Neural Machine Translation with Pivot Languages

By Yong Cheng, Yang Liu, Qian Yang, Maosong Sun, Wei Xu. 2016

35 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

Citations in the corpus

Meta-Learning for Low-Resource Neural Machine Translation (2018, background): “For instance, Cheng et al. (2016); Chen et al. (2017); Lee et al. (2017); Chen et al. (2018) investigate the use of a pivot to build a translation path be-tween two languages even without any directed resource.”
Complete Multilingual Neural Machine Translation (2020, neutral): “…To translate between languages with little training data, three general approaches emerged, i. bridging through a third language (pivot-based MT) (Cheng et al., 2016; Currey and Heaﬁeld, 2019), ii. generating pseudo-parallel data between direct language pairs and training the direct pairs with…”
A Teacher-Student Framework for Zero-Resource Neural Machine Translation (2017, methodology): “To address this problem, it is possible to construct a sub-space by either sampling (Shen et al., 2016), generating a k -best list (Cheng et al., 2016b) or mode approximation (Kim and Rush, 2016).”
Contextual Parameter Generation for Universal Neural Machine Translation (2018, background): “Closely related is the work of Cheng et al. (2016) who proposed the joint training of source-to-pivot and pivot-to-target NMT models.”
Improved Zero-shot Neural Machine Translation via Ignoring Spurious Correlations (2019, methodology): “…et al., 2018; Ses-torain et al., 2018), that zero-shot NMT is sensitive to training conditions, and the translation quality usually lags behind the pivot-based meth-ods which use a shared language as a bridge for translation (Utiyama and Isahara, 2007; Cheng et al., 2016; Chen et al., 2017).”
Translating Translationese: A Two-Step Approach to Unsupervised Machine Translation (2019, methodology): “Recent approaches to zero-shot machine translation include pivot-based methods (Chen et al., 2017; Zheng et al., 2017; Cheng et al., 2016) and multi-lingual NMT methods (Firat et al., 2016a,b; Johnson et al., 2016; Ha et al., 2016, 2017).”
Unsupervised Neural Machine Translation with Weight Sharing (2018, methodology): “Compared to (Cheng et al., 2016; Saha et al., 2016) which use the fully shared encoder, we only share partial weights for the encoders and decoders.”
Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine (2023, unknown): “• Convenience : Essentially, pivot prompting is similar to the pivot translation technique in previous studies (Cheng et al., 2016) but is more convenient for ChatGPT.”
Is ChatGPT A Good Translator? A Preliminary Study (2023, unknown): “• Convenience: Essentially, pivot prompting is similar to the pivot translation technique in previous studies (Cheng et al., 2016) but is more convenient for ChatGPT.”