Word Position Aware Translation Memory for Neural Machine Translation

He, Qiuxiang; Huang, Guoping; Liu, Lemao; Li, Li

doi:10.1007/978-3-030-32233-5_29

Cited by 8 publications

(11 citation statements)

References 7 publications

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“…Given the input sentence x, Zhang et al (2018) try to assign target words in ŷ with higher rewards, when they appear in y r and the aligned source words are in both x r and x. He et al (2019) follow a similar framework and consider the position information of those target words when rewarding. Those works reward the target words in an explicit way, however, the one-sentence-one-model approach (Li et al, 2016c;Turchi et al, 2017) propose to reward target word implicitly.…”

Section: Inference Phasementioning

confidence: 99%

A Survey on Retrieval-Augmented Text Generation

Li¹,

Su²,

Wang³

et al. 2022

Preprint

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Recently, retrieval-augmented text generation attracted increasing attention of the computational linguistics community. Compared with conventional generation models, retrievalaugmented text generation has remarkable advantages and particularly has achieved state-ofthe-art performance in many NLP tasks. This paper aims to conduct a survey about retrievalaugmented text generation. It firstly highlights the generic paradigm of retrieval-augmented generation, and then it reviews notable approaches according to different tasks including dialogue response generation, machine translation, and other generation tasks. Finally, it points out some promising directions on top of recent methods to facilitate future research.

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Section: Inference Phasementioning

confidence: 99%

A Survey on Retrieval-Augmented Text Generation

Li¹,

Su²,

Wang³

et al. 2022

Preprint

Self Cite

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“…The most obvious drawback of fine-tuning is that the delay is too long for testing sentences. To avoid the online tuning process, Zhang et al (2018) and He et al (2019) dynamically integrate translation pieces, based on n-grams extracted from the matched segments in the TM target, into the beam search stage. The second type of approach is efficient but heavily depends on the global hyper-parameter λ, which is sensitive to the development set, leading to inferior performance.…”

Section: Related Workmentioning

confidence: 99%

“…Many notable approaches have been proposed to augment an NMT model by using a TM. For example, Zhang et al (2018) and He et al (2019) extract scored n-grams from a TM and then reward each partial translation once it matches an extracted n-gram during beam search. Gu et al (2018) and Xia et al (2019) use an auxiliary network to encode a TM and then integrate it into the NMT architecture.…”

Section: Introductionmentioning

confidence: 99%

Fast and Accurate Neural Machine Translation with Translation Memory

He¹,

Huang²,

Cui³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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It is generally believed that a translation memory (TM) should be beneficial for machine translation tasks. Unfortunately, existing wisdom demonstrates the superiority of TMbased neural machine translation (NMT) only on the TM-specialized translation tasks rather than general tasks, with a non-negligible computational overhead. In this paper, we propose a fast and accurate approach to TM-based NMT within the Transformer framework: the model architecture is simple and employs a single bilingual sentence as its TM, leading to efficient training and inference; and its parameters are effectively optimized through a novel training criterion. Extensive experiments on six TM-specialized tasks show that the proposed approach substantially surpasses several strong baselines that use multiple TMs, in terms of BLEU and running time. In particular, the proposed approach also advances the strong baselines on two general tasks (WMT news Zh→En and En→De).

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“…Suppose we are given a translation memory (TM) for a source sentence which is a list of bilingual sentence pairs. Generally, there are two ways to improve translation models with translation memory: training model parameters with augmented data (i.e., memory) [41,42,43] and summarizing knowledge from translation memory to augment MT decoder [44,45,46]. For the latter idea, a typical solution to represent a TM is to encode each word in both the source and target sides by a neural memory [44].…”

Section: Graph Based Translation Memorymentioning

confidence: 99%

“…For each sentence, we retrieve 100 translation pairs from the training set by using Apache Lucene [86]. We score the source side of each retrieved pair against the source sentence with fuzzy matching score and select top N = 5 translation sentence pairs as a translation memory for the sentence to be translated, following [44,45,46]. Sentences from the target side in the translation memory are used to form a graph, with each word represented as a node and the connection between adjacent words in a sentence represented as an undirected edge.…”

Section: Translation Memorymentioning

confidence: 99%

TranSmart: A Practical Interactive Machine Translation System

Huang¹,

Liu²,

Wang³

et al. 2021

Preprint

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Automatic machine translation is super efficient to produce translations yet their quality is not guaranteed. This technique report introduces TranSmart, a practical human-machine interactive translation system that is able to trade off translation quality and efficiency. Compared to existing publicly available interactive translation systems, TranSmart supports three key features, word-level autocompletion, sentence-level autocompletion and translation memory. By word-level and sentencelevel autocompletion, TranSmart allows users to interactively translate words in their own manners rather than the strict manner from left to right. In addition, TranSmart has the potential to avoid similar translation mistakes by using translated sentences in history as its memory. This report presents major functions of TranSmart, algorithms for achieving these functions, how to use the TranSmart APIs, and evaluation results of some key functions. TranSmart is publicly available at its homepage 1 .

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Word Position Aware Translation Memory for Neural Machine Translation

Cited by 8 publications

References 7 publications

A Survey on Retrieval-Augmented Text Generation

A Survey on Retrieval-Augmented Text Generation

Fast and Accurate Neural Machine Translation with Translation Memory

TranSmart: A Practical Interactive Machine Translation System

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