pwc-archive/papers-with-abstracts huggingface dataset:
DLJUST at SemEval-2021 Task 7: Hahackathon: Linking Humor and Offense
Authors: Hani Al-Omari, Isra{'}a AbedulNabi, Rehab Duwairi
Date: 2021-08-01 00:00:00
Proceeding: SEMEVAL 2021
Tasks: Humor Detection

Humor detection and rating poses interesting linguistic challenges to NLP; it is highly subjective depending on the perceptions of a joke and the context in which it is used. This paper utilizes and compares transformers models; BERT base and Large, BERTweet, RoBERTa base and Large, and RoBERTa base irony, for detecting and rating humor and offense. The proposed models, where given a text in cased and uncased type obtained from SemEval-2021 Task7: HaHackathon: Linking Humor and Offense Across Different Age Groups. The highest scored model for the first subtask: Humor Detection, is BERTweet base cased model with 0.9540 F1-score, for the second subtask: Average Humor Rating Score, it is BERT Large cased with the minimum RMSE of 0.5555, for the fourth subtask: Average Offensiveness Rating Score, it is BERTweet base cased model with minimum RMSE of 0.4822.
Gulu at SemEval-2021 Task 7: Detecting and Rating Humor and Offense
Authors: Maoqin Yang
Date: 2021-08-01 00:00:00
Proceeding: SEMEVAL 2021
Tasks: regression, Task 2

Humor 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 tasks: task 1 which contains 3 subtasks (1a, 1b, and 1c), and task 2. Subtask 1a and 1c can be regarded as classification problems and take ALBERT as the basic model. Subtask 1b and 2 can be viewed as regression issues and take RoBERTa as the basic model.
DUTH at SemEval-2021 Task 7: Is Conventional Machine Learning for Humorous and Offensive Tasks enough in 2021?
Authors: Alexandros Karasakalidis, Dimitrios Effrosynidis, Avi Arampatzis
Date: 2021-08-01 00:00:00
Proceeding: SEMEVAL 2021
Tasks: Classification, regression

This paper describes the approach that was developed for SemEval 2021 Task 7 (Hahackathon: Incorporating Demographic Factors into Shared Humor Tasks) by the DUTH Team. We used and compared a variety of preprocessing techniques, vectorization methods, and numerous conventional machine learning algorithms, in order to construct classification and regression models for the given tasks. We used majority voting to combine the models{'} outputs with small Neural Networks (NN) for classification tasks and their mean for regression for improving our system{'}s performance. While these methods proved weaker than modern, deep learning models, they are still relevant in research tasks because of their low requirements on computational power and faster training.
DeepBlueAI at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with Stacking Diverse Language Model-Based Methods
Authors: Bingyan Song, Chunguang Pan, Shengguang Wang, Zhipeng Luo
Date: 2021-08-01 00:00:00
Proceeding: SEMEVAL 2021
Tasks: Language Modeling, Language Modelling, valid

This paper describes the winning system for SemEval-2021 Task 7: Detecting and Rating Humor and Offense. Our strategy is stacking diverse pre-trained language models (PLMs) such as RoBERTa and ALBERT. We first perform fine-tuning on these two PLMs with various hyperparameters and different training strategies. Then a valid stacking mechanism is applied on top of the fine-tuned PLMs to get the final prediction. Experimental results on the dataset released by the organizer of the task show the validity of our method and we win first place and third place for subtask 2 and 1a.
CS-UM6P at SemEval-2021 Task 7: Deep Multi-Task Learning Model for Detecting and Rating Humor and Offense
Authors: Kabil Essefar, Abdellah El Mekki, Abdelkader El Mahdaouy, Nabil El Mamoun, Ismail Berrada
Date: 2021-08-01 00:00:00
Proceeding: SEMEVAL 2021
Tasks: Binary Classification, Humor Detection, Multi-Task Learning

Humor detection has become a topic of interest for several research teams, especially those involved in socio-psychological studies, with the aim to detect the humor and the temper of a targeted population (e.g. a community, a city, a country, the employees of a given company). Most of the existing studies have formulated the humor detection problem as a binary classification task, whereas it revolves around learning the sense of humor by evaluating its different degrees. In this paper, we propose an end-to-end deep Multi-Task Learning (MTL) model to detect and rate humor and offense. It consists of a pre-trained transformer encoder and task-specific attention layers. The model is trained using MTL uncertainty loss weighting to adaptively combine all sub-tasks objective functions. Our MTL model tackles all sub-tasks of the SemEval-2021 Task-7 in one end-to-end deep learning system and shows very promising results.
hub at SemEval-2021 Task 7: Fusion of ALBERT and Word Frequency Information Detecting and Rating Humor and Offense
Authors: Bo Huang, Yang Bai
Date: 2021-08-01 00:00:00
Proceeding: SEMEVAL 2021
Tasks: Humor Detection, regression, Sentence, text-classification, Text Classification

