Linguistic Borrowing as a Way to Enrich  Oil and Gas Terminology

Samigullina, L. Z.; Samigullina, Elina F.; Danilova, O.V.; Латыпова, Ю. А.

doi:10.2991/fred-19.2020.13

Cited by 4 publications

(6 citation statements)

References 7 publications

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“…The adoption of newer terms for MBR-like methods may be a type of terminology drift. Related phenomena have been studied in the philosophy of science literature, including pressures to coin new terms (Dyke, 1992;Merton, 1957), potential negative consequences of divergent terminology (Calvert, 1956;Samigullina et al, 2020), and decreased citation of older methods in NLP (Singh et al, 2023). For a more involved discussion of the literature on term coining and possible connections, see Appendix B.…”

Section: Discussionmentioning

confidence: 99%

“…in studying visual distortions during plane takeoff (Calvert, 1956)). Borrowing terminology from another language (often Latin or Greek) or from another field has been described as a method to build common ground between researchers (Samigullina et al, 2020) and as a possibly concerning pressure against developing language-specific scientific terminology in lower-resourced languages (Hultgren, 2013). However, most work on lexical divides in science has focused on divides across language or field rather than divides across time in the same field.…”

Section: B Contextualizing This Work Within Philosophy Of Sciencementioning

confidence: 99%

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It’s MBR All the Way Down: Modern Generation Techniques Through the Lens of Minimum Bayes Risk

Bertsch,

Xie,

Neubig

et al. 2023

Proceedings of the Big Picture Workshop

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Minimum Bayes Risk (MBR) decoding is a method for choosing the outputs of a machine learning system based not on the output with the highest probability, but the output with the lowest risk (expected error) among multiple candidates. It is a simple but powerful method: for an additional cost at inference time, MBR provides reliable several-point improvements across metrics for a wide variety of tasks without any additional data or training. Despite this, MBR is not frequently applied in NLP works, and knowledge of the method itself is limited. We first provide an introduction to the method and the recent literature. We show that several recent methods that do not reference MBR can be written as special cases of MBR; this reformulation provides additional theoretical justification for the performance of these methods, explaining some results that were previously only empirical. We provide theoretical and empirical results about the effectiveness of various MBR variants and make concrete recommendations for the application of MBR in NLP models, including future directions in this area.

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Section: Discussionmentioning

confidence: 99%

Section: B Contextualizing This Work Within Philosophy Of Sciencementioning

confidence: 99%

It’s MBR All the Way Down: Modern Generation Techniques Through the Lens of Minimum Bayes Risk

Bertsch,

Xie,

Neubig

et al. 2023

Proceedings of the Big Picture Workshop

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Proceedings of the Big Picture Workshop

2023

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A key contribution to being a successful researcher in natural language processing, as in any area, is having a clear overarching vision of what your body of research is trying to accomplish. Using my own 40-year career as an example, I will attempt to provide general advice on formulating and pursuing a coherent research vision. In particular, I will focus on formulating a unique, personal objective that exploits your specific talents, knowledge, and passions, and that is distinct from the current popular trends in the field. I will also focus on formulating a vision that bridges existing fields of study to produce an overarching agenda that unifies previously disparate ideas.

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“…Issues related to various aspects of the establishment, formation and development of terminology are constantly in the focus of attention of linguists. The developmental features of these language subsystems provide an excellent opportunity for modeling and studying the features of linguistic evolution in general (Bylina et al, 2019;Goncharova & Shemyakina, 2020;Korneyeva, 2015;Kuznetsov et al, 2020;Nechayeva, 2016;Samigullina et al, 2019). From our point of view, an analysis of economic terminology is particularly relevant at the moment.…”

Section: Introductionmentioning

confidence: 99%