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This paper describes the details of our system submitted to the SemEval-2014 shared task about aspect-based sentiment analysis on review texts. We participated in subtask 2 (prediction of the polarity …...

This paper describes the Duluth systems that participated in Task 15 of SemEval 2015. The goal of the task was to automatically construct dictionary entries (via a series of three …...

AbstractThis paper describes our submissions to task 7 in SemEval 2017, i.e., Detection and Interpretation of English Puns. We participated in the first two subtasks, which are to detect and …...

AbstractThis paper describes our system for detection and interpretation of English puns. We participated in 2 subtasks related to homographic puns achieve comparable results for these tasks. Through the paper …...

AbstractThis paper describes our participation in SemEval-2017 Task 10. We competed in Subtask 1 and 2 which consist respectively in identifying all the key phrases in scientific publications and label …...

AbstractIn this paper, we present our systems for the “SemEval-2017 Task-5 on Fine-Grained Sentiment Analysis on Financial Microblogs and News”. In our system, we combined hand-engineered lexical, sentiment and metadata …...

AbstractThis paper introduces a new task on Multilingual and Cross-lingual SemanticThis paper introduces a new task on Multilingual and Cross-lingual Semantic Word Similarity which measures the semantic similarity of word …...

AbstractThis paper describes Sew-Embed, our language-independent approach to multilingual and cross-lingual semantic word similarity as part of the SemEval-2017 Task 2. We leverage the Wikipedia-based concept representations developed by Raganato …...

AbstractThis paper discusses the approach taken by the UWaterloo team to arrive at a solution for the Fine-Grained Sentiment Analysis problem posed by Task 5 of SemEval 2017. The paper …...

AbstractThe four sub-tasks of SecureNLP build towards a capability for quickly highlighting critical information from malware reports, such as the specific actions taken by a malware sample. Digital Operatives (DO) …...

AbstractThis paper describes our submissions for SemEval-2018 Task 8: Semantic Extraction from CybersecUrity REports using NLP. The DM_NLP participated in two subtasks: SubTask 1 classifies if a sentence is useful …...

AbstractCybersecurity risks such as malware threaten the personal safety of users, but to identify malware text is a major challenge. The paper proposes a supervised learning approach to identifying malware …...

AbstractWe present the SemEval-2019 Task 12 which focuses on toponym resolution in scientific articles. Given an article from PubMed, the task consists of detecting mentions of names of places, or …...

AbstractThis paper describes Amobee’s participation in SemEval-2020 task 7: “Assessing Humor in Edited News Headlines”, sub-tasks 1 and 2. The goal of this task was to estimate the funniness of …...

AbstractThis paper describes our system submission Hasyarasa for the SemEval-2020 Task-7: Assessing Humor in Edited News Headlines. This task has two subtasks. The goal of Subtask 1 is to predict …...

AbstractWe describe our system for Task 5 of SemEval 2020: Modelling Causal Reasoning in Language: Detecting Counterfactuals. Despite deep learning has achieved significant success in many fields, it still hardly …...

AbstractIn this paper, we present our system for SemEval-2020 task 3, Predicting the (Graded) Effect of Context in Word Similarity. Due to the unsupervised nature of the task, we concentrated …...

AbstractIn this paper, we describe our team’s (JUSTers) effort in the Commonsense Validation and Explanation (ComVE) task, which is part of SemEval2020. We evaluate five pre-trained Transformer-based language models with …...

AbstractThis paper contains a description of my solution to the problem statement of SemEval 2020: Assessing the Funniness of Edited News Headlines. I propose a Siamese Transformer based approach, coupled …...

AbstractThis paper presents the deep-learning model that is submitted to the SemEval-2020 Task 5 competition: “Detecting Counterfactuals”. We participated in both Subtask1 and Subtask2. The model proposed in this paper …...

AbstractWe present a counterfactual recognition (CR) task, the shared Task 5 of SemEval-2020. Counterfactuals describe potential outcomes (consequents) produced by actions or circumstances that did not happen or cannot happen …...

AbstractWe apply contextualised word embeddings to lexical semantic change detection in the SemEval-2020 Shared Task 1. This paper focuses on Subtask 2, ranking words by the degree of their semantic …...

AbstractOur system for the PropEval task explores the ability of semantic features to detect and label propagandistic rhetorical techniques in English news articles. For Subtask 2, labeling identified propagandistic fragments …...

AbstractThe main contribution of this paper is to fine-tune transformer-based language models pre-trained on several text corpora, some being general (E.g., Wikipedia, BooksCorpus), some being the corpora from which the …...

AbstractInscribing persuasion techniques in memes is the most impactful way to influence peoples’ mindsets. People are more inclined to memes as they are more stimulating and convincing and hence memes …...

AbstractThis paper introduces the result of Team Grenzlinie’s experiment in SemEval-2021 task 7: HaHackathon: Detecting and Rating Humor and Offense. This task has two subtasks. Subtask1 includes the humor detection …...

AbstractHumor recognition is a challenging task in natural language processing. This document presents my approaches to detect and rate humor and offense from the given text. This task includes 2 …...

