A Genre-Aware Attention Model to Improve the Likability Prediction of Books

Maharjan, Suraj; Montes, Manuel; González, Fabio A.; Solorio, Thamar

doi:10.18653/v1/d18-1375

Cited by 21 publications

(16 citation statements)

References 22 publications

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“…The computation of such an embedding can be seen as a form of feature selection, and as such, it can be applied to any set of features sharing the same representation. This applies to cases where features come from different domains as in multimodal tasks [78] or from different levels of a neural architecture [38] or where they simply represent different aspects of the input document [136]. Similarly, attention can also be exploited as an auxiliary task during training so that specific features can be modeled via a multitask setting.…”

Section: Uses Of Attentionmentioning

confidence: 99%

“…However, the input can also be other things, such as a juxtaposition of features or relevant aspects of the same textual element. For instance, Li et al [56] and Zadeh et al [78] considered the inputs composed of different sources, and in [136] and [139], the input represents different aspects of the same document. In that case, embeddings of the input can be collated together and fed into an attention model as multiple keys, as long as the embeddings share the same representation.…”

Section: A Input Representationmentioning

confidence: 99%

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Attention in Natural Language Processing

Galassi

Lippi

Torroni

2021

IEEE Trans. Neural Netw. Learning Syst.

451

130

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Attention is an increasingly popular mechanism used in a wide range of neural architectures. The mechanism itself has been realized in a variety of formats. However, because of the fast-paced advances in this domain, a systematic overview of attention is still missing. In this article, we define a unified model for attention architectures in natural language processing, with a focus on those designed to work with vector representations of the textual data. We propose a taxonomy of attention models according to four dimensions: the representation of the input, the compatibility function, the distribution function, and the multiplicity of the input and/or output. We present the examples of how prior information can be exploited in attention models and discuss ongoing research efforts and open challenges in the area, providing the first extensive categorization of the vast body of literature in this exciting domain.

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Section: Uses Of Attentionmentioning

confidence: 99%

Section: A Input Representationmentioning

confidence: 99%

Attention in Natural Language Processing

Galassi

Lippi

Torroni

2021

IEEE Trans. Neural Netw. Learning Syst.

451

130

View full text Add to dashboard Cite

show abstract

“…It emphasizes the reasonable allocation of limited computing power when facing problems ( Chaudhari et al, 2019 ). Due to the excellent effect, this mechanism has made breakthroughs in NLP ( Maharjan et al, 2018 ), and computer vision (CV) ( Li et al, 2019 ). At the same time, attention mechanism is also introduced in transportation research.…”

Section: Related Workmentioning

confidence: 99%

ST-AFN: a spatial-temporal attention based fusion network for lane-level traffic flow prediction

Shen

Zhang

et al. 2021

PeerJ Computer Science

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Traffic flow prediction is the foundation of many applications in smart cities, and the granular precision of traffic flow prediction has to be enhanced with refined applications. However, most of the existing researches cannot meet these requirements. In this paper, we propose a spatial-temporal attention based fusion network (ST-AFN), for lane-level precise prediction. This seq2seq model consists of three parts, namely speed process network, spatial encoder, and temporal decoder. In order to exploit the dynamic dependencies among lanes, attention mechanism blocks are embedded in those networks. The application of deep spatial-temporal information matrix results in progresses in term of reliability. Furthermore, a specific ground lane selection method is also proposed to ST-AFN. To evaluate the proposed model, four months of real-world traffic data are collected in Xiaoshan District, Hangzhou, China. Experimental results demonstrate that ST-AFN can achieve more accurate and stable results than the benchmark models. To the best of our knowledge, this is the first time that a deep learning method has been applied to forecast traffic flow at the lane level on urban ground roads instead of expressways or elevated roads.

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“…Then, attention scores were computed for these encodings. More recently, authors of [41] place an attention layer on top of several modality-specific feature encoding layers to model the importance of different modalities in book genre prediction. There are many other works [20,35,39,40] that leverage this technique, i.e.…”

Section: Related Workmentioning

confidence: 99%

Multimodal Data Fusion based on the Global Workspace Theory

Bao

Fountas

Olugbade

et al. 2020

Proceedings of the 2020 International Conference on Multimodal Interaction

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We propose a novel neural network architecture, named the Global Workspace Network (GWN), which addresses the challenge of dynamic and unspecified uncertainties in multimodal data fusion. Our GWN is a model of attention across modalities and evolving through time, and is inspired by the well-established Global Workspace Theory from the field of cognitive science. The GWN achieved average F1 score of 0.92 for discrimination between pain patients and healthy participants and average F1 score = 0.75 for further classification of three pain levels for a patient, both based on the multimodal EmoPain dataset captured from people with chronic pain and healthy people performing different types of exercise movements in unconstrained settings. In these tasks, the GWN significantly outperforms the typical fusion approach of merging by concatenation. We further provide extensive analysis of the behaviour of the GWN and its ability to address uncertainties (hidden noise) in multimodal data. CCS CONCEPTS • Computing methodologies → Machine learning algorithms; Neural networks.

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A Genre-Aware Attention Model to Improve the Likability Prediction of Books

Cited by 21 publications

References 22 publications

Attention in Natural Language Processing

Attention in Natural Language Processing

ST-AFN: a spatial-temporal attention based fusion network for lane-level traffic flow prediction

Multimodal Data Fusion based on the Global Workspace Theory

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