![]() ![]() Unlike some other models, DeBERTa accounts for words’ absolute positions in the language modeling process. DeBERTa uses both the content and position information of context words for MLM, such that it’s able to recognize “store” and “mall” in the sentence “a new store opened beside the new mall” play different syntactic roles, for example. It’ll be released in open source and integrated into the next version of Microsoft’s Turing natural language representation model, which supports products like Bing, Office, Dynamics, and Azure Cognitive Services.ĭeBERTa is pretrained through masked language modeling (MLM), a fill-in-the-blank task where a model is taught to use the words surrounding a masked “token” to predict what the masked word should be. DeBERTa isn’t new - it was open-sourced last year - but the researchers say they trained a larger version with 1.5 billion parameters (i.e., the internal variables that the model uses to make predictions). The Google team hasn’t yet detailed the improvements that led to its model’s record-setting performance on SuperGLUE, but the Microsoft researchers behind DeBERTa detailed their work in a blog post published earlier this morning. Each worker, paid an average of $23.75 an hour, completed a short training phase before annotating up to 30 samples of selected test sets using instructions and an FAQ page. To establish human performance baselines, the researchers drew on existing literature for WiC, MultiRC, RTE, and ReCoRD and hired crowdworker annotators through Amazon’s Mechanical Turk platform. Moreover, it doesn’t include all forms of gender or social bias, making it a coarse measure of prejudice. However, the researchers note that this measure has limitations in that it offers only positive predictive value: While a poor bias score is clear evidence that a model exhibits gender bias, a good score doesn’t mean the model is unbiased. SuperGLUE also attempts to measure gender bias in models with Winogender Schemas, pairs of sentences that differ only by the gender of one pronoun in the sentence. It’s designed to be an improvement on the Turing Test. Winograd Schema Challenge (WSC) is a task where models, given passages from fiction books, must answer multiple-choice questions about the antecedent of ambiguous pronouns.Word-in-Context (WiC) provides models two text snippets and a polysemous word (i.e., word with multiple meanings) and requires them to determine whether the word is used with the same sense in both sentences.Recognizing Textual Entailment (RTE) challenges natural language models to identify whenever the truth of one text excerpt follows from another text excerpt.Reading Comprehension with Commonsense Reasoning Dataset (ReCoRD) has models predict masked-out words and phrases from a list of choices in passages from CNN and the Daily Mail, where the same words or phrases might be expressed using multiple different forms, all of which are considered correct.A model must predict which answers are true and false. Multi-Sentence Reading Comprehension (MultiRC) is a question-answer task where each example consists of a context paragraph, a question about that paragraph, and a list of possible answers.Choice of plausible alternatives (COPA) provides a premise sentence about topics from blogs and a photography-related encyclopedia from which models must determine either the cause or effect from two possible choices.CommitmentBank (CB) tasks models with identifying a hypotheses contained within a text excerpt from sources including the Wall Street Journal and determining whether the hypothesis holds true.The questions come from Google users, who submit them via Google Search. Boolean Questions (BoolQ) requires models to respond to a question about a short passage from a Wikipedia article that contains the answer.It comprises eight language understanding tasks drawn from existing data and accompanied by a performance metric as well as an analysis toolkit. Watch on-demand sessions today.Īs the researchers wrote in the paper introducing SuperGLUE, their benchmark is intended to be a simple, hard-to-game measure of advances toward general-purpose language understanding technologies for English. ![]() Learn the critical role of AI & ML in cybersecurity and industry specific case studies. ![]()
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