This paper introduces the system description of the hub team, which explains the related work and experimental results of our team{'}s participation in SemEval 2021 Task 7: HaHackathon: Detecting and Rating Humor and Offense. We successfully submitted the test set prediction results of the two subtasks in the task. The goal of the task is to perform humor detection, grade evaluation, and offensive evaluation on each English text data in the data set. Tasks can be divided into two types of subtasks. One is a text classification task, and the other is a text regression task. What we need to do is to use our method to detect the humor and offensive information of the sentence as accurately as possible. The methods used in the results submitted by our team are mainly composed of ALBERT, CNN, and Tf-Idf algorithms. The result evaluation indicators submitted by the classification task are F1 score and Accuracy. The result evaluation index of the regression task submission is the RMSE. The final scores of the prediction results of the two subtask test sets submitted by our team are task1a 0.921 (F1), task1a 0.9364 (Accuracy), task1b 0.6288 (RMSE), task1c 0.5333 (F1), task1c 0.0.5591 (Accuracy), and task2 0.5027 (RMSE) respectively.
YoungSheldon at SemEval-2021 Task 7: Fine-tuning Is All You Need
Authors: Mayukh Sharma, Ilanthenral Kandasamy, W.b. Vasantha
Date: 2021-08-01 00:00:00
Proceeding: SEMEVAL 2021
Tasks: All, regression

In 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 Models (PLMs) to evaluate their performance for humor and offense detection. For regression tasks, we averaged the scores of different models leading to better performance than the original models. We participated in all SubTasks. Our best performing system was ranked 4 in SubTask 1-b, 8 in SubTask 1-c, 12 in SubTask 2, and performed well in SubTask 1-a. We further show comprehensive results using different pre-trained language models which will help as baselines for future work.
Team\_KGP at SemEval-2021 Task 7: A Deep Neural System to Detect Humor and Offense with Their Ratings in the Text Data
Authors: Anik Mondal, Raksha Sharma
Date: 2021-08-01 00:00:00
Proceeding: SEMEVAL 2021
Tasks: Binary Classification, regression

This paper describes the system submitted to SemEval-2021 Task-7 for all four subtasks. Two subtasks focus on detecting humor and offense from the text (binary classification). On the other hand, the other two subtasks predict humor and offense ratings of the text (linear regression). In this paper, we present two different types of fine-tuning methods by using linear layers and bi-LSTM layers on top of the pre-trained BERT model. Results show that our system is able to outperform baseline models by a significant margin. We report F1 scores of 0.90 for the first subtask and 0.53 for the third subtask, while we report an RMSE of 0.57 and 0.58 for the second and fourth subtasks, respectively.
ZYJ at SemEval-2021 Task 7: HaHackathon: Detecting and Rating Humor and Offense with ALBERT-Based Model
Authors: Yingjia Zhao, Xin Tao
Date: 2021-08-01 00:00:00
Proceeding: SEMEVAL 2021

This article introduces the submission of subtask 1 and subtask 2 that we participate in SemEval-2021 Task 7: HaHackathon: Detecting and Rating Humor and Offense, we use a model based on ALBERT that uses ALBERT as the module for extracting text features. We modify the upper layer structure by adding specific networks to better summarize the semantic information. Finally, our system achieves an F-Score of 0.9348 in subtask 1a, RMSE of 0.7214 in subtask 1b, F-Score of 0.4603 in subtask 1c, and RMSE of 0.5204 in subtask 2.
UoR at SemEval-2021 Task 7: Utilizing Pre-trained DistilBERT Model and Multi-scale CNN for Humor Detection
Authors: Zehao Liu, Carl Haines, HuiZhi Liang
Date: 2021-08-01 00:00:00
Proceeding: SEMEVAL 2021
Tasks: Humor Detection, regression

