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.
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.
In this study, we focused on the interpolation methods for the
derivation of digital elevation models, based on field gridding
observations with different spacing.
The present study aimed to discuss the concept of quality of
information provided by the MIS in the Syrian Telecommunications
company with identifying the most important dimensions of
information quality, and how to measure them in order to test their
impact on the decision making process.
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 research aims to show the importance of ensuring the
competence of all who operate specific equipment, perform tests
and/or calibrations, evaluate results, and sign test reports and
calibration certificates.
Studying of land use changing Detection needs to the speed of implementation to
convoy the changes on the ground. the traditional ways in the analysis and visual
interpretation of the images and field studying need a lot of time and effort. So The
objective of this search is to classify group images (Landsat TM , ETM+) taken in different
dates automatically, and then to calculate the area of each land use/land cover, during the
years studied (1990 -2000- 2010) and comparison areas to identify the most important
changes occurring during that period.
The research aims to show the importance of the application of
International Standard ISO 17025: 2005 to adjust the general
requirements of the test laboratories efficiency. The importance of
this international standard by giving great importance
to the test
methods and calibration, which stressed the need for the
documented methods to carry out tests and analyzes and operation
of devices, and that it leads to work systematically and preserve the
expertise & documenting them and discover the analytical
problems and resolve them.
