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Multimodal Machine Translation

Jiatong Liu
2021 IEEE Access  
Therefore, two attention mechanisms are simultaneously used to capture text and image contexts for translation.  ...  As the scale of data is getting larger and larger and deep neural network models are widely used, pre-training technology has been widely used and has achieved remarkable results, but it has also received  ...  Chen for their help in this work.  ... 
doi:10.1109/access.2021.3115135 fatcat:d2anaeg3qnarpfmlc4eap2urrm

Deep Learning, Natural Language Processing, and Explainable Artificial Intelligence in the Biomedical Domain [article]

Milad Moradi, Matthias Samwald
2022 arXiv   pre-print
Deep learning methods are then described in Section 2.  ...  Deep learning neural networks explained in this section have been widely used to implement powerful NLP systems for a wide variety of applications such as Information retrieval, named entity recognition  ...  In this example, a bi-RNN is used for the encoder and a forward RNN is used for the decoder.  ... 
arXiv:2202.12678v2 fatcat:4nv42mbpuveb7euxkr4b6ojuxi

A Unified Approach of Detecting Misleading Images via Tracing its Instances on Web and Analysing its Past Context for the Verification of Content [article]

Deepika Varshney, Dinesh Kumar Vishwakarma
2021 arXiv   pre-print
It also demonstrates the efficiency of our proposed approach and features using both Machine and Deep Learning Model (Bi-directional LSTM).  ...  In this paper, we investigated machine learning-based as well as deep learning-based approaches utilized to verify misleading multimedia content, where the available image traces are used to identify the  ...  an image via tracing it on an image search engine and then collecting its past instances to retrieve the relevant crucial knowledge for prediction using both deep and machine learning models.  Prominently  ... 
arXiv:2109.09929v1 fatcat:6sv3tv4o4jfsjjkh5aolxle3eu

Brain Informatics

Ning Zhong, Jeffrey M. Bradshaw, Jiming Liu, John G. Taylor
2011 IEEE Intelligent Systems  
New kinds of BI methods and global research communities will emerge to develop a platform on the intelligent Web and knowledge grids that enable high-speed, distributed, large-scale analysis and computations  ...  and radically B rain informatics (BI) is an emerging interdisciplinary and multidisciplinary research fi eld that focuses on studying the mechanisms underlying the human information processing system  ...  Three Aspects of Brain Informatics Studies Three aspects of BI studies deserve closer attention: systematic investigations for complex brain science problems, new information technologies for supporting  ... 
doi:10.1109/mis.2011.83 fatcat:rewrtcysqjdgjdjfhbybtd6w3a

A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis [article]

Xiaozheng Xie, Jianwei Niu, Xuefeng Liu, Zhengsu Chen, Shaojie Tang, Shui Yu
2020 arXiv   pre-print
In this survey, we summarize the current progress on integrating medical domain knowledge into deep learning models for various tasks, such as disease diagnosis, lesion, organ and abnormality detection  ...  they pay particular attention to.  ...  A dual-attention model is designed to facilitate the high-level interaction of semantic information and visual information.  ... 
arXiv:2004.12150v3 fatcat:2cqumcjkizgivmo67reznxacie

Hierarchical Network Emotional Assistance Mechanism for Emotion Cause Extraction

Yu Wang, Bingjie Wei, Shuhua Ruan, Xingshu Chen, Haizhou Wang, Aleksandar Jevremovic
2022 Security and Communication Networks  
This method uses a hierarchical network composed of bidirectional gated recurrent units, attention mechanism, and graph convolutional networks to capture clause context information, deep semantic information  ...  Thus, a model of the deep neural network combined with the emotional assistance mechanism is established.  ...  Different weight matrices are used for context propagation according to different edges.  ... 
doi:10.1155/2022/3597771 fatcat:rq4emposbve2lfpro325yva2za

Fine-Grained Chemical Entity Typing with Multimodal Knowledge Representation [article]

Chenkai Sun, Weijiang Li, Jinfeng Xiao, Nikolaus Nova Parulian, ChengXiang Zhai, Heng Ji
2021 arXiv   pre-print
Automated knowledge discovery from trending chemical literature is essential for more efficient biomedical research.  ...  cross-modal attention to learn effective representation of text in the chemistry domain.  ...  of incorporating non-local features, which may have added additional useful information for entity type disambiguation.  ... 
arXiv:2108.12899v1 fatcat:cib35n544vdo5pj34ztrm7jowy

DeepAnalyze: Learning to Localize Crashes at Scale [article]

Manish Shetty, Chetan Bansal, Suman Nath, Sean Bowles, Henry Wang, Ozgur Arman, Siamak Ahari
2021 arXiv   pre-print
These insights enable us to develop DeepAnalyze, a novel multi-task sequence labeling approach for identifying blamed frames in stack traces.  ...  The analysis provides valuable insights on where and how the crashes happen and what methods to blame for the crashes.  ...  To the best of our knowledge, DeepAnalyze is the first effort to use MTL in context of debugging.  ... 
arXiv:2109.14326v2 fatcat:47g3c3hlwrbv7mellrsefltfse

