Sarcasm is an intricate form of speech, where meaning is conveyed implicitly. Being a convoluted form of expression, detecting sarcasm is an assiduous problem. The difficulty in recognition of sarcasm has many pitfalls, including misunderstandings in everyday communications, which leads us to an increasing focus on automated sarcasm detection. In the second edition of the Figurative Language Processing (FigLang 2020) workshop, the shared task of sarcasm detection released two datasets, containing responses along with their context sampled from Twitter and Reddit.In this work, we use RoBERT a large to detect sarcasm in both the datasets. We further assert the importance of context in improving the performance of contextual word embedding based models by using three different types of inputs -Response-only, Context-Response, and Context-Response (Separated). We show that our proposed architecture performs competitively for both the datasets. We also show that the addition of a separation token between context and target response results in an improvement of 5.13% in the F1-score in the Reddit dataset.
With the growing use of social media and its availability, many instances of the use of offensive language have been observed across multiple languages and domains. This phenomenon has given rise to the growing need to detect the offensive language used in social media crosslingually. In OffensEval 2020, the organizers have released the multilingual Offensive Language Identification Dataset (mOLID), which contains tweets in five different languages, to detect offensive language. In this work, we introduce a cross-lingual inductive approach to identify the offensive language in tweets using the contextual word embedding XLM-RoBERTa (XLM-R). We show that our model performs competitively on all five languages, obtaining the fourth position in the English task with an F1-score of 0.919 and eighth position in the Turkish task with an F1-score of 0.781. Further experimentation proves that our model works competitively in a zero-shot learning environment, and is extensible to other languages.
Computing with words is a concept that is used to solve problems with input in natural language. Modifiers are transformation functions with predefined labels used extensively in decision-making to specify the desired value of a linguistic variable defined by fuzzy sets. In past years, few efforts have been made to study the application of Computing with Words (CW) in many domains ranging from fraud detection systems to diagnosis systems in medicine. However, the application of CW in these fields with modified Fuzzy sets did not give satisfactory results. When applied to modified Fuzzy sets, the existing interpolation rule does not cover the extreme left and extreme right-shifted fuzzy sets. Hence, there is a need to introduce a new interpolation rule when working with modifiers. This paper introduces a new Fuzzy Modifier Interpolation Rule to Type-1 Fuzzy sets and Interval Type-2 (IT-2) Fuzzy Sets to enhance the quality of results obtained when modifiers are applied.Povzetek: V prispevku je predstavljeno novo interpolacijsko pravilo za mehke modifikatorje v mehkih nizih tipa 1 in intervalnega tipa 2.
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