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Register a new user``Politeness, you simpleton!'' retorted [MASK]: Masked prediction of literary characters
`` المداراة، أنت simpleton!
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ما هي أفضل طريقة لتعلم المدينات للكيانات، وما الذي يمكن تعلمه منهم؟نحن نعتبر هذا السؤال لحالة الشخصيات الأدبية.نتعلم المهمة الصعبة للغاية المتمثلة في التخمين، من جملة في الرواية، التي يتم التحدث بها الشخصية، ونحن نجبر على المدينات لمعرفة المعلومات التي تشفر بها عن شخصياتهم الأدبية.نجد أنه عند تدريبها بشكل مستمر، تقوم Asceddings Engysdings بشكل جيد في مهمة تنبؤ الكيانات الملثمين، وأنها ترمز معلومات كبيرة عن سمات وخصائص الكيانات.
What is the best way to learn embeddings for entities, and what can be learned from them? We consider this question for the case of literary characters. We address the highly challenging task of guessing, from a sentence in the novel, which character is being talked about, and we probe the embeddings to see what information they encode about their literary characters. We find that when continuously trained, entity embeddings do well at the masked entity prediction task, and that they encode considerable information about the traits and characteristics of the entities.
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This study is based on the monitoring attempt based on combining three fields of knowledge are: philosophy, history, literature, reflected this attempt in the biography of science is the science of the history of ideas, and this is science culminatio
n of the evolution of the two fields the first two; philosophy evolved Mbagesha of (metaphysics), to ( theory of knowledge); also resulted in other sciences as a science meeting, and Ethics. As history has maintained relative stability in terms of subject matter, identify the actions of human beings and their curricula, until the appearance of the philosophy of history taught ideal motives and Supreme underlying structures behind human.
The third field any literature has remained a laboratory for each development, it is the scene of test philosophy forest, and specifically different research methods included in the field who is also the document adopted by historians in their studies and their work in the course of nations and their actions .
The Mathol study presented is another type of philosophy, literary, which to this type of approach substantive literature to Atjal Thread literary purpose as much Matjal ideas - the constituent units of this topic purpose, and therefore taught literature and the same thought in the dynamics and evolution and change, being studied in more Msarha inclusive diversity in literature, and are in the process of typing another kind of history is the history of an individual separate from the history Bashklah unwavering: metaphysician (divine), and material (ground)
This paper describes our submission to SemEval-2021 Task 1: predicting the complexity score for single words. Our model leverages standard morphosyntactic and frequency-based features that proved helpful for Complex Word Identification (a related tas
k), and combines them with predictions made by Transformer-based pre-trained models that were fine-tuned on the Shared Task data. Our submission system stacks all previous models with a LightGBM at the top. One novelty of our approach is the use of multi-task learning for fine-tuning a pre-trained model for both Lexical Complexity Prediction and Word Sense Disambiguation. Our analysis shows that all independent models achieve a good performance in the task, but that stacking them obtains a Pearson correlation of 0.7704, merely 0.018 points behind the winning submission.
Transformer is an attention-based neural network, which consists of two sublayers, namely, Self-Attention Network (SAN) and Feed-Forward Network (FFN). Existing research explores to enhance the two sublayers separately to improve the capability of Tr
ansformer for text representation. In this paper, we present a novel understanding of SAN and FFN as Mask Attention Networks (MANs) and show that they are two special cases of MANs with static mask matrices. However, their static mask matrices limit the capability for localness modeling in text representation learning. We therefore introduce a new layer named dynamic mask attention network (DMAN) with a learnable mask matrix which is able to model localness adaptively. To incorporate advantages of DMAN, SAN, and FFN, we propose a sequential layered structure to combine the three types of layers. Extensive experiments on various tasks, including neural machine translation and text summarization demonstrate that our model outperforms the original Transformer.
Under the general rule in the proof of legal actions in the
Syrian law on evidence in writing, shall not be proof of
testimony only within the limits of quantitative quorum for the
certificate as stipulated Syrian Evidence Act.
However, evidence
of proof the law has been passed to testify if
required commitment exceeded the value of proven
quantitative quorum of the certificate; if a literary mind if
found without written guide.
This study aims to indicate what the literary inhibitor; and by
clarifying cases of literary inhibitor, which allows proof of
testimony, also aims to demonstrate the power of the judiciary
to appreciate the literary inhibitor, and the consequences of
proven literary inhibitor and how to prove.
The paper reports the results of a translationese study of literary texts based on translated and non-translated Russian. We aim to find out if translations deviate from non-translated literary texts, and if the established differences can be attribu
ted to typological relations between source and target languages. We expect that literary translations from typologically distant languages should exhibit more translationese, and the fingerprints of individual source languages (and their families) are traceable in translations. We explore linguistic properties that distinguish non-translated Russian literature from translations into Russian. Our results show that non-translated fiction is different from translations to the degree that these two language varieties can be automatically classified. As expected, language typology is reflected in translations of literary texts. We identified features that point to linguistic specificity of Russian non-translated literature and to shining-through effects. Some of translationese features cut across all language pairs, while others are characteristic of literary translations from languages belonging to specific language families.
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