Deep Learning, NLP, NMT, AI, ML
This article was published as a part of the Data Science Blogathon. Introduction Did you ever have a thought or a wish that you just wanted to write two lines of an essay or a journal and the computer just wrote the rest for you? If yes, then GPT-3 is the answer for you. Baffled? So are the people who got their hands on the GPT-3. Every field in AI is making advancements and NLP & Deep learning are such […]
Read moreNatural language understanding (NLU) is one of the longest running goals in AI, and SuperGLUE is currently among the most challenging benchmarks for evaluating NLU models. The benchmark consists of a wide range of NLU tasks, including question answering, natural language inference, co-reference resolution, word sense disambiguation, and others. Take the causal reasoning task (COPA in Figure 1) as an example. Given the premise “the child became immune to the disease” and the question “what’s the cause for this?,” the […]
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Introduction File manipulation is one of the most important skills to master in any programming language, and doing it correctly is of utmost importance. Making a mistake could cause an issue in your program, other programs running on the same system, and even the system itself. Possible errors can occur due to the parent directory not existing, or by other programs changing files in the file system at the same time, creating something that is called a race condition. A […]
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Introduction Pandas provides a huge range of methods and functions to manipulate data, including merging DataFrames. Merging DataFrames allows you to both create a new DataFrame without modifying the original data source or alter the original data source. If you are familiar with the SQL or a similar type of tabular data, you probably are familiar with the term join, which means combining DataFrames to form a new DataFrame. If you are a beginner it can be hard to fully […]
Read moreDecember 8, 2020 By: Ankit Arun, Soumya Batra, Vikas Bhardwaj, Ashwini Challa, Pinar Donmez, Peyman Heidari, Hakan Inan, Shashank Jain, Anuj Kumar, Shawn Mei, Karthik Mohan, Michael White Abstract Natural language generation (NLG) is a critical component in conversational systems, owing to its role of formulating a correct and natural text response. Traditionally, NLG components have been deployed using template-based solutions. Although neural network solutions recently developed in the research community have been shown to provide several benefits, deployment of […]
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The advent of the Transformer can arguably be described as a driving force behind many of the recent advances in natural language processing. However, despite their sizeable performance improvements, as recently shown, the model is severely over-parameterized, being parameter inefficient and computationally expensive to train… Inspired by the success of parameter-sharing in pretrained deep contextualized word representation encoders, we explore parameter-sharing methods in Transformers, with a specific focus on encoder-decoder models for sequence-to-sequence tasks such as neural machine translation. We […]
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Semi-supervised learning refers to algorithms that attempt to make use of both labeled and unlabeled training data. Semi-supervised learning algorithms are unlike supervised learning algorithms that are only able to learn from labeled training data. A popular approach to semi-supervised learning is to create a graph that connects examples in the training dataset and propagates known labels through the edges of the graph to label unlabeled examples. An example of this approach to semi-supervised learning is the label spreading algorithm […]
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Introduction In this article we’ll be taking a look at Global and Non-Local Variables in Python and how you to use them to avoid issues when writing code. We’ll be starting off with a brief primer on variable scopes before we launch into the how and why of using global and non-local variables in your own functions. Scopes in Python Before we can get started, we first have to touch on scopes. For those of you who are less familiar, […]
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Large pre-trained language models are capable of generating realistic text. However, controlling these models so that the generated text satisfies lexical constraints, i.e., contains specific words, is a challenging problem… Given that state-of-the-art language models are too large to be trained from scratch in a manageable time, it is desirable to control these models without re-training them. Methods capable of doing this are called plug-and-play. Recent plug-and-play methods have been successful in constraining small bidirectional language models as well as […]
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Semi-supervised learning refers to algorithms that attempt to make use of both labeled and unlabeled training data. Semi-supervised learning algorithms are unlike supervised learning algorithms that are only able to learn from labeled training data. A popular approach to semi-supervised learning is to create a graph that connects examples in the training dataset and propagate known labels through the edges of the graph to label unlabeled examples. An example of this approach to semi-supervised learning is the label propagation algorithm […]
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