Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Tang, Gongbo"'
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
In this paper, we evaluate the translation of negation both automatically and manually, in English--German (EN--DE) and English--Chinese (EN--ZH). We show that the ability of neural machine translation (NMT) models to translate negation has improved
Externí odkaz:
http://arxiv.org/abs/2107.12203
Recent work has shown that deeper character-based neural machine translation (NMT) models can outperform subword-based models. However, it is still unclear what makes deeper character-based models successful. In this paper, we conduct an investigatio
Externí odkaz:
http://arxiv.org/abs/2011.03469
Neural machine translation (NMT) has achieved new state-of-the-art performance in translating ambiguous words. However, it is still unclear which component dominates the process of disambiguation. In this paper, we explore the ability of NMT encoders
Externí odkaz:
http://arxiv.org/abs/1908.11771
In this paper, we try to understand neural machine translation (NMT) via simplifying NMT architectures and training encoder-free NMT models. In an encoder-free model, the sums of word embeddings and positional embeddings represent the source. The dec
Externí odkaz:
http://arxiv.org/abs/1907.08158
Recent work has shown that the encoder-decoder attention mechanisms in neural machine translation (NMT) are different from the word alignment in statistical machine translation. In this paper, we focus on analyzing encoder-decoder attention mechanism
Externí odkaz:
http://arxiv.org/abs/1810.07595
Recently, non-recurrent architectures (convolutional, self-attentional) have outperformed RNNs in neural machine translation. CNNs and self-attentional networks can connect distant words via shorter network paths than RNNs, and it has been speculated
Externí odkaz:
http://arxiv.org/abs/1808.08946
In this paper, we apply different NMT models to the problem of historical spelling normalization for five languages: English, German, Hungarian, Icelandic, and Swedish. The NMT models are at different levels, have different attention mechanisms, and
Externí odkaz:
http://arxiv.org/abs/1806.05210
We introduce the Helsinki Neural Machine Translation system (HNMT) and how it is applied in the news translation task at WMT 2017, where it ranked first in both the human and automatic evaluations for English--Finnish. We discuss the success of Engli
Externí odkaz:
http://arxiv.org/abs/1708.05942
Akademický článek
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