Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userTransferring Representations of Logical Connectives
نقل تمثيلات الديون المنطقية
183
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
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.
References used
https://aclanthology.org/
rate research
Read More
Natural language processing (NLP) is often the backbone of today's systems for user interactions, information retrieval and others. Many of such NLP applications rely on specialized learned representations (e.g. neural word embeddings, topic models)
that improve the ability to reason about the relationships between documents of a corpus. Paired with the progress in learned representations, the similarity metrics used to compare representations of documents are also evolving, with numerous proposals differing in computation time or interpretability. In this paper we propose an extension to a specific emerging hybrid document distance metric which combines topic models and word embeddings: the Hierarchical Optimal Topic Transport (HOTT). In specific, we extend HOTT by using context-enhanced word representations. We provide a validation of our approach on public datasets, using the language model BERT for a document categorization task. Results indicate competitive performance of the extended HOTT metric. We furthermore apply the HOTT metric and its extension to support educational media research, with a retrieval task of matching topics in German curricula to educational textbooks passages, along with offering an auxiliary explanatory document representing the dominant topic of the retrieved document. In a user study, our explanation method is preferred over regular topic keywords.
Learning a good latent representation is essential for text style transfer, which generates a new sentence by changing the attributes of a given sentence while preserving its content. Most previous works adopt disentangled latent representation learn
ing to realize style transfer. We propose a novel text style transfer algorithm with entangled latent representation, and introduce a style classifier that can regulate the latent structure and transfer style. Moreover, our algorithm for style transfer applies to both single-attribute and multi-attribute transfer. Extensive experimental results show that our method generally outperforms state-of-the-art approaches.
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.
Understanding how linguistic structure is encoded in contextualized embedding could help explain their impressive performance across NLP. Existing approaches for probing them usually call for training classifiers and use the accuracy, mutual informat
ion, or complexity as a proxy for the representation's goodness. In this work, we argue that doing so can be unreliable because different representations may need different classifiers. We develop a heuristic, DirectProbe, that directly studies the geometry of a representation by building upon the notion of a version space for a task. Experiments with several linguistic tasks and contextualized embeddings show that, even without training classifiers, DirectProbe can shine lights on how an embedding space represents labels and also anticipate the classifier performance for the representation.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
