Towards Two-Dimensional Sequence to Sequence Model in Neural Machine Translation

Bahar, Parnia; Brix, Christopher; Ney, Hermann

doi:10.18653/v1/d18-1335

Cited by 25 publications

(25 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When an LSTM cell is recursively utilized in a 1D array As shown in Fig. 5, a 2D LSTM network can be realized when the LSTM cell is recursively in a 2D mesh form [13]. Each LSTM cell utilizes the hidden and cell states from the two neighboring cells in the left and below positions in the mesh, and its states are delivered to its neighboring cells in the right and top positions.…”

Section: B Framework Architecture and Methodsmentioning

confidence: 99%

Applying Deep-Learning-Based Computer Vision to Wireless Communications: Methodologies,Opportunities, and Challenges

Tian¹,

Pan²,

Alouini³

2020

Preprint

View full text Add to dashboard Cite

<div>Deep learning (DL) has seen great success in the computer vision (CV) field, and related techniques have been used in security, healthcare, remote sensing, and many other fields. As a parallel development, visual data has become universal in daily life, easily generated by ubiquitous low-cost cameras. Therefore, exploring DL-based CV may yield useful information about objects, such as their number, locations, distribution, motion, etc. Intuitively, DL-based CV can also facilitate and improve the designs of wireless communications, especially in dynamic network scenarios. However, so far, such work is rare in the literature. The primary purpose of this article, then, is to introduce ideas about applying DL-based CV in wireless communications to bring some novel degrees of freedom to both theoretical research and engineering applications. To illustrate how DL-based CV can be applied in wireless communications, an example of using a DL-based CV with a millimeter-wave (mmWave) system is given to realize optimal mmWave multiple-input and multiple-output (MIMO) beamforming in mobile scenarios. In this example, we propose a framework to predict future beam indices from previously observed beam indices and images of street views using ResNet, 3-dimensional ResNext, and a long short-term memory network. The experimental results show that our frameworks achieve much higher accuracy than the baseline method, and that visual data can significantly improve the performance of the MIMO beamforming system. Finally, we discuss the opportunities and challenges of applying DL-based CV in wireless communications.</div>

show abstract

Section: B Framework Architecture and Methodsmentioning

confidence: 99%

Applying Deep-Learning-Based Computer Vision to Wireless Communications: Methodologies,Opportunities, and Challenges

Tian¹,

Pan²,

Alouini³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…As given in Fig. 5, 2D LSTM network can be realized when the LSTM cell is recursively in a 2D mesh form [13]. Each LSTM cell utilizes the hidden and cell states from the two neighboring cells in the left and below positions in the mesh.…”

Section: B Framework Architecture and Methodsmentioning

confidence: 99%

Applying Deep-Learning-Based Computer Vision to Wireless Communications: Methodologies,Opportunities, and Challenges

Tian¹,

Pan²,

Alouini³

2020

Preprint

View full text Add to dashboard Cite

<div>Deep learning (DL) has obtained great success in computer vision (CV) field, and the related techniques have been widely used in security, healthcare, remote sensing, etc. On the other hand, visual data is universal in our daily life, which is easily generated by prevailing but low-cost cameras. Therefore, DL-based CV can be explored to obtain and forecast some useful information about the objects, e.g., the number, locations, distribution, motion, etc. Intuitively, DL-based CV can facilitate and improve the designs of wireless communications, especially in dynamic network scenarios. However, so far, it is rare to see such kind of works in the existing literature. Then, the primary purpose of this article is to introduce ideas of applying DL-based CV in wireless communications to bring some novel degrees of freedom for both theoretical researches and engineering applications. To illustrate how DL-based CV can be applied in wireless communications, an example of using DL-based CV to millimeter wave (mmWave) system is given to realize optimal mmWave multiple-input and multiple-output (MIMO) beamforming in mobile scenarios. In this example, we proposed a framework to predict the future beam indices from the previously-observed beam indices and images of street views by using ResNet, 3-dimensional ResNext, and long short term memory network. Experimental results show that our frameworks can achieve much higher accuracy than the baseline method, and visual data can help significantly improve the performance of MIMO beamforming system. Finally, we discuss the opportunities and challenges of applying DL-based CV in wireless communications.</div>

show abstract

“…The 2D alternating RNN is a novel translation architecture in development by the MLLP group. This architecture approaches the machine translation problem with a two-dimensional view, much in the same manner as Kalchbrenner et al (2015); Bahar et al (2018) and Elbayad et al (2018). This view is based on the premise that translation is fundamentally a two-dimensional problem, where each word of the target sentence can be explained in some way by all the words in the source sentence.…”

Section: D Alternating Rnnmentioning

confidence: 99%

The MLLP-UPV Spanish-Portuguese and Portuguese-Spanish Machine Translation Systems for WMT19 Similar Language Translation Task

Baquero-Arnal¹,

Iranzo-Sánchez²,

Civera³

et al. 2019

Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)

View full text Add to dashboard Cite

This paper describes the participation of the MLLP research group of the Universitat Politècnica de València in the WMT 2019 Similar Language Translation Shared Task. We have submitted systems for the Portuguese ↔ Spanish language pair, in both directions. They are based on the Transformer architecture as well as on a novel architecture called 2D alternating RNN. Both systems have been domain adapted through fine-tuning that has been shown to be very effective.

show abstract

Towards Two-Dimensional Sequence to Sequence Model in Neural Machine Translation

Cited by 25 publications

References 16 publications

Applying Deep-Learning-Based Computer Vision to Wireless Communications: Methodologies,Opportunities, and Challenges

Applying Deep-Learning-Based Computer Vision to Wireless Communications: Methodologies,Opportunities, and Challenges

Applying Deep-Learning-Based Computer Vision to Wireless Communications: Methodologies,Opportunities, and Challenges

The MLLP-UPV Spanish-Portuguese and Portuguese-Spanish Machine Translation Systems for WMT19 Similar Language Translation Task

Contact Info

Product

Resources

About