Deep Learning, NLP, NMT, AI, ML
Articles About Natural Language Processing
News š¤ Transformers 4.6 adds VISION! Transformers v4.6 is our first release dedicated to computer vision! 1ļøā£ CLIP from OpenAI, for Image-Text similarity or Zero-Shot Image classification 2ļøā£ ViT from GoogleAI 3ļøā£ DeiT from FacebookAI Try SOTA image classification with ViT and DeiT on the Model Hub! š¤ Datasets 1.6 š¤Datasets v1.6 brings
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10 Jun21 Issue #134 ā A Targeted Attack on Black-Box Neural MT in Robustness, The Neural MT Weekly Author: Dr. Karin Sim, Machine Translation Scientist @ Iconic Introduction Last week we looked at how neural machine translation (NMT) systems are naturally susceptible to gender bias. In todayās blog post we look at the vulnerability of an NMT system to targeted attacks, which could result in unsolicited or harmful translations. Specifically we report on work by Xu et al., 2021, which […]
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This article was published as a part of theĀ Data Science Blogathon Have you ever thought of generating questions from the SRT files of Movies? I donāt know if we can use this but it is pretty exciting when I came to know as a beginner that we can do that. What is SRT? In simple terms, the subtitles you see in Amazon Prime, Netflix, Hotstar, HBO, etc are saved in a text file with (.srt) extension with timestamps. The timestamp […]
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This article was published as a part of theĀ Data Science Blogathon Introduction We all have made our resumes at some point in time. In a resume, we try to include important facts about ourselves like our education, work experience, skills, etc. Let us work on a resume dataset today.Ā The text we put in our resume speaks a lot about us. For example, our education, skills, work experience, and other random information about us are all present in a resume. […]
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June 6, 2021 By: Lingfei Wu, Yu Chen, Heng Ji, Yunyao Li Abstract This tutorial of Deep Learning on Graphs for Natural Language Processing (DLG4NLP) is timely for the computational linguistics community, and covers relevant and interesting topics, including automatic graph construction for NLP, graph representation learning for NLP, various advanced GNN based models (e.g., graph2seq, graph2tree, and graph2graph) for NLP, and the applications of GNNs in various NLP tasks (e.g., machine translation, natural language generation, information extraction and semantic […]
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This article was published as a part of theĀ Data Science Blogathon Overview: From the advent of modern Science & Technology, Researchers are trying to find a far better solution for the real-time problems we face in our day-to-day life. Technologies like Machine learning, AI, deep learning, and NLP give dynastic and diplomatic solutions in various growing sectors like finance, healthcare, and therefore the retail industry, etc, and making it more and more reliable in production. Will Machine Learning change the […]
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This article was published as a part of theĀ Data Science Blogathon According to Accenture ā ā57% of the Businesses agree that chatbots deliver larger ROI with minimal effort.ā Table of Contents : 1. Whatās a chatbot? 2. A dive into types of chatbots 3.What are the top platforms to build a chatbot? 4. What are the top Frameworks for building a chatbot? 5. The Algorithm to build a Chatbot. 6. Tips to follow before building your first chatbot 7. Top […]
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02 Jun21 Issue #133 ā Evaluating Gender Bias in MT in Evaluation, The Neural MT Weekly Author: Akshai Ramesh, Machine Translation Scientist @ Iconic Introduction We often tend to personify aspects of life that may vary based upon the beholderās interpretation. There are plenty of examples for this ā āMother Earthā, Doctor (Men), Cricketer (Men), Nurse(Woman), Cook(Woman), etc. The MT systems are trained with a large amount of parallel corpus which encodes this social bias. If that is the case, […]
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This article was published as a part of theĀ Data Science Blogathon BERT is too kind ā so this article will be touching on BERT and sequence relationships! Abstract A significant portion of NLP relies on the connection in highly-dimensional spaces. Typically an NLP processing will take any text, prepare it to generate a tremendous vector/array rendering said text ā then make certain transformations. Itās a highly-dimensional charm. At an exceptional level, thereās not much extra to it. We require to […]
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This article was published as a part of theĀ Data Science Blogathon Introduction Natural Language Processing (NLP) is a subpart of Artificial Intelligence that uses algorithms to understand and process human language. Various computational methods are used to process and analyze human language and a wide variety of real-life problems are solved using Natural Language Processing. (Source: Kaggle.com) UsingĀ Natural Language Processing, we use machines by making them understand how human language works. Basically, we use text data and make computers analyze […]
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