IISERB Brains at SemEval 2022 Task 6: A Deep-learning Framework to Identify Intended Sarcasm in English

• URL: http://arxiv.org/abs/2203.02244v1
• Date: Fri, 4 Mar 2022 11:23:54 GMT
• Title: IISERB Brains at SemEval 2022 Task 6: A Deep-learning Framework to Identify Intended Sarcasm in English
• Authors: Tanuj Singh Shekhawat, Manoj Kumar, Udaybhan Rathore, Aditya Joshi, Jasabanta Patro
• Abstract summary: This paper describes the system architectures and the models submitted by our team "IISERBBrains" to SemEval 2022 Task 6 competition. We also report the other models and results that we obtained through our experiments after organizers published the gold labels of their evaluation data.
• Score: 6.46316101972863
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper describes the system architectures and the models submitted by our team "IISERBBrains" to SemEval 2022 Task 6 competition. We contested for all three sub-tasks floated for the English dataset. On the leader-board, wegot19th rank out of43 teams for sub-taskA, the 8th rank out of22 teams for sub-task B,and13th rank out of 16 teams for sub-taskC. Apart from the submitted results and models, we also report the other models and results that we obtained through our experiments after organizers published the gold labels of their evaluation data

Related papers

• SemEval-2024 Shared Task 6: SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes [48.83290963506378]
  This paper presents the results of the SHROOM, a shared task focused on detecting hallucinations. We observe a number of key trends in how this approach was tackled. While a majority of the teams did outperform our proposed baseline system, the performances of top-scoring systems are still consistent with a random handling of the more challenging items.
  arXiv  Detail & Related papers  (2024-03-12T15:06:22Z)
• NADI 2023: The Fourth Nuanced Arabic Dialect Identification Shared Task [28.986040897360336]
  We describe the findings of the fourth Nuanced Arabic Dialect Identification Shared Task (NADI 2023) NADI 2023 targeted both dialect identification (Subtask 1) and dialect-to-MSA machine translation (Subtask 2 and Subtask 3). We describe the methods employed by the participating teams and briefly offer an outlook for NADI.
  arXiv  Detail & Related papers  (2023-10-24T18:41:24Z)
• ICDAR 2023 Competition on Hierarchical Text Detection and Recognition [60.68100769639923]
  The competition is aimed to promote research into deep learning models and systems that can jointly perform text detection and recognition. We present details of the proposed competition organization, including tasks, datasets, evaluations, and schedule. During the competition period (from January 2nd 2023 to April 1st 2023), at least 50 submissions from more than 20 teams were made in the 2 proposed tasks.
  arXiv  Detail & Related papers  (2023-05-16T18:56:12Z)
• NADI 2022: The Third Nuanced Arabic Dialect Identification Shared Task [16.688997360734472]
  Third Nuanced Arabic Dialect Identification Shared Task (NADI 2022) NADI 2022 targeted both dialect identification (Subtask 1) and dialectal sentiment analysis (Subtask 2) at the country level. Winning team achieved 27.06 F1 on Subtask 1 and F1=75.16 on Subtask 2.
  arXiv  Detail & Related papers  (2022-10-18T04:31:05Z)
• Overview of Abusive and Threatening Language Detection in Urdu at FIRE 2021 [50.591267188664666]
  We present two shared tasks of abusive and threatening language detection for the Urdu language. We present two manually annotated datasets containing tweets labelled as (i) Abusive and Non-Abusive, and (ii) Threatening and Non-Threatening. For both subtasks, m-Bert based transformer model showed the best performance.
  arXiv  Detail & Related papers  (2022-07-14T07:38:13Z)
• UrduFake@FIRE2021: Shared Track on Fake News Identification in Urdu [55.41644538483948]
  This study reports the second shared task named as UrduFake@FIRE2021 on identifying fake news detection in Urdu language. The proposed systems were based on various count-based features and used different classifiers as well as neural network architectures. The gradient descent (SGD) algorithm outperformed other classifiers and achieved 0.679 F-score.
  arXiv  Detail & Related papers  (2022-07-11T19:15:04Z)
• MIA 2022 Shared Task: Evaluating Cross-lingual Open-Retrieval Question Answering for 16 Diverse Languages [54.002969723086075]
  We evaluate cross-lingual open-retrieval question answering systems in 16 typologically diverse languages. The best system leveraging iteratively mined diverse negative examples achieves 32.2 F1, outperforming our baseline by 4.5 points. The second best system uses entity-aware contextualized representations for document retrieval, and achieves significant improvements in Tamil (20.8 F1), whereas most of the other systems yield nearly zero scores.
  arXiv  Detail & Related papers  (2022-07-02T06:54:10Z)
• Retrospective on the 2021 BASALT Competition on Learning from Human Feedback [92.37243979045817]
  The goal of the competition was to promote research towards agents that use learning from human feedback (LfHF) techniques to solve open-world tasks. Rather than mandating the use of LfHF techniques, we described four tasks in natural language to be accomplished in the video game Minecraft. Teams developed a diverse range of LfHF algorithms across a variety of possible human feedback types.
  arXiv  Detail & Related papers  (2022-04-14T17:24:54Z)
• Nowruz at SemEval-2022 Task 7: Tackling Cloze Tests with Transformers and Ordinal Regression [1.9078991171384017]
  This paper outlines the system using which team Nowruz participated in SemEval 2022 Task 7 Identifying Plausible Clarifications of Implicit and Underspecified Phrases.
  arXiv  Detail & Related papers  (2022-04-01T16:36:10Z)
• CyberWallE at SemEval-2020 Task 11: An Analysis of Feature Engineering for Ensemble Models for Propaganda Detection [0.0]
  We use a bi-LSTM architecture in the Span Identification subtask and train a complex ensemble model for the Technique Classification subtask. Our systems achieve a rank of 8 out of 35 teams in the SI subtask and 8 out of 31 teams in the TC subtask.
  arXiv  Detail & Related papers  (2020-08-22T15:51:16Z)
• problemConquero at SemEval-2020 Task 12: Transformer and Soft label-based approaches [2.434159858639793]
  We present various systems submitted by our team problemConquero for SemEval-2020 Shared Task 12 Multilingual Offensive Language Identification in Social Media. We participated in all the three sub-tasks of OffensEval-2020, and our final submissions during the evaluation phase included transformer-based approaches and a soft label-based approach.
  arXiv  Detail & Related papers  (2020-07-21T15:06:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.