AbstractThis paper describes the winning system for subtask 2 and the second-placed system for subtask 1 in SemEval 2021 Task 4: ReadingComprehension of Abstract Meaning. We propose to use pre-trianed …...

AbstractIn this paper we propose a contextual attention based model with two-stage fine-tune training using RoBERTa. First, we perform the first-stage fine-tune on corpus with RoBERTa, so that the model …...

AbstractThis paper presents the results and main findings of SemEval-2021 Task 1 - Lexical Complexity Prediction. We provided participants with an augmented version of the CompLex Corpus (Shardlow et al. …...

AbstractThis paper describes our system used in the SemEval-2021 Task4 Reading Comprehension of Abstract Meaning, achieving 1st for subtask 1 and 2nd for subtask 2 on the leaderboard. We propose …...

AbstractThis paper describes our contribution to SemEval-2021 Task 7: Detecting and Rating Humor and Of-fense.This task contains two sub-tasks, sub-task 1and sub-task 2. Among them, sub-task 1 containsthree sub-tasks, sub-task …...

AbstractIn this paper, we describe our system used for SemEval 2021 Task 7: HaHackathon: Detecting and Rating Humor and Offense. We used a simple fine-tuning approach using different Pre-trained Language …...

AbstractThis paper presents our systems for the three Subtasks of SemEval Task4: Reading Comprehension of Abstract Meaning (ReCAM). We explain the algorithms used to learn our models and the process …...

AbstractIn multilingual colloquial settings, it is a habitual occurrence to compose expressions of text or speech containing tokens or phrases of different languages, a phenomenon popularly known as code-switching or …...

AbstractThis paper describes the system for the Semeval-2022 Task4 ”Patronizing and Condescending Language Detection”.An entity engages in Patronizing and Condescending Language(PCL) when its language use shows a superior attitude towards …...

AbstractIn the paper, we describe a unified system for task 3 of SemEval-2022. The task aims to recognize the semantic structures of sentences by providing two nominal arguments and to …...

AbstractIn this paper we propose four deep learning models for the task of detecting and classifying Patronizing and Condescending Language (PCL) using a corpus of over 13,000 annotated paragraphs in …...

AbstractCode-mixing is the phenomena of mixing various linguistic units such as paragraphs, sentences, phrases, words, etc., of one language with that of the other language in any text. This code-mixing …...

AbstractPatronizing and condescending language (PCL) has a large harmful impact and is difficult to detect, both for human judges and existing NLP systems. At SemEval-2022 Task 4, we propose a …...

AbstractThis paper describes the Stevens Institute of Technology’s submission for the WMT 2022 Shared Task: Code-mixed Machine Translation (MixMT). The task consisted of two subtasks, subtask 1 Hindi/English to Hinglish …...

AbstractIn this paper, we present our submission to the SemEval 2022 - Task 4 on Patronizing and Condescending Language (PCL) detection. Weapproach this problem as a traditional text classification problem …...

AbstractPatronizing and condescending language (PCL) can find its way into many mediums of public discourse. Presence of PCL in text can produce negative effects in the society. The challenge presented …...

AbstractThis paper describes our system for Task 4 of SemEval 2022: Patronizing and Condescending Language (PCL) Detection. For sub-task 1, where the objective is to classify a text as PCL …...

AbstractIn this paper, we describe the implementations of our systems for the SemEval-2023 Task 5 ‘Clickbait Spoiling’, which involves the classification of clickbait posts in sub-task 1 and the spoiler …...

AbstractThis paper describes our system submitted for SemEval Task 7, Multi-Evidence Natural Language Inference for Clinical Trial Data. The task consists of 2 subtasks. Subtask 1 is to determine the …...

AbstractThe paper presents an approach for solving SemEval 2023 Task 7 - identifying the inference relation in a clinical trials dataset. The system has two levels for retrieving relevant clinical …...

AbstractThis paper describes our participation as team FramingFreaks in the SemEval-2023 task 3 “Category and Framing Predictions in online news in a multi-lingual setup.” We participated in subtasks 1 and …...

AbstractThis paper presents the systems and approaches of the Gallagher team for the SemEval-2023 Task 5: Clickbait Spoiling. We propose a method to classify the type of spoiler (phrase, passage, …...

AbstractIn this paper, we describe the multi strategy system for SemEval-2022 Task 7, This task aims to determine whether a given statement is supported by one or two Clinical Trial …...

AbstractThis paper describes the submissions of the Natural Language Processing (NLP) team from the Australian Research Council Industrial Transformation Training Centre (ITTC) for Cognitive Computing in Medical Technologies to the …...

AbstractThis paper describes our approach for SemEval- 2023 Task 3: Detecting the category, the fram- ing, and the persuasion techniques in online news in a multilingual setup. For Subtask 1 …...

AbstractThis paper describes our system submitted to SemEval-2023 Task 5: Clickbait Spoiling. We work on spoiler generation of the subtask 2 and develop a system which comprises two parts: 1) …...