pwc-archive/papers-with-abstracts huggingface dataset:
BennettNLP at SemEval-2020 Task 8: Multimodal sentiment classification Using Hybrid Hierarchical Classifier
Authors: Ambuje Gupta, Harsh Kataria, Souvik Mishra, Tapas Badal, Vipul Mishra
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Multimodal Sentiment Analysis, Optical Character Recognition (OCR), Sentiment Analysis, Sentiment Classification

Memotion analysis is a very crucial and important subject in today{'}s world that is dominated by social media. This paper presents the results and analysis of the SemEval-2020 Task-8: Memotion analysis by team Kraken that qualified as winners for the task. This involved performing multimodal sentiment analysis on memes commonly posted over social media. The task comprised of 3 subtasks, Task A was to find the overall sentiment of a meme and classify it into positive, negative or neutral, Task B was to classify it into the different types which were namely humour, sarcasm, offensive or motivation where a meme could have more than one category, Task C was to further quantify the classifications achieved in task B. An imbalanced data of 6992 rows was utilized for this which contained images (memes), text (extracted OCR) and their annotations in 17 classes provided by the task organisers. In this paper, the authors proposed a hybrid neural Na{\"\i}ve-Bayes Support Vector Machine and logistic regression to solve a multilevel 17 class classification problem. It achieved the best result in Task B i.e 0.70 F1 score. The authors were ranked third in Task B.
BERT at SemEval-2020 Task 8: Using BERT to Analyse Meme Emotions
Authors: Adithya Avvaru, Sanath Vobilisetty
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Sentiment Analysis

Sentiment analysis, being one of the most sought after research problems within Natural Language Processing (NLP) researchers. The range of problems being addressed by sentiment analysis is increasing. Till now, most of the research focuses on predicting sentiment, or sentiment categories like sarcasm, humor, offense and motivation on text data. But, there is very limited research that is focusing on predicting or analyzing the sentiment of internet memes. We try to address this problem as part of {``}Task 8 of SemEval 2020: Memotion Analysis{''}. We have participated in all the three tasks under Memotion Analysis. Our system built using state-of-the-art Transformer-based pre-trained Bidirectional Encoder Representations from Transformers (BERT) performed better compared to baseline models for the two tasks A and C and performed close to the baseline model for task B. In this paper, we present the data used, steps used by us for data cleaning and preparation, the fine-tuning process for BERT based model and finally predict the sentiment or sentiment categories. We found that the sequence models like Long Short Term Memory(LSTM) and its variants performed below par in predicting the sentiments. We also performed a comparative analysis with other Transformer based models like DistilBERT and XLNet.
CN-HIT-MI.T at SemEval-2020 Task 8: Memotion Analysis Based on BERT
Authors: Zhen Li, Yaojie Zhang, Bing Xu, Tiejun Zhao
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Emotion Recognition

Internet memes emotion recognition is focused by many researchers. In this paper, we adopt BERT and ResNet for evaluation of detecting the emotions of Internet memes. We focus on solving the problem of data imbalance and data contains noise. We use RandAugment to enhance the data of the picture, and use Training Signal Annealing (TSA) to solve the impact of the imbalance of the label. At the same time, a new loss function is designed to ensure that the model is not affected by input noise which will improve the robustness of the model. We participated in sub-task a and our model based on BERT obtains 34.58{\%} macro F1 score, ranking 10/32.
CSECU\_KDE\_MA at SemEval-2020 Task 8: A Neural Attention Model for Memotion Analysis
Authors: Abu Nowshed Chy, Umme Aymun Siddiqua, Masaki Aono
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Optical Character Recognition (OCR)

A meme is a pictorial representation of an idea or theme. In the age of emerging volume of social media platforms, memes are spreading rapidly from person to person and becoming a trending ways of opinion expression. However, due to the multimodal characteristics of meme contents, detecting and analyzing the underlying emotion of a meme is a formidable task. In this paper, we present our approach for detecting the emotion of a meme defined in the SemEval-2020 Task 8. Our team CSECU{\_}KDE{\_}MA employs an attention-based neural network model to tackle the problem. Upon extracting the text contents from a meme using an optical character reader (OCR), we represent it using the distributed representation of words. Next, we perform the convolution based on multiple kernel sizes to obtain the higher-level feature sequences. The feature sequences are then fed into the attentive time-distributed bidirectional LSTM model to learn the long-term dependencies effectively. Experimental results show that our proposed neural model obtained competitive performance among the participants{'} systems.
Guoym at SemEval-2020 Task 8: Ensemble-based Classification of Visuo-Lingual Metaphor in Memes
Authors: YingMei Guo, Jinfa Huang, Yanlong Dong, Mingxing Xu
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020

In this paper, we describe our ensemble-based system designed by guoym Team for the SemEval-2020 Task 8, Memotion Analysis. In our system, we utilize five types of representation of data as input of base classifiers to extract information from different aspects. We train five base classifiers for each type of representation using five-fold cross-validation. Then the outputs of these base classifiers are combined through data-based ensemble method and feature-based ensemble method to make full use of all data and representations from different aspects. Our method achieves the performance within the top 2 ranks in the final leaderboard of Memotion Analysis among 36 Teams.
Hitachi at SemEval-2020 Task 8: Simple but Effective Modality Ensemble for Meme Emotion Recognition
Authors: Terufumi Morishita, Gaku Morio, Shota Horiguchi, Hiroaki Ozaki, Toshinori Miyoshi
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Emotion Recognition

Users of social networking services often share their emotions via multi-modal content, usually images paired with text embedded in them. SemEval-2020 task 8, Memotion Analysis, aims at automatically recognizing these emotions of so-called internet memes. In this paper, we propose a simple but effective Modality Ensemble that incorporates visual and textual deep-learning models, which are independently trained, rather than providing a single multi-modal joint network. To this end, we first fine-tune four pre-trained visual models (i.e., Inception-ResNet, PolyNet, SENet, and PNASNet) and four textual models (i.e., BERT, GPT-2, Transformer-XL, and XLNet). Then, we fuse their predictions with ensemble methods to effectively capture cross-modal correlations. The experiments performed on dev-set show that both visual and textual features aided each other, especially in subtask-C, and consequently, our system ranked 2nd on subtask-C.
Infotec + CentroGEO at SemEval-2020 Task 8: Deep Learning and Text Categorization approach for Memes classification
Authors: Guillermo Ruiz, Eric S. Tellez, Daniela Moctezuma, Sabino Miranda-Jim{\'e