The ADAPT System Description for the STAPLE 2020 English-to-Portuguese Translation Task

Haque, Rejwanul; Yasmin, Moslem,; Way, Andy

doi:10.18653/v1/2020.ngt-1.17

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…As a result, many semantic, pragmatic, and textual aspects are still not treated well with current methods. While there is some research on terminology issues (Thunström & Steingrimsson, 2022;Zulfiqar et al, 2018) and domain adaptation (e.g., Haque et al, 2020), overarching academic text features (general academic vocabulary, neologisms, acronyms, intersentential and intrasentential links, overall text cohesion, claim hedging, rhetorical moves) are rarely or not at all considered. Since automated text generation relies on the same technology as NMT, it is likely to pose similar issues for academic writing purposes.…”

Section: Researchmentioning

confidence: 99%

Automated Text Generation and Summarization for Academic Writing

Benites,

Delorme Benites,

Anson

2023

Digital Writing Technologies in Higher Education

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In this chapter, we discuss the implications of automatic text generation for academic writing. We first review the current state of the technology and how it is being used. We then discuss the implications of using automatic text generators for academic writing, including the need for users to be aware of the limitations of the technology and how to use it effectively. We also discuss how the use of automatic text generation can change the traditional stages of writing, and how the content generated by these systems is not justified by semantic or extra-linguistic criteria. We finally argue that notions that have been useful for explaining, analyzing, and teaching academic writing will need to be re-examined in the light of human–machine-interaction. (This abstract has been automatically generated using OpenAI and slightly post-edited; see this article’s Appendix for an explanation.)

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Section: Researchmentioning

confidence: 99%

Automated Text Generation and Summarization for Academic Writing

Benites,

Delorme Benites,

Anson

2023

Digital Writing Technologies in Higher Education

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Simultaneous Translation and Paraphrase for Language Education

Mayhew

Bicknell

Brust

et al. 2020

Proceedings of the Fourth Workshop on Neural Generation and Translation

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We present the task of Simultaneous Translation and Paraphrasing for Language Education (STAPLE). Given a prompt in one language, the goal is to generate a diverse set of correct translations that language learners are likely to produce. This is motivated by the need to create and maintain large, high-quality sets of acceptable translations for exercises in a language-learning application, and synthesizes work spanning machine translation, MT evaluation, automatic paraphrasing, and language education technology.We developed a novel corpus with unique properties for five languages (Hungarian, Japanese, Korean, Portuguese, and Vietnamese), and report on the results of a shared task challenge which attracted 20 teams to solve the task. In our meta-analysis, we focus on three aspects of the resulting systems: external training corpus selection, model architecture and training decisions, and decoding and filtering strategies. We find that strong systems start with a large amount of generic training data, and then finetune with in-domain data, sampled according to our provided learner response frequencies.

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The ADAPT System Description for the STAPLE 2020 English-to-Portuguese Translation Task

Cited by 2 publications

References 15 publications

Automated Text Generation and Summarization for Academic Writing

Automated Text Generation and Summarization for Academic Writing

Simultaneous Translation and Paraphrase for Language Education

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