Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on. In this paper, we present NL-Augmenter, a new participatory Pythonbased natural language augmentation framework which supports the creation of both transformations (modifications to the data) and filters (data splits according to specific features). We describe the framework and an initial set of 117 transformations and 23 filters for a variety of natural language tasks. We demonstrate the efficacy of NL-Augmenter by using several of its tranformations to analyze the robustness of popular natural language models. The infrastructure, datacards and robutstness analysis results are available publicly on the NL-Augmenter repository (https://github. com/GEM-benchmark/NL-Augmenter).

In a current experiment we were testing Com-monGen dataset for structure-to-text task from GEM living benchmark with the constraint based POINTER model. POINTER represents a hybrid architecture, combining insertionbased and transformer paradigms, predicting the token and the insertion position at the same time. The text is therefore generated gradually in a parallel non-autoregressive manner, given the set of keywords. The pretrained model was fine-tuned on a training split of the Common-Gen dataset and the generation result was compared to the validation and challenge splits. 1 The received metrics outputs, which measure lexical equivalence, semantic similarity and diversity, are discussed in details in a present system description.

Data augmentation is an important method for evaluating the robustness of and enhancing the diversity of training data for natural language processing (NLP) models. In this paper, we present NL-Augmenter, a new participatory Python-based natural language (NL) augmentation framework which supports the creation of transformations (modifications to the data) and filters (data splits according to specific features). We describe the framework and an initial set of 117 transformations and 23 filters for a variety of NL tasks annotated with noisy descriptive tags. The transformations incorporate noise, intentional and accidental human mistakes, socio-linguistic variation, semantically-valid style, syntax changes, as well as artificial constructs that are unambiguous to humans. We demonstrate the efficacy of NL-Augmenter by using its transformations to analyze the robustness of popular language models. We find different models to be differently challenged on different tasks, with quasi-systematic score decreases. The infrastructure, datacards, and robustness evaluation results are publicly available on GitHub for the benefit of researchers working on paraphrase generation, robustness analysis, and low-resource NLP. El aumento de datos es un método importante para evaluar la solidez y mejorar la diversidad del entrenamiento datos para modelos de procesamiento de lenguaje natural (NLP). इस लेख में, हम एनएल-ऑगमेंटर का प्रस्ताव करते हैं - एक नया भागी- दारी पूर्वक, पायथन में बनाया गया, लैंग्वेज (एनएल) ऑग्मेंटेशन फ्रेमवर्क जो ट्रांसफॉर्मेशन (डेटा में बदलाव करना) और फीलटर (फीचर्स के अनुसार डेटा का भाग करना) के नीरमान का समर्थन करता है।. 我们描述了NL-Augmenter框架及其初步包含的117种转换和23个过滤器，并 大致标注分类了一系列可适配的自然语言任务. این دگرگونی ها شامل نویز، اشتباهات عمدی و تصادفی انسانی، تنوع اجتماعی-زبانی، سبک معنایی معتبر، تغییرات نحوی و همچنین ساختارهای مصنوعی است که برای انسان ها مبهم است. NL-Augmenterpa allin kaynintam qawachiyku, tikrakuyninku- nata servichikuspayku, chaywanmi qawariyku modelos de lenguaje popular nisqapa allin takyasqa kayninta. Kami menemukan model yang berbeda ditantang secara berbeda pada tugas yang berbeda, dengan penurunan skor kuasi-sistematis. Infrastruktur, kartu data, dan hasil evaluasi ketahanan dipublikasikan tersedia secara gratis di GitHub untuk kepentingan para peneliti yang mengerjakan pembuatan parafrase, analisis ketahanan, dan NLP sumber daya rendah.

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The paper focuses on a poetic discourse of the avant-garde poetic community “41º” (A. Kruchenykh, I. Terentyev, I. Zdanevich). Unlike the public actions of “classic” avant-garde (futuristic) communities, “41º” relied less on scandal as a means to attract the attention of the “newshawk”, a relay of a public action for a broader audience. Regional journalists, literary critics sympathized with the futurists and included them into their creative community. Their attention did not have to be attracted on purpose. Scandal has been replaced by cooperation. Therefore, the communicative strategy that has developed in the “41º” community is predicated on a dialogue, it implies the activity of the reader. It as a consequence, the reader parodies the poet-orator. The reader deliberately alters and then creatively twists the poet’s statement, appropriating the device and becoming the author himself, a subject expressing feelings. This strategy becomes topical in A. Kruchenykh and I. Terentyev poetic theory as part of the “readerly” program. A quest “mistakes” (textual zones subject to a parodic recoding) in someone else’s text is foregrounded. A “mistake” (“shift”) makes it possible to reimagine the text as a semiotic object and reassign its reference to a readerly intention not coinciding with the authorial one, so that the reader as a co-performer becomes a co-author. The purpose of text misreading (“hacking”) is to shift the focus from the “I” of the writing subject and from the printed statement to the context as a source of creativity and to the performer’s gesture as the referent of the statement.