Humour detection is an interesting but difficult task in NLP. Because humorous might not be obvious in text, it can be embedded into context, hide behind the literal meaning and require prior knowledge to understand. We explored different shallow and deep methods to create a humour detection classifier for task 7-1a. Models like Logistic Regression, LSTM, MLP, CNN were used, and pre-trained models like DistilBert were introduced to generate accurate vector representation for textual data. We focused on applying multi-scale strategy on modelling, and compared different models. Our best model is the DistilBERT+MultiScale CNN, it used different sizes of CNN kernel to get multiple scales of features, which achieved 93.7{\%} F1-score and 92.1{\%} accuracy on the test set.
TECHSSN at SemEval-2021 Task 7: Humor and Offense detection and classification using ColBERT embeddings
Authors: Rajalakshmi Sivanaiah, Angel Deborah S, S Milton Rajendram, Mirnalinee Tt, Abrit Pal Singh, Aviansh Gupta, Ayush Nanda
Date: 2021-08-01 00:00:00
Proceeding: SEMEVAL 2021
Tasks: Binary Classification

This paper describes the system used for detecting humor in text. The system developed by the team TECHSSN uses binary classification techniques to classify the text. The data undergoes preprocessing and is given to ColBERT (Contextualized Late Interaction over BERT), a modification of Bidirectional Encoder Representations from Transformers (BERT). The model is re-trained and the weights are learned for the dataset. This system was developed for the task 7 of the competition, SemEval 2021.
Amherst685 at SemEval-2021 Task 7: Joint Modeling of Classification and Regression for Humor and Offense
Authors: Brian Zylich, Akshay Gugnani, Gabriel Brookman, Nicholas Samoray
Date: 2021-08-01 00:00:00
Proceeding: SEMEVAL 2021

This paper describes our submission to theSemEval{'}21: Task 7- HaHackathon: Detecting and Rating Humor and Offense. In this challenge, we explore intermediate finetuning, backtranslation augmentation, multitask learning, and ensembling of different language models. Curiously, intermediate finetuning and backtranslation do not improve performance, while multitask learning and ensembling do improve performance. We explore why intermediate finetuning and backtranslation do not provide the same benefit as other natural language processing tasks and offer insight into the errors that our model makes. Our best performing system ranks 7th on Task 1bwith an RMSE of 0.5339
DuluthNLP at SemEval-2021 Task 7: Fine-Tuning RoBERTa Model for Humor Detection and Offense Rating
Authors: Samuel Akrah
Date: 2021-08-01 00:00:00
Proceeding: SEMEVAL 2021
Tasks: Humor Detection

This paper presents the DuluthNLP submission to Task 7 of the SemEval 2021 competition on Detecting and Rating Humor and Offense. In it, we explain the approach used to train the model together with the process of fine-tuning our model in getting the results. We focus on humor detection, rating, and of-fense rating, representing three out of the four subtasks that were provided. We show that optimizing hyper-parameters for learning rate, batch size and number of epochs can increase the accuracy and F1 score for humor detection
CSECU-DSG at SemEval-2021 Task 7: Detecting and Rating Humor and Offense Employing Transformers
Authors: Afrin Sultana, Nabila Ayman, Abu Nowshed Chy
Date: 2021-08-01 00:00:00
Proceeding: SEMEVAL 2021

With the emerging trends of using online platforms, peoples are increasingly interested in express their opinion through humorous texts. Identifying and rating humorous texts poses unique challenges to NLP due to subjective phenomena i.e. humor may vary to gender, profession, age, and classes of people. Besides, words with multiple senses, cultural domain, and pragmatic competence also need to be considered. A humorous text may be offensive to others. To address these challenges SemEval-2021 introduced a HaHackathon task focusing on detecting and rating humorous and offensive texts. This paper describes our participation in this task. We employed a stacked embedding and fine-tuned transformer models based classification and regression approach from the features from GPT2 medium, BERT, and RoBERTa transformer models. Besides, we utilized the fine-tuned BERT and RoBERTa models to examine the performances. Our method achieved competitive performances in this task.
RedwoodNLP at SemEval-2021 Task 7: Ensembled Pretrained and Lightweight Models for Humor Detection
Authors: Nathan Chi, Ryan Chi
Date: 2021-08-01 00:00:00
Proceeding: SEMEVAL 2021
Tasks: Humor Detection

