Hard Attention
35 papers with code • 0 benchmarks • 0 datasets
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Libraries
Use these libraries to find Hard Attention models and implementationsMost implemented papers
Sequence-to-sequence Models for Cache Transition Systems
xiaochang13/CacheTransition-Seq2seq
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ACL 2018
In this paper, we present a sequence-to-sequence based approach for mapping natural language sentences to AMR semantic graphs.
Latent Alignment and Variational Attention
harvardnlp/var-attn
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NeurIPS 2018
This work considers variational attention networks, alternatives to soft and hard attention for learning latent variable alignment models, with tighter approximation bounds based on amortized variational inference.
Surprisingly Easy Hard-Attention for Sequence to Sequence Learning
sid7954/beam-joint-attention
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EMNLP 2018
In this paper we show that a simple beam approximation of the joint distribution between attention and output is an easy, accurate, and efficient attention mechanism for sequence to sequence learning.
Robust Sequence-to-Sequence Acoustic Modeling with Stepwise Monotonic Attention for Neural TTS
keonlee9420/Stepwise_Monotonic_Multihead_Attention
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In this paper, we propose a novel stepwise monotonic attention method in sequence-to-sequence acoustic modeling to improve the robustness on out-of-domain inputs.
Graph Representation Learning via Hard and Channel-Wise Attention Networks
dmlc/dgl
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To further reduce the requirements on computational resources, we propose the cGAO that performs attention operations along channels.
Neural Architectures for Nested NER through Linearization
We propose two neural network architectures for nested named entity recognition (NER), a setting in which named entities may overlap and also be labeled with more than one label.
Read, Highlight and Summarize: A Hierarchical Neural Semantic Encoder-based Approach
In this paper, we propose a method based on extracting the highlights of a document; a key concept that is conveyed in a few sentences.
Learning Texture Transformer Network for Image Super-Resolution
In this paper, we propose a novel Texture Transformer Network for Image Super-Resolution (TTSR), in which the LR and Ref images are formulated as queries and keys in a transformer, respectively.
AxFormer: Accuracy-driven Approximation of Transformers for Faster, Smaller and more Accurate NLP Models
amrnag/specialized-transformers
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We propose AxFormer, a systematic framework that applies accuracy-driven approximations to create optimized transformer models for a given downstream task.
A Hybrid Attention Mechanism for Weakly-Supervised Temporal Action Localization
Moreover, our temporal semi-soft and hard attention modules, calculating two attention scores for each video snippet, help to focus on the less discriminative frames of an action to capture the full action boundary.
