English-to-Japanese Diverse Translation by Combining Forward and Backward Outputs

Kaneko, Masahiro; Imankulova, Aizhan; Hirasawa, Tosho; Komachi, Mamoru

doi:10.18653/v1/2020.ngt-1.15

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…This ranked second or third in all five language tracks. masahiro (Kaneko et al, 2020) took a simple ensemble approach that requires no modification to an off-the-shelf NMT system (fairseq). The authors train multiple forward (L2R) and backward (R2L) models using different initial seeds, first by pretraining on general corpora and then fine-tuning on STAPLE data.…”

Section: Baselinesmentioning

confidence: 99%

Proceedings of the Fourth Workshop on Neural Generation and Translation

2020

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We describe the finding of the Fourth Workshop on Neural Generation and Translation, held in concert with the annual conference of the Association for Computational Linguistics (ACL 2020). First, we summarize the research trends of papers presented in the proceedings. Second, we describe the results of the three shared tasks 1) efficient neural machine translation (NMT) where participants were tasked with creating NMT systems that are both accurate and efficient, and 2) document-level generation and translation (DGT) where participants were tasked with developing systems that generate summaries from structured data, potentially with assistance from text in another language and 3) STAPLE task: creation of as many possible translations of a given input text. This last shared task was organised by Duolingo.1 BLEU+case.mixed+lang.en-de+numrefs.1+s mooth.exp+test.wmt * +tok.13a+version.1.4.8 for various WMT test sets 2 Participants are likely to have used these test sets in development. The WMT 2020 test set was not yet available and others were out of the domain the systems were trained for.

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Section: Baselinesmentioning

confidence: 99%

Proceedings of the Fourth Workshop on Neural Generation and Translation

2020

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A Hybrid Continuous-Time Dynamic Graph Representation Learning Model by Exploring Both Temporal and Repetitive Information

Wang

Chen

et al. 2023

ACM Trans. Knowl. Discov. Data

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Recently, dynamic graph representation learning has attracted more and more attention from both academic and industrial communities due to its capabilities of capturing different real-world phenomena. For a dynamic graph represented as a sequence of timestamped events, there are two kinds of evolutionary essences: temporal and repetitive information. At present, the temporal information of interactions (e.g. timestamps) have been deeply explored. However, as another vital nature of dynamic graphs, the repetitive information of interactions between two nodes is neglected, which may lead to inaccurate node representation. To address this issue, we propose a novel continuous-time dynamic graph representation learning model, which consists of a node-level-memory based module, a historical high-order neighborhood based vertical aggregation module and a repetitive-topological information based horizontal aggregation module. In particular, to characterize the evolving pattern of the repetitive information of interactions between a pair of nodes, we put forward a repetitive-interaction based attention mechanism to integrate the two key attributes (i.e., the content and the number of interactions) of repetitive interactions at different moments, based on the insight that the repetitive behaviors of nodes are widespread and essential. We conduct extensive experiments including future link prediction tasks (for transductive and inductive learning) and dynamic node classification task, and results on three real-life dynamic graph datasets demonstrate that the proposed method significantly outperforms state-of-the-art baselines, for both observed nodes and new ones.

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English-to-Japanese Diverse Translation by Combining Forward and Backward Outputs

Cited by 2 publications

References 8 publications

Proceedings of the Fourth Workshop on Neural Generation and Translation

Proceedings of the Fourth Workshop on Neural Generation and Translation

A Hybrid Continuous-Time Dynamic Graph Representation Learning Model by Exploring Both Temporal and Repetitive Information

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