This paper introduces a long-range multiple-choice Question Answering (QA) dataset, based on full-length fiction book texts. The questions are formulated as 10-way multiple-choice questions, where the task is to select the correct character name give
n a character description, or vice-versa. Each character description is formulated in natural text and often contains information from several sections throughout the book. We provide 20,000 questions created from 10,000 manually annotated descriptions of characters from 177 books containing 152,917 words on average. We address the current discourse regarding dataset bias and leakage by a simple anonymization procedure, which in turn enables interesting probing possibilities. Finally, we show that suitable baseline algorithms perform very poorly on this task, with the book size itself making it non-trivial to attempt a Transformer-based QA solution. This leaves ample room for future improvement, and hints at the need for a completely different type of solution.
There are common semantics shared across text and images. Given a sentence in a source language, whether depicting the visual scene helps translation into a target language? Existing multimodal neural machine translation methods (MNMT) require triple
ts of bilingual sentence - image for training and tuples of source sentence - image for inference. In this paper, we propose ImagiT, a novel machine translation method via visual imagination. ImagiT first learns to generate visual representation from the source sentence, and then utilizes both source sentence and the imagined representation'' to produce a target translation. Unlike previous methods, it only needs the source sentence at the inference time. Experiments demonstrate that ImagiT benefits from visual imagination and significantly outperforms the text-only neural machine translation baselines. Further analysis reveals that the imagination process in ImagiT helps fill in missing information when performing the degradation strategy.
In this work, the effect of electric permittivity and thin layer thickness on the energy
gape has been studied in a system consisting of three thin layers by means of finding the
self action potential of charge carrier located in the central layer
of this system. Then, the
self action potential energy has been found by solving Schrödinger equation at the
extracted potential. This study shows that energy gape of PbI2 decreases with layer
thickness while it increases/decreases according to comparison between dielectric
permittivity values of central layer and counterparts of two neighborhood layers on both sides.
The displacement method of the important methods in stylistic studies and modern
linguistics; it is based onDepart from the familiar and ordinary language to create beauty
creative outlines a literary text by the poet and increase its luster, and i
ts showText
aesthetic statement and the possibility of the poet and his abilities shift in his words, which
the recipient is a matter of contemplation and interpretation Revealed in a metaphorical
sense, it stands on aesthetic and poetic language.
And here it highlights the role of the imagination in the production of diverse literary
images, since the poet called free rein to his imagination in the generation of meanings
andInnovation, and this is done in the familiar skip weave method, and breach of the
modest traditions between language users,
If the purpose of the distortion of the text is to create beauty and stylistic innovation
that leaves an impact on its recipients, which confirms Position
Poetic language; Art is not art until breathing shed from the standard, and even filled
his intention of violating the order and violation of norms,
That's what the beauty of D poets, literary texts semantic my style.
