Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Kocijan, Vid"'
Autor:
Hu, Weihua, Yuan, Yiwen, Zhang, Zecheng, Nitta, Akihiro, Cao, Kaidi, Kocijan, Vid, Leskovec, Jure, Fey, Matthias
We present PyTorch Frame, a PyTorch-based framework for deep learning over multi-modal tabular data. PyTorch Frame makes tabular deep learning easy by providing a PyTorch-based data structure to handle complex tabular data, introducing a model abstra
Externí odkaz:
http://arxiv.org/abs/2404.00776
In this work, we introduce and analyze an approach to knowledge transfer from one collection of facts to another without the need for entity or relation matching. The method works for both canonicalized knowledge bases and uncanonicalized or open kno
Externí odkaz:
http://arxiv.org/abs/2401.15439
Bias-measuring datasets play a critical role in detecting biased behavior of language models and in evaluating progress of bias mitigation methods. In this work, we focus on evaluating gender bias through coreference resolution, where previous datase
Externí odkaz:
http://arxiv.org/abs/2302.05674
The Winograd Schema Challenge - a set of twin sentences involving pronoun reference disambiguation that seem to require the use of commonsense knowledge - was proposed by Hector Levesque in 2011. By 2019, a number of AI systems, based on large pre-tr
Externí odkaz:
http://arxiv.org/abs/2201.02387
Training a model to provide natural language explanations (NLEs) for its predictions usually requires the acquisition of task-specific NLEs, which is time- and resource-consuming. A potential solution is the few-shot out-of-domain transfer of NLEs fr
Externí odkaz:
http://arxiv.org/abs/2112.06204
Autor:
Kocijan, Vid, Lukasiewicz, Thomas
Publikováno v:
EMNLP 2021
The aim of knowledge base completion is to predict unseen facts from existing facts in knowledge bases. In this work, we introduce the first approach for transfer of knowledge from one collection of facts to another without the need for entity or rel
Externí odkaz:
http://arxiv.org/abs/2108.13073
Publikováno v:
AAAI 2021
Diagnostic datasets that can detect biased models are an important prerequisite for bias reduction within natural language processing. However, undesired patterns in the collected data can make such tests incorrect. For example, if the feminine subse
Externí odkaz:
http://arxiv.org/abs/2011.01837
Hard cases of pronoun resolution have been used as a long-standing benchmark for commonsense reasoning. In the recent literature, pre-trained language models have been used to obtain state-of-the-art results on pronoun resolution. Overall, four categ
Externí odkaz:
http://arxiv.org/abs/2010.02570
The Winograd Schema Challenge is both a commonsense reasoning and natural language understanding challenge, introduced as an alternative to the Turing test. A Winograd schema is a pair of sentences differing in one or two words with a highly ambiguou
Externí odkaz:
http://arxiv.org/abs/2004.13831
Publikováno v:
In Artificial Intelligence December 2023 325