Zobrazeno 1 - 10
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pro vyhledávání: '"Unanue, Inigo"'
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
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
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
In recent years, neural machine translation (NMT) has become the dominant approach in automated translation. However, like many other deep learning approaches, NMT suffers from overfitting when the amount of training data is limited. This is a seriou
Externí odkaz:
http://arxiv.org/abs/1909.13466
Regularization of neural machine translation is still a significant problem, especially in low-resource settings. To mollify this problem, we propose regressing word embeddings (ReWE) as a new regularization technique in a system that is jointly trai
Externí odkaz:
http://arxiv.org/abs/1904.02461