The Natural Language Decathlon is a multitask challenge that spans 10 tasks. It’s been a hot minute (if you’re in the northern hemisphere, a very hot minute), but SuperGLUE v2.0 is out! To do well on SQuAD 2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering.Note: I use problem and task interchangeably in this post, as each problem sort of defined around a task that a model needs to perform, so please don’t get confused by that.In case of NLP, even if the output format is predetermined, the dimensions cannot be fixed. Therefore, we can calculate the loss/offset from ground truth at a pixel. But our real world is not a simple one. In addition, the dataset contains 8,841,823 passages — extracted from 3,563,535 web documents retrieved by Bing — that provide the information necessary for curating the natural language answers.To address these weaknesses, SQuAD 2.0 combined existing SQuAD data with over 50,000 unanswerable questions written adversarial-ly by crowd workers to look similar to answerable ones. • The work is original. • The work is likely to make big impact on NLP research. Though the concept is fairly simple - they use character-level LSTMs to model sentences - what makes this paper particularly interesting is their focus on domains. In their model, they treat everything as question answering, which seems very nice to me. SuperGLUE is available at super.gluebenchmark.com. We also cap the number of times to count each word based on the highest number of times it appears in any reference sentence, which helps us avoid unnecessary repetition of words — S4 scenario.Even though learning biases has more to do with training data and less to do with model architecture, I feel, having a metric for capturing biases or a standard for biases would be a good practice to adapt.Before we dive into the details and nuances of various metrics out there, I want to talk here not only about why it matters to have a good metric, but also what a good metric is — in my opinion — just in case you don’t make it till the end of the article.In this type of metric test, a question and a text are given, and the model needs to predict the answer from the given text.In most of deep learning tasks — classification, regression, or image generation — it is pretty easy and straight forward to evaluate the performance of a model because of the solution space is finite/not very large. This paper recaps lessons learned from the GLUE benchmark and presents SuperGLUE, a new benchmark styled after GLUE with a new set of more difficult language understanding tasks, improved resources, and a new public leaderboard. level.As the name suggests, it was originally used to evaluate translations from one language to another.It is a large scale dataset focused on machine reading comprehension. If you want to understand each task in detail, please go through the papers, linked in the reference section, at the end of the post.GLUE and SuperGLUE evaluate the performance of a model on a collection of tasks, rather than a single one to get a holistic view on the performance. From this point forward, we expect the benchmark to be stable.
That is when these metrics come in handy.This does not seem to be correct; we should not be scoring S3 and S4 so highly. So how do we decide which model to use for a particular problem? The following table shows the tasks included in DecaNLP: The following image shows how different tasks are formatted into QA in DecaNLP datasets: GLUE. SuperGLUE is a new benchmark styled after original GLUE benchmark with a set of more difficult language understanding tasks, improved resources, and a new public leaderboard.. 1 Introduction In the past year, there has been notable progress across many natural language processing (NLP) The real world is full of biases and we don’t want our solutions to be biased as it can have inconceivable consequences.The dataset started off focusing on QnA but has since evolved to focus on any problem related to search.In case of regression, the number of values in the output is fixed and hence loss can be calculated for each value, even though the possibilities for each value is infinite.If I give you a text from the Harry Potter series and ask you “Why were Percy Jackson and his friends in trouble”?
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