Recent Trends in Deep Learning Based Open-Domain Textual Question Answering Systems

Zhen Huang, Shiyi Xu, Minghao Hu, Xinyi Wang, Jinyan Qiu, Yongquan Fu, Yuncai Zhao, Yuxing Peng, Changjian Wang
2020 IEEE Access  
Open-domain textual question answering (QA), which aims to answer questions from large data sources like Wikipedia or the web, has gained wide attention in recent years.  ...  INDEX TERMS Open-domain textual question answering, deep learning, machine reading comprehension, information retrieval.  ...  BiDAF [87] and Multi-Perspective Matching [95] leveraged Bi-LSTM for semantic information aggregation.  ... 
doi:10.1109/access.2020.2988903 fatcat:po4euxfronf3pob52qc2wcgrre

Evolution of Semantic Similarity – A Survey [article]

Dhivya Chandrasekaran, Vijay Mago
2021 arXiv   pre-print
This survey article traces the evolution of such methods, categorizing them based on their underlying principles as knowledge-based, corpus-based, deep neural network-based methods, and hybrid methods.  ...  The versatility of natural language makes it difficult to define rule-based methods for determining semantic similarity measures.  ...  and Andrew Heppner for their feedback and revisions on this publication.  ... 
arXiv:2004.13820v2 fatcat:fh7jkq7cyvczdnxarhscya2u4u

A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations [article]

Md. Ashraful Islam, Mir Mahathir Mohammad, Sarkar Snigdha Sarathi Das, Mohammed Eunus Ali
2020 arXiv   pre-print
To the best of our knowledge, this work is the first comprehensive survey of all major deep learning-based POI recommendation works.  ...  for a user.  ...  [77] uses an attention mechanism designed for utilizing spatiotemporal information.  ... 
arXiv:2011.10187v1 fatcat:3uampnqerfdvnpuzrxcrsjviwq

Unbox the Blackbox: Predict and Interpret YouTube Viewership Using Deep Learning [article]

Jiaheng Xie, Xiao Liu
2022 arXiv   pre-print
Following the design-science paradigm, we propose a novel interpretable IT system, Precise Wide and Deep Learning (PrecWD), to precisely interpret viewership prediction.  ...  Predicting video viewership is a top priority for content creators and video-sharing sites. Content creators live on such predictions to maximize influences and minimize budgets.  ...  Perceived usefulness, ease of use, and usage of information technology: A replication. MIS quarterly. 1992 Jun 1:227-47.  ... 
arXiv:2101.01076v5 fatcat:wzbxm32ndvespfnlgngawqfkyq

SoftNER: Mining Knowledge Graphs From Cloud Incidents [article]

Manish Shetty, Chetan Bansal, Sumit Kumar, Nikitha Rao, Nachiappan Nagappan
2021 arXiv   pre-print
First, we build a novel multi-task learning based BiLSTM-CRF model which leverages not just the semantic context but also the data-types for extracting factual information in the form of named entities  ...  Lastly, using the knowledge extracted by SoftNER, we are able to build accurate models for applications such as incident triaging and recommending entities based on their relevance to incident titles.  ...  We leverage syntactic pattern extractors for bootstrapping the training data. Further, we incorporate a novel multi-task BiLSTM-CRF deep learning model with an attention mechanism.  ... 
arXiv:2101.05961v2 fatcat:envypijvyvej3lieo3yqudf6ri

Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond [article]

Zhuosheng Zhang, Hai Zhao, Rui Wang
2020 arXiv   pre-print
With the burst of deep neural networks and the evolution of contextualized language models (CLMs), the research of MRC has experienced two significant breakthroughs.  ...  We depict the simple but widely-used matching attention M in Figure 8 -(b) for example, whose formulation is further described in §5.6.3 for detailed reference.  ...  Knowledge Injection. Extra knowledge can be easily incorporated into CLMs by both embedding fusion and masking.  ... 
arXiv:2005.06249v1 fatcat:htdq7hk6mrghvknwbkchgdioku

Knowledgeable Reader: Enhancing Cloze-Style Reading Comprehension with External Commonsense Knowledge [article]

Todor Mihaylov, Anette Frank
2018 arXiv   pre-print
Instead of relying only on document-to-question interaction or discrete features as in prior work, our model attends to relevant external knowledge and combines this knowledge with the context representation  ...  By including knowledge explicitly, our model can also provide evidence about the background knowledge used in the RC process.  ...  We thank the reviewers for their helpful questions and comments.  ... 
arXiv:1805.07858v1 fatcat:ta2ii3sxafhgnmvhoonubo2lt4
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