[PDF] Top 20 A Character Aware Encoder for Neural Machine Translation

... the character embedding in the proposed character-aware NMT model are all initialized to 512 dimensions by Gaussian ...proposed character-aware NMT model, we also test the performance ... See full document

... A single sentence does not always convey in- formation required to translate it into other lan- guages; we sometimes need to add or special- ize words that are omitted or ambiguous in the source languages (e.g., zero ... See full document

... train encoder-decoder neural machine ...the encoder. In this paper, we propose a method that enhances the encoder and at- tention using target monolingual corpora by generating multiple ... See full document

... Ambiguous pronouns are relatively sparse in a general-purpose test set, and previous work has designed targeted evaluation of pronoun transla- tion (Hardmeier et al., 2015; Miculicich Werlen and Popescu-Belis, 2017; ... See full document

... that should ideally be tuned for each language pair and corpus, an expensive step that is frequently omitted. Even when properly tuned, the repre- sentation of the corpus generated by pipelined external processing is ... See full document

... Figure 3: BLEU against sentence length. four of the baselines, and the improvement is the largest when the beam size is 500. For a clear pre- sentation, we plotted the BLEU curves by varying beam size. Figure 2 shows ... See full document

... fully character-level (Cherry et ...the machine translation task from English into five languages from different language families and exhibiting distinct mor- phological typology: Arabic ... See full document

... Following Luong et al. (2015); Kuang et al. (2018), we also force the models to produce the reference target words during inference to get the alignment between input sentences and their ref- erence outputs. We merge the ... See full document

... We have proposed a novel approach to hierar- chical attention for context-aware NMT, based on sparse attention, which is both scalable and efficient. Experiments and evaluation on three English→German datasets in ... See full document

... fully character-level NMT model that maps a character sequence in a source language to a character sequence in a target ...subword-level encoder on DE-EN and CS-EN, and achieves a comparable ... See full document

... dard encoder-decoder architecture, an encoder first maps the source-language input sentence into a sequence of hidden vectors, and a decoder then incrementally predicts the target output ... See full document

... function is a highly non-convex function of the parameters with more complicated land- scape than that for decoder without exter- nal memory, rendering direct optimization over all the parameters rather difficult. ... See full document

... well-known encoder- decoder framework for NMT. Its encoder is a recur- rent neural network (RNN) that reads the source (a sequence of byte pairs representing text in some lan- guage) and encodes it ... See full document

... Why Character-Level Translation? Why not Word-Level Translation? The most pressing issue with word-level processing is that we do not have a perfect word segmentation al- gorithm for any one ... See full document

... Optical Character Recognition (OCR) refers to the process by which an electronic device accepts as input the image of some text which may be either an uploaded photograph or the scanned image of some printed text ... See full document

... propose neural string-to-tree by predicting linearized parse ...syntax-inspired encoder on top of a BiLSTM ...on translation data, they also ex- periment with pre-training the encoder using ... See full document

... existing machine trans- lation pipelines (Chan et ...overall translation performance as measured by ...improve translation quality and accuracy of am- biguous ... See full document

... As models mentioned above only use 1-best segmentation as inputs, lattice which can pack many different segmentations in a compact form has been widely used in statistical machine translation (SMT) (Xu et ... See full document

... In Table. 2 (a)–(b), we show the translations of some randomly selected sentences from the de- velopment and test sets. We chose the ones that have no unknown words. (a) lists long sentences (longer than 30 words), and ... See full document

... The character-based embedding has an impact in learning a better translation model at various levels, which seems to include better alignment, reordering, morphological generation and disam- ...the ... See full document