204 research outputs found
Reinforced Mnemonic Reader for Machine Reading Comprehension
In this paper, we introduce the Reinforced Mnemonic Reader for machine
reading comprehension tasks, which enhances previous attentive readers in two
aspects. First, a reattention mechanism is proposed to refine current
attentions by directly accessing to past attentions that are temporally
memorized in a multi-round alignment architecture, so as to avoid the problems
of attention redundancy and attention deficiency. Second, a new optimization
approach, called dynamic-critical reinforcement learning, is introduced to
extend the standard supervised method. It always encourages to predict a more
acceptable answer so as to address the convergence suppression problem occurred
in traditional reinforcement learning algorithms. Extensive experiments on the
Stanford Question Answering Dataset (SQuAD) show that our model achieves
state-of-the-art results. Meanwhile, our model outperforms previous systems by
over 6% in terms of both Exact Match and F1 metrics on two adversarial SQuAD
datasets.Comment: Published in 27th International Joint Conference on Artificial
Intelligence (IJCAI), 201
Expressing metaphorically, writing creatively: Metaphor identification for creativity assessment
Metaphor, which can implicitly express profound meanings and emotions, is a unique writing technique frequently used in human language. In writing, meaningful metaphorical expressions can enhance the literariness and creativity of texts. Therefore, the usage of metaphor is a significant impact factor when assessing the creativity and literariness of writing. However, little to no automatic writing assessment system considers metaphorical expressions when giving the score of creativity. For improving the accuracy of automatic writing assessment, this paper proposes a novel creativity assessment model that imports a token-level metaphor identification method to extract metaphors as the indicators for creativity scoring. The experimental results show that our model can accurately assess the creativity of different texts with precise metaphor identification. To the best of our knowledge, we are the first to apply automatic metaphor identification to assess writing creativity. Moreover, identifying features (e.g., metaphors) that influence writing creativity using computational approaches can offer fair and reliable assessment methods for educational settings
Metaphor research in the 21st century : a bibliographic analysis
Metaphor is widely used in human communication. The cohort of scholars studying metaphor in various fields is continuously growing, but very few work has been done in bibliographical analysis of metaphor research. This paper examines the advancements in metaphor research from 2000 to 2017. Using data retrieved from Microsoft Academic Graph and Web of Science, this paper makes a macro analysis of metaphor research, and expounds the underlying patterns of its development. Taking into consideration sub-fields of metaphor research, the internal analysis of metaphor research is carried out from a micro perspective to reveal the evolution of research topics and the inherent relationships among them. This paper provides novel insights into the current state of the art of metaphor research as well as future trends in this field, which may spark new research interests in metaphor from both linguistic and interdisciplinary perspectives. © 2020, ComSIS Consortium. All rights reserved
Expanding Language-Image Pretrained Models for General Video Recognition
Contrastive language-image pretraining has shown great success in learning
visual-textual joint representation from web-scale data, demonstrating
remarkable "zero-shot" generalization ability for various image tasks. However,
how to effectively expand such new language-image pretraining methods to video
domains is still an open problem. In this work, we present a simple yet
effective approach that adapts the pretrained language-image models to video
recognition directly, instead of pretraining a new model from scratch. More
concretely, to capture the long-range dependencies of frames along the temporal
dimension, we propose a cross-frame attention mechanism that explicitly
exchanges information across frames. Such module is lightweight and can be
plugged into pretrained language-image models seamlessly. Moreover, we propose
a video-specific prompting scheme, which leverages video content information
for generating discriminative textual prompts. Extensive experiments
demonstrate that our approach is effective and can be generalized to different
video recognition scenarios. In particular, under fully-supervised settings,
our approach achieves a top-1 accuracy of 87.1% on Kinectics-400, while using
12 times fewer FLOPs compared with Swin-L and ViViT-H. In zero-shot
experiments, our approach surpasses the current state-of-the-art methods by
+7.6% and +14.9% in terms of top-1 accuracy under two popular protocols. In
few-shot scenarios, our approach outperforms previous best methods by +32.1%
and +23.1% when the labeled data is extremely limited. Code and models are
available at https://aka.ms/X-CLIPComment: Accepted by ECCV2022, Ora
Reconstructing the earliest known composite-tiled roofs from the Chinese Loess Plateau
The origins of composite tiles, one of the oldest forms of roofing, are still unclear. This study is based on a set of over 5000 clay tile fragments excavated from a single context in the Qiaocun site on the Chinese Loess Plateau, dated to ~ 2400-2200 BCE (Early Longshan Period). By combining morphological measurement statistics, 3D modeling, computer-based simulations, and reference to historical and archaeological records, we reconstruct the earliest known composite-tile roofing techniques and demonstrate that tile production was under a low-level standardization, with manual control forming a key agent during the roofing process. The quantitative study of the composite roof tiles from Qiaocun was then placed in its archaeological context and compared with other sites on the Loess Plateau. It was found that tile-roofed buildings were, by necessity, community projects. Such structures served as nodes in larger social communication networks; additionally, their appearance was linked to intensified social complexity in public affairs during the Longshan Period. The invention of clay tiles was associated with the inception of thick rammed-earth walls which had sufficient strength to serve as load-bearing structures for heavy tiled roofs. The roof tiles excavated from Qiaocun site indicate that the Loess Plateau was a key center for the origin and spread of composite tiles and related roofing and construction methods, suggesting a Longshan-Western Zhou tradition of roofing techniques in East Asia
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