Social media is abundant in visual and textual information presented together or in isolation. Research Domain Criteria (RDoC) is a framework that integrates multi-dimensional information for a better understanding of mental disorders. Counterfactual statements describe events that have not or could not have occurred and the possible implications of such events.

Our work is relevant to any task concerned with the combination of different modalities. In this project, we develop a system for addressing the research problem posed in Task 4 of SemEval 2020, which involves differentiating between natural language statements that confirm to common sense and those that do not. The novelty in work lies in the architecture design, which handles the logical implication of contradicting statements and simultaneous information extraction from both sentences.

In our work, we solve for the task of regressing funniness and predicting the funnier edited headline by leveraging the recently proposed powerful LM’s and humor heuristics-based features. agaur@iitk.ac.in Lab: WL 312.

Sarcasm is a relatively new innovation in NLP and topic-based sarcasm generation is still an unexplored field. We have demonstrated the immediate zero-shot application of such a parser on Marathi and also applied an existing word-embedding alignment method called MUSE to improve the cross-lingual application performance. We submitted two models for sub-task C (offense target identification), one using soft labels and the other using BERT based fine-tuned model. A recent work aimed at same, has recently proposed the Humicroedit (Hossain et al., 2019) dataset, which contains edited news headlines graded for funniness, as a step to identify causes of humor.

We achieved F1 scores of 0.707 (ranked 5th) and 0.725 (ranked 13th) on Hindi-English (Hinglish) and Spanish-English (Spanglish) datasets, respectively. This project will address a subdomain of this problem from the sphere of machine comprehension. In this project, we develop for addressing the research problem posed in SemEval-2020 Shared Task 12 Multilingual Offensive Language Identification in Social Media.

In this project, we develop a system for addressing the research problem posed in Task 10 of SemEval-2020: Emphasis Selection For Written Text in Visual Media. Our best rank for sub-task C was 20 out of 39 using BERT based fine-tuned model.

RoBERTa based fine-tuned model for sub-task B (automatic categorization of offense types) was submitted.

For Subtask 1, our method consists of learning embeddings using Bert and then using a Binary classifier. We make use of pre-trained BERT language model enhanced with tagging techniques developed for the task of Named Entity Recognition (NER), to develop a system for identifying propaganda spans in the text. Recently, MELD dataset was released which accelerated the research in conversational systems involving emotion recognition. Preferred E-mail: ssax AT cse.iitk.ac.in or ssax AT iitk.ac.in. Second, identifying the most crucial reason why a statement does not make sense.

State-of-the-art NLP models fail at simple additions and deletions of characters in the input sentences, calling for a need to defend against such attacks.

We aim to develop KG for the IIT Kanpur website using classical NLP techniques.

An unstructured text contains valuable information but retrieving elements of interest from the unstructured text requires crucial NLP techniques to process unstructured text. The organizers propose three subtasks - first, selecting between two sentences, the one which is against common sense.

We also explored various other approaches that involved the use of classical methods, other neural architectures and the incorporation of different linguistic features.

Our ranks for sub-task A were Greek-19 out of 37, Turkish-22 out of 46, Danish-26 out of 39, Arabic-39 out of 53, and English-20 out of 85.

In this work, we propose a novel learning technique called Learning from Description (LDES) and analyze our approach for the case of zero-shot text classification (ZS-TC). The model will give user the flexibility to control the category and intensity of emotion as well as the subject of the generated text.

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