[PDF] Top 20 Modeling Coherence for Discourse Neural Machine Translation

... the discourse text with ordering structure is bet- ter trained with its original order while not to be scattered, ensuring the effectiveness of our other systems since they are all trained like the first-pass ... See full document

... assessing coherence in a monolingual context have covered entity transitions (Barzilay and Lapata, 2008; El- sner and Charniak, 2011; Burstein et ...2012), discourse relations (Lin et ...in coherence ... See full document

... in Machine Translation and is crit- ical for translation of anaphoric pronouns and for providing consistent ...ferent discourse-level phenomena in NMT us- ing BLEU and eliminate the necessity ... See full document

... recurrent neural network language model (RNNLM) to combine a word-level model with a sentence-level model for document ...cal coherence with a topic-based model. They extract a coherence chain for ... See full document

... contains translation errors, which may affect trans- lation ...language modeling data, but the “inverse” NMT system is not able to translate unseen target word forms (no lemmatization is done) and therefore ... See full document

... on neural noisy channel mod- eling relied on latent variable models that in- crementally process the source and target sen- ...with neural language models trained on bil- lions of words show that noisy ... See full document

... Chinese-English Translation Table 1 summarizes the BLEU scores of different systems on the Chinese-English translation ...improves translation quality in terms of BLEU on most cases, and ob- tains ... See full document

... Table 4 show AdverSuc numbers for different models. As can be seen, the latent variable model VLV-GM is able to generate chunk of texts that are most indistinguishable from coherent texts from humans. This is due to its ... See full document

... a discourse tree. As shown in Figure 1(a), the leaves of a discourse tree (three in this example) correspond to contiguous atomic clause-like text spans, called elementary discourse units (EDUs), ... See full document

... Document-level machine translation (MT) re- mains challenging due to the difficulty in effi- ciently using document context for ...level neural machine translation ...document-level ... See full document

... have. Neural Machine Translation (NMT) models are now the most performant, to the ex- tent that in the past year they have been the best performing at WMT (Bojar et ... See full document

... Intuitively, a readable text should also be coherent, and an incoherent text will result in low readabil- ity. Both readability and coherence indicate how fluent a text is. We thus hypothesize that a model that ... See full document

... For machine translation to tackle discourse phenomena, models must have access to extra- sentential linguistic ...in neural machine translation (NMT), but models have been ... See full document

... coverage modeling with contrasting character- istics, which all share a clear linguistic intuition and yet can be trained in a data driven ...into neural network based NLP ... See full document

... a translation is a rich source of information for predicting what words will be attended to in the ...explicitly modeling the relationship be- tween previous and subsequent attention levels for each word ... See full document

... in neural machine trans- lation (NMT) has certain capability of im- plicitly learning syntactic information of source sentences, this paper shows that source syntax can be explicitly incorpo- rated into NMT ... See full document

... convolutional neural machine translation without this bottleneck was first achieved in Kaiser and Bengio (2016) and Kalchbrenner et ...(Extended Neural GPU) used a recurrent stack of gated ... See full document

... The changing nature of MT output (due to rap- id technological development) may also influ- ence translators’ perceptions and work processes. Since neural machine translation (NMT) is a recent ... See full document

... Cotterell and Kreutzer (2018) frame back- translation as a variational process, with the space of source sentences as the latent space. Their ap- proach argues that the distribution of the synthetic data generator ... See full document

... We used 1 million sentence pairs that are con- tained in train-1.txt of ASPEC-JE corpus for training the translation rule tables and NMT models. We also used 3 million Japanese sen- tences that are contained in ... See full document