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pro vyhledávání: '"Hardmeier, Christian"'
Autor:
Varab, Daniel, Hardmeier, Christian
Recent work has suggested that end-to-end system designs for cross-lingual summarization are competitive solutions that perform on par or even better than traditional pipelined designs. A closer look at the evidence reveals that this intuition is bas
Externí odkaz:
http://arxiv.org/abs/2409.00414
Dehumanization is a mental process that enables the exclusion and ill treatment of a group of people. In this paper, we present two data sets of dehumanizing text, a large, automatically collected corpus and a smaller, manually annotated data set. Bo
Externí odkaz:
http://arxiv.org/abs/2402.08764
Autor:
Tang, Gongbo, Hardmeier, Christian
Coreference resolution is the task of finding expressions that refer to the same entity in a text. Coreference models are generally trained on monolingual annotated data but annotating coreference is expensive and challenging. Hardmeier et al.(2013)
Externí odkaz:
http://arxiv.org/abs/2305.17709
We investigate the problem of determining the predictive confidence (or, conversely, uncertainty) of a neural classifier through the lens of low-resource languages. By training models on sub-sampled datasets in three different languages, we assess th
Externí odkaz:
http://arxiv.org/abs/2210.15452
A lot of Machine Learning (ML) and Deep Learning (DL) research is of an empirical nature. Nevertheless, statistical significance testing (SST) is still not widely used. This endangers true progress, as seeming improvements over a baseline might be st
Externí odkaz:
http://arxiv.org/abs/2204.06815
Autor:
Ulmer, Dennis, Bassignana, Elisa, Müller-Eberstein, Max, Varab, Daniel, Zhang, Mike, van der Goot, Rob, Hardmeier, Christian, Plank, Barbara
The field of Deep Learning (DL) has undergone explosive growth during the last decade, with a substantial impact on Natural Language Processing (NLP) as well. Yet, compared to more established disciplines, a lack of common experimental standards rema
Externí odkaz:
http://arxiv.org/abs/2204.06251
We present an unsupervised method to detect English unergative and unaccusative verbs. These categories allow us to identify verbs participating in the causative-inchoative alternation without knowing the semantic roles of the verb. The method is bas
Externí odkaz:
http://arxiv.org/abs/2111.00808
Popular approaches for quantifying predictive uncertainty in deep neural networks often involve distributions over weights or multiple models, for instance via Markov Chain sampling, ensembling, or Monte Carlo dropout. These techniques usually incur
Externí odkaz:
http://arxiv.org/abs/2110.03051
How to Write a Bias Statement: Recommendations for Submissions to the Workshop on Gender Bias in NLP
Autor:
Hardmeier, Christian, Costa-jussà, Marta R., Webster, Kellie, Radford, Will, Blodgett, Su Lin
At the Workshop on Gender Bias in NLP (GeBNLP), we'd like to encourage authors to give explicit consideration to the wider aspects of bias and its social implications. For the 2020 edition of the workshop, we therefore requested that all authors incl
Externí odkaz:
http://arxiv.org/abs/2104.03026
We generalize principal component analysis for embedding words into a vector space. The generalization is made in two major levels. The first is to generalize the concept of the corpus as a counting process which is defined by three key elements voca
Externí odkaz:
http://arxiv.org/abs/2007.04629