Abstract

Clickbait is the text or a thumbnail image that entices the user to click the accompanying link. Clickbaits employ strategies while deliberately hiding the critical elements of the article and revealing partial information in the title, which arouses sufficient curiosity and motivates the user to click the link. In this work, we identify the kind of spoiler given a clickbait title. We formulate this as a text classification problem. We finetune pretrained transformer models on the title of the post and build models for theclickbait-spoiler classification. We achieve a balanced accuracy of 0.70 which is close to the baselin

Abstract

The Clickbait Challenge targets spoiling the clickbaits using short pieces of information known as spoilers to satisfy the curiosity in- duced by a clickbait post. The large context of the article associated with the clickbait and differences in the spoiler forms, make the task challenging. Hence, to tackle the large con- text, we propose an Information Condensation- based approach, which prunes down the unnec- essary context. Given an article, our filtering module optimised with a contrastive learning objective first selects the parapraphs that are the most relevant to the corresponding clickbait. The resulting condensed article is then fed to the two downstream tasks of spoiler type clas- sification and spoiler generation. We demon- strate and analyze the gains from this approach on both the tasks. Overall, we win the task of spoiler type classification and achieve competi- tive results on spoiler generation

Abstract

This paper describes our system used in the SemEval-2023 Task 11 Learning With Dis- agreements (Le-Wi-Di). This is a subjective task since it deals with detecting hate speech, misogyny and offensive language. Thus, dis- agreement among annotators is expected. We experiment with different settings like loss functions specific for subjective tasks and in- clude anonymized annotator-specific informa- tion to help us understand the level of disagree- ment. We perform an in-depth analysis of the performance discrepancy of these differ- ent modelling choices. Our system achieves a cross-entropy of 0.58, 4.01 and 3.70 on the test sets of HS-Brexit, ArMIS and MD-Agreement, respectively. Our code implementation is pub- licly available.

Abstract

Identifying human values behind arguments is a complex task which requires understanding of premise, stance and conclusion together. We propose a method that uses a pre-trained lan- guage model, DeBERTa, to tokenize and con- catenate the text before feeding it into a fully connected neural network. We also show that leveraging the hierarchy in values improves the performance by .14 F1 score compared to only using level 2 values. Our code is made publicly available here.1

Abstract

This paper describes our approach for SemEval- 2023 Task 10: Explainable Detection of Online Sexism (EDOS). The task deals with identifi- cation and categorization of sexist content into fine-grained categories for explainability in sex- ism classification. The explainable categoriza- tion is proposed through a set of three hierar- chical tasks that constitute a taxonomy of sexist content, each task being more granular than the former for categorization of the content. Our team (iREL) participated in all three hierarchi- cal subtasks. Considering the inter-connected task structure, we study multilevel training to study the transfer learning from coarser to finer tasks. Our experiments based on pretrained transformer architectures also make use of ad- ditional strategies such as domain-adaptive pre- training to adapt our models to the nature of the content dealt with, and use of the focal loss objective for handling class imbalances. Our best-performing systems on the three tasks achieve macro-F1 scores of 85.93, 69.96 and 54.62 on their respective validation sets

Abstract

In this paper, we present our team’s involvement in Task 6: LegalEval: Understanding Legal Texts. The task comprised three subtasks, and we focus on subtask A: Rhetorical Roles prediction. Our approach included experimenting with pre-trained embeddings and refining them with statistical and neural classifiers. We provide a thorough examination ofour experiments, solutions, and analysis, culminating in our best-performing model and current progress. We achieved a micro F1 score of 0.6133 on the test data using fine-tuned LegalBERT embeddings.

Abstract

Fair resource allocation is an important problem in many real-world scenarios, where resources such as goods and chores must be allocated among agents. In this survey, we delve into the intricacies of fair allocation, focusing specifically on the challenges associated with indivisible resources. We de- fine fairness and efficiency within this context and thoroughly survey existential results, algorithms, and approximations that satisfy various fairness criteria, including envyfreeness, proportionality, MMS, and their relaxations. Additionally, we discuss algorithms that achieve fairness and efficiency, such as Pareto Optimality and Utilitarian Welfare. We also study the computational complexity of these algorithms, the likelihood of finding fair allocations, and the price of fairness for each fairness notion. We also cover mixed instances of indivisible and divisible items and investigate different valuation and allocation settings. By summarizing the state-of-the-art research, this survey provides valuable insights into fair resource allocation of indivisible goods and chores, highlighting compu- tational complexities, fairness guarantees, and trade-offs between fairness and efficiency. It serves as a foundation for future advancements in this vital field.

Abstract

Deep learning systems have shown state-of-the-art performance in clinical prediction tasks. However, current research suggests that cleverly produced hostile images can trick these systems. Deep learning-based medical image classification algorithms have been questioned regarding their practical deployment. To address this problem, we provide an unsupervised learning technique for detecting adversarial attacks on medical images. Without identifying the attackers or reducing classification performance, our suggested strategy FNS (Features Normalization and Standardization), can detect adversarial attacks more effectively than earlier methods.

Abstract

Coordinate-based implicit neural networks, or neural fields, have emerged as useful representations of shape and appearance in 3D computer vision. Despite advances, however, it remains challenging to build neural fields for categories of objects without datasets like ShapeNet that provide “canonicalized” object instances that are consistently aligned for their 3D position and orientation (pose).We present Canonical Field Network (CaFi-Net), a self-supervised method to canonicalize the 3D pose of instances from an object category represented as neural fields, specifically neural radiance fields (NeRFs). CaFi-Net directly learns from continuous and noisy radiance fields using a Siamese network architecture that is designed to extract equivariant field features for category-level canonicalization. During inference, our method takes pre-trained neural radiance fields of novel object instances at arbitrary 3D pose and estimates a canonical field with consistent 3D pose across the entire category. Extensive experiments on a new dataset of 1300 NeRF models across 13 object categories show that our method matches or exceeds the performance of 3D point cloud-based methods.

Abstract

Multivariate Data Analysis for Motor Failure Detection and Isolation in a Multicopter UAV using Real-Flight Attitude Signals