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Register a new userTransferring Representations of Logical Connectives
نقل تمثيلات الديون المنطقية
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في خطوط أنابيب معالجة اللغة الطبيعية الحديثة، فمن الممارسات الشائعة أن تعزز "نموذج لغة تابعة له على جثة كبيرة من النص، ثم إلى Finetune '' من التمثيلات التي تم إنشاؤها من خلال الاستمرار في تدريبهم على مهمة استنصائية نصية تمييزية.ومع ذلك، ليس من الواضح فورا ما إذا كان المعنى المنطقي ضروري لنموذج الاستقصاء المنطقي يتم التقاطه بواسطة نماذج اللغة في هذه النموذج.نحن ندرس هذه الوصفة المؤقتة الوصيفة مع نماذج اللغة التي تم تدريبها على مهمة استقامة اللغة الاصطناعية، والنتائج الحالية على مجموعات الاختبار معرفة نماذج التحقيق "بديهيا" منطق الدرس الأول.
In modern natural language processing pipelines, it is common practice to pretrain'' a generative language model on a large corpus of text, and then to finetune'' the created representations by continuing to train them on a discriminative textual inference task. However, it is not immediately clear whether the logical meaning necessary to model logical entailment is captured by language models in this paradigm. We examine this pretrain-finetune recipe with language models trained on a synthetic propositional language entailment task, and present results on test sets probing models' knowledge of axioms of first order logic.
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Since language is a natural concrete phenomenon, it became a fact that
language has been a matter of induction by making it go through
experiment in attempt to attain the rules that can take hold of the
language's partial phenomena and organize th
em in general regulations,
and if we observed the linguistic matter which grammarians investigated,
we could find that their work involved both the complete induction and
the incomplete induction according to Aristotle's induction method, but
they disagreed with this method in accordance with the nature of Islamic
method of thinking; therefore they had their own distinguishing method
of induction.
Determining whether two documents were composed by the same author, also known as authorship verification, has traditionally been tackled using statistical methods. Recently, authorship representations learned using neural networks have been found to
outperform alternatives, particularly in large-scale settings involving hundreds of thousands of authors. But do such representations learned in a particular domain transfer to other domains? Or are these representations inherently entangled with domain-specific features? To study these questions, we conduct the first large-scale study of cross-domain transfer for authorship verification considering zero-shot transfers involving three disparate domains: Amazon reviews, fanfiction short stories, and Reddit comments. We find that although a surprising degree of transfer is possible between certain domains, it is not so successful between others. We examine properties of these domains that influence generalization and propose simple but effective methods to improve transfer.
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