Zobrazeno 1 - 10
of 176
pro vyhledávání: '"Piccardi, Massimo"'
Text classifiers are vulnerable to adversarial examples -- correctly-classified examples that are deliberately transformed to be misclassified while satisfying acceptability constraints. The conventional approach to finding adversarial examples is to
Externí odkaz:
http://arxiv.org/abs/2405.11904
Machine translation for Vietnamese-English in the medical domain is still an under-explored research area. In this paper, we introduce MedEV -- a high-quality Vietnamese-English parallel dataset constructed specifically for the medical domain, compri
Externí odkaz:
http://arxiv.org/abs/2403.19161
Cross-lingual summarization (XLS) generates summaries in a language different from that of the input documents (e.g., English to Spanish), allowing speakers of the target language to gain a concise view of their content. In the present day, the predo
Externí odkaz:
http://arxiv.org/abs/2403.13240
Cross-lingual text classification leverages text classifiers trained in a high-resource language to perform text classification in other languages with no or minimal fine-tuning (zero/few-shots cross-lingual transfer). Nowadays, cross-lingual text cl
Externí odkaz:
http://arxiv.org/abs/2306.04996
Predicting the traffic incident duration is a hard problem to solve due to the stochastic nature of incident occurrence in space and time, a lack of information at the beginning of a reported traffic disruption, and lack of advanced methods in transp
Externí odkaz:
http://arxiv.org/abs/2209.08735
Multi-document summarization (MDS) has made significant progress in recent years, in part facilitated by the availability of new, dedicated datasets and capacious language models. However, a standing limitation of these models is that they are traine
Externí odkaz:
http://arxiv.org/abs/2203.02894
To date, most abstractive summarisation models have relied on variants of the negative log-likelihood (NLL) as their training objective. In some cases, reinforcement learning has been added to train the models with an objective that is closer to thei
Externí odkaz:
http://arxiv.org/abs/2106.04080
Neural machine translation models are often biased toward the limited translation references seen during training. To amend this form of overfitting, in this paper we propose fine-tuning the models with a novel training objective based on the recentl
Externí odkaz:
http://arxiv.org/abs/2106.02208
Document-level machine translation focuses on the translation of entire documents from a source to a target language. It is widely regarded as a challenging task since the translation of the individual sentences in the document needs to retain aspect
Externí odkaz:
http://arxiv.org/abs/2010.03732
With the rapid growth of the scientific literature, manually selecting appropriate citations for a paper is becoming increasingly challenging and time-consuming. While several approaches for automated citation recommendation have been proposed in the
Externí odkaz:
http://arxiv.org/abs/2007.04070