An understanding of humor is an essential component of human-facing NLP systems. In this paper, we investigate several methods for detecting humor in short statements as part of Semeval-2021 Shared Task 7. For Task 1a, we apply an ensemble of fine-tuned pre-trained language models; for Tasks 1b, 1c, and 2a, we investigate various tree-based and linear machine learning models. Our final system achieves an F1-score of 0.9571 (ranked 24 / 58) on Task 1a, an RMSE of 0.5580 (ranked 18 / 50) on Task 1b, an F1-score of 0.5024 (ranked 26 / 36) on Task 1c, and an RMSE of 0.7229 (ranked 45 / 48) on Task 2a.
EndTimes at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with BERT and Ensembles
Authors: Chandan Kumar Pandey, Chirag Singh, Karan Mangla
Date: 2021-08-01 00:00:00
Proceeding: SEMEVAL 2021
Tasks: Humor Detection, Meta-Learning

This paper describes Humor-BERT, a set of BERT Large based models that we used in the SemEval-2021 Task 7: Detecting and Rating Humor and Offense. It presents pre and post processing techniques, variable threshold learning, meta learning and Ensemble approach to solve various sub-tasks that were part of the challenge. We also present a comparative analysis of various models we tried. Our method was ranked 4th in Humor Controversy Detection, 8th in Humor Detection, 19th in Average Offense Score prediction and 40th in Average Humor Score prediction globally. F1 score obtained for Humor classification was 0.9655 and for Controversy detection it was 0.6261. Our user name on the leader board is ThisIstheEnd and team name is EndTimes.
IIITH at SemEval-2021 Task 7: Leveraging transformer-based humourous and offensive text detection architectures using lexical and hurtlex features and task adaptive pretraining
Authors: Tathagata Raha, Ishan Sanjeev Upadhyay, Radhika Mamidi, Vasudeva Varma
Date: 2021-08-01 00:00:00
Proceeding: SEMEVAL 2021
Tasks: Text Detection

This paper describes our approach (IIITH) for SemEval-2021 Task 5: HaHackathon: Detecting and Rating Humor and Offense. Our results focus on two major objectives: (i) Effect of task adaptive pretraining on the performance of transformer based models (ii) How does lexical and hurtlex features help in quantifying humour and offense. In this paper, we provide a detailed description of our approach along with comparisions mentioned above.
FII FUNNY at SemEval-2021 Task 7: HaHackathon: Detecting and rating Humor and Offense
Authors: Mihai Samson, Daniela Gifu
Date: 2021-08-01 00:00:00
Proceeding: SEMEVAL 2021

The {``}HaHackathon: Detecting and Rating Humor and Offense{''} task at the SemEval 2021 competition focuses on detecting and rating the humor level in sentences, as well as the level of offensiveness contained in these texts with humoristic tones. In this paper, we present an approach based on recent Deep Learning techniques by both trying to train the models based on the dataset solely and by trying to fine-tune pre-trained models on the gigantic corpus.
CONNER: A Cascade Count and Measurement Extraction Tool for Scientific Discourse
Authors: Jiarun Cao, Yuejia Xiang, Yunyan Zhang, Zhiyuan Qi, Xi Chen, Yefeng Zheng
Date: 2021-08-01 00:00:00
Proceeding: SEMEVAL 2021
Tasks: Joint Entity and Relation Extraction, Relation Extraction

This paper presents our wining contribution to SemEval 2021 Task 8: MeasEval. The purpose of this task is identifying the counts and measurements from clinical scientific discourse, including quantities, entities, properties, qualifiers, units, modifiers, and their mutual relations. This task can be induced to a joint entity and relation extraction problem. Accordingly, we propose CONNER, a cascade count and measurement extraction tool that can identify entities and the corresponding relations in a two-step pipeline model. We provide a detailed description of the proposed model hereinafter. Furthermore, the impact of the essential modules and our in-process technical schemes are also investigated.
Stanford MLab at SemEval-2021 Task 8: 48 Hours Is All You Need
Authors: Patrick Liu, Niveditha Iyer, Erik Rozi, Ethan A. Chi
Date: 2021-08-01 00:00:00
Proceeding: SEMEVAL 2021
Tasks: All

This paper presents our system for the Quantity span identification, Unit of measurement identification and Value modifier classification subtasks of the MeasEval 2021 task. The purpose of the Quantity span identification task was to locate spans of text that contain a count or measurement, consisting of a value, usually followed by a unit and occasionally additional modifiers. The goal of the modifier classification task was to determine whether an associated text fragment served to indicate range, tolerance, mean value, etc. of a quantity. The developed systems used pre-trained BERT models which were fine-tuned for the task at hand. We present our system, investigate how architectural decisions affected model predictions, and conduct an error analysis. Overall, our system placed 12 / 19 in the shared task and in the 2nd place for the Unit subcategory.
LIORI at SemEval-2021 Task 8: Ask Transformer for measurements
Authors: Adis Davletov, Denis Gordeev, Nikolay Arefyev, Emil Davletov
Date: 2021-08-01 00:00:00
Proceeding: SEMEVAL 2021
Tasks: Multi-Task Learning, Question Answering

This work describes our approach for subtasks of SemEval-2021 Task 8: MeasEval: Counts and Measurements which took the official first place in the competition. To solve all subtasks we use multi-task learning in a question-answering-like manner. We also use learnable scalar weights to weight subtasks{'} contribution to the final loss in multi-task training. We fine-tune LUKE to extract quantity spans and we fine-tune RoBERTa to extract everything related to found quantities, including quantities themselves.
KaushikAcharya at SemEval-2021 Task 9: Candidate Generation for Fact Verification over Tables
Authors: Kaushik Acharya
Date: 2021-08-01 00:00:00
Proceeding: SEMEVAL 2021
Tasks: Dependency Parsing, Fact Verification

This paper describes the system submitted in the SemEval-2021 Statement Verification and Evidence Finding with Tables task. The system relies on candidate generation for logical forms on the table based on keyword matching and dependency parsing on the claim statements.
MedAI at SemEval-2021 Task 10: Negation-aware Pre-training for Source-free Negation Detection Domain Adaptation
Authors: Jinquan Sun, Qi Zhang, Yu Wang, Lei Zhang
Date: 2021-08-01 00:00:00
Proceeding: SEMEVAL 2021
Tasks: Domain Adaptation, Negation, Negation Detection, Source-Free Domain Adaptation, Unsupervised Domain Adaptation

Due to the increasing concerns for data privacy, source-free unsupervised domain adaptation attracts more and more research attention, where only a trained source model is assumed to be available, while the labeled source data remain private. To get promising adaptation results, we need to find effective ways to transfer knowledge learned in source domain and leverage useful domain specific information from target domain at the same time. This paper describes our winning contribution to SemEval 2021 Task 10: Source-Free Domain Adaptation for Semantic Processing. Our key idea is to leverage the model trained on source domain data to generate pseudo labels for target domain samples. Besides, we propose Negation-aware Pre-training (NAP) to incorporate negation knowledge into model. Our method win the 1st place with F1-score of 0.822 on the official blind test set of Negation Detection Track.
YNU-HPCC at SemEval-2021 Task 10: Using a Transformer-based Source-Free Domain Adaptation Model for Semantic Processing
Authors: Zhewen Yu, Jin Wang, Xuejie Zhang
Date: 2021-08-01 00:00:00
Proceeding: SEMEVAL 2021
Tasks: Domain Adaptation, NER, Source-Free Domain Adaptation

Data sharing restrictions are common in NLP datasets. The purpose of this task is to develop a model trained in a source domain to make predictions for a target domain with related domain data. To address the issue, the organizers provided the models that fine-tuned a large number of source domain data on pre-trained models and the dev data for participants. But the source domain data was not distributed. This paper describes the provided model to the NER (Name entity recognition) task and the ways to develop the model. As a little data provided, pre-trained models are suitable to solve the cross-domain tasks. The models fine-tuned by large number of another domain could be effective in new domain because the task had no change.
ECNUICA at SemEval-2021 Task 11: Rule based Information Extraction Pipeline
Authors: Jiaju Lin, Jing Ling, Zhiwei Wang, Jiawei Liu, Qin Chen, Liang He
Date: 2021-08-01 00:00:00
Proceeding: SEMEVAL 2021
Tasks: Open Information Extraction

This paper presents our endeavor for solving task11, NLPContributionGraph, of SemEval-2021. The purpose of the task was to extract triples from a paper in the Nature Language Processing field for constructing an Open Research Knowledge Graph. The task includes three sub-tasks: detecting the contribution sentences in papers, identifying scientific terms and predicate phrases from the contribution sentences; and inferring triples in the form of (subject, predicate, object) as statements for Knowledge Graph building. In this paper, we apply an ensemble of various fine-tuned pre-trained language models (PLM) for tasks one and two. In addition, self-training methods are adopted for tackling the shortage of annotated data. For the third task, rather than using classic neural open information extraction (OIE) architectures, we generate potential triples via manually designed rules and develop a binary classifier to differentiate positive ones from others. The quantitative results show that we obtain the 4th, 2nd, and 2nd rank in three evaluation phases.