contain textual utterances. We first present how one can implement and further specialize the metric by training the underlying ranking models on a legal Data-to-Text dataset. We show how it may provide a more robust evaluation than other evaluation schemes in challenging settings using a dataset comprising paraphrases between the table values and their respective references. Finally, we evaluate its generalization capabilities on a well-known dataset, WebNLG, by comparing it with human evaluation and a recently introduced metric based on natural language inference. We then illustrate how it naturally characterizes, both quantitatively and qualitatively, omissions and hallucinations.
We present the results and main findings of the shared task at WOAH 5 on hateful memes detection. The task include two subtasks relating to distinct challenges in the fine-grained detection of hateful memes: (1) the protected category attacked by the
meme and (2) the attack type. 3 teams submitted system description papers. This shared task builds on the hateful memes detection task created by Facebook AI Research in 2020.
The Shared Task on Evaluating Accuracy focused on techniques (both manual and automatic) for evaluating the factual accuracy of texts produced by neural NLG systems, in a sports-reporting domain. Four teams submitted evaluation techniques for this ta
sk, using very different approaches and techniques. The best-performing submissions did encouragingly well at this difficult task. However, all automatic submissions struggled to detect factual errors which are semantically or pragmatically complex (for example, based on incorrect computation or inference).
We hereby present our submission to the Shared Task in Evaluating Accuracy at the INLG 2021 Conference. Our evaluation protocol relies on three main components; rules and text classifiers that pre-annotate the dataset, a human annotator that validate
s the pre-annotations, and a web interface that facilitates this validation. Our submission consists in fact of two submissions; we first analyze solely the performance of the rules and classifiers (pre-annotations), and then the human evaluation aided by the former pre-annotations using the web interface (hybrid). The code for the web interface and the classifiers is publicly available.
Pronunciation lexicons and prediction models are a key component in several speech synthesis and recognition systems. We know that morphologically related words typically follow a fixed pattern of pronunciation which can be described by language-spec
ific paradigms. In this work we explore how deep recurrent neural networks can be used to automatically learn and exploit this pattern to improve the pronunciation prediction quality of words related by morphological inflection. We propose two novel approaches for supplying morphological information, using the word's morphological class and its lemma, which are typically annotated in standard lexicons. We report improvements across a number of European languages with varying degrees of phonological and morphological complexity, and two language families, with greater improvements for languages where the pronunciation prediction task is inherently more challenging. We also observe that combining bidirectional LSTM networks with attention mechanisms is an effective neural approach for the computational problem considered, across languages. Our approach seems particularly beneficial in the low resource setting, both by itself and in conjunction with transfer learning.
The aim of the research is to determine the role of the characteristics of banking information systems in improving banking performance in real estate bank branches in the Syrian coast. The researcher followed the descriptive and analytical approach
in his study, and a set of methods, including relying on secondary data, and primary through a questionnaire that was developed through the researcher's access to the published literature, and the research community consisted of workers at the upper and middle management levels in the real estate bank branches, and then the SPSS program was relied upon as a tool for analysis Available data. The study found that the real estate bank does not achieve an increase in the size of the market share, and the bank’s management is not keen to provide information to workers that help them in their work at the time they need. The study recommended that the bank should achieve an increase in the size of the market share, by increasing interest in marketing information systems, and the bank’s management must be keen to provide information that helps workers in their work at the time they need, because it improves their performance and this is reflected in the performance The bank in general.
The game of handball is a group of games characterized by a variety of basic skills
offensive and defensive, and varied plans, whether in the attack or defense, it is worth
mentioning that all movements of the attack aimed at finishing the correcti
on against the
opposing team, which is one of the most important duties in the practice of handball and
stop The result of the match on the accuracy of the technical performance of this skill. This
is what called for the selection of the skill of correction in this study because of its
importance, and that it is considered a key skills players in this game.
The research sample consisted of (12) young players from the center of Lattakia
Governorate. The experimental method was used in a group style.
The aim of the research is to prepare qualitative exercises to improve the accuracy of the
skill of the correction and to identify the role of the exercises in the development of the
skill level of correction, which can give positive results after application.
Where the sample was subjected to tribal testing to measure accuracy in the skill of
correction and then applied a set of specific exercises aimed at developing the skill of
correction and then telemetry when the completion of the exercise.
In comparing the tribal and remote measurements of the sample, the results of the study
showed that the specific exercises used in teaching and developing the skill of accuracy of
the correction of the base resulted in a significant improvement in the level of the research
sample.
The study recommended the use of training programs based on scientific foundations in the
development of skillful performance in handball. And the need to use targeted and
organized exercises and take into account the age groups and skill level when using
exercises to develop the skill of correction.
This study aims is to analyze the effect of spatial accuracy of the control points on the
images geometric correction accuracy, and this is done by applying tests on the same
image (IKONOS), where polynomial transformations were applied using sets
of control
points, each with absolute accuracy different from the other. These points were
extrapolated from a 1/1000 topographic map and from a georeferenced MOMS satellite
image with geometric accuracy of 2m and measured by GPS. The study showed that it is
possible to obtain the most accurate geometric correction by using control points with
absolute accuracy close to the spatial resolution of the image. It also showed that the use of
more precise control points would not ameliorate the accuracy of the geometric correction,
because the measurement of these points on the image is limited by its spatial resolution.
The main objective of this research is to develop an
arithmetic model for transformations between geographic and
State Plane Coordinate within the three types of Conformal
Syrian Conical projection (tangent, secant and Semiconformal),
In order to
enable all Specialists and surveyors to
carry out direct and reverse transformations of horizontal
coordinates of the points without returning to any competent
authorities to avoid any administrative and computational
complexities.
This study was analyzed the temporal variation of Moderate Resolution Imaging
Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) of natural
stands of cedrus libani in the northern humid part and eastern exposure of the Syrian
coastal mountains (Slenfeh, Jawbat Burghal), and its correlation with climatic variables
(temperature and precipitation) during the period of 2004-2014. We examined the
interannual and seasonal variation in NDVI values of Cedrus stands, and accumulative
effects of climatic variables (temperature and precipitation) on stands using simple linear
regression and correlation (Pearson). The NDVI values of Cedrus libani stands showed
significant increase in Slenfeh and Jawbat Burghal (0.006, 0.004 /year) respectively. We
found that the annual mean NDVI was significantly correlated with annual mean
precipitation in Jawbat Burghal (R = 0.689).The significant increase trend of seasonal
mean NDVI was in Slenfeh summer and Jawbat Burghal winter (R = 0.638, R = 0.724)
respectively. The results showed, there were accumulative effects of temperature on
Cedrus libani in Slenfeh and Jawbat Burghal in autumn and winter, while the
accumulative effects of precipitation in autumn and summer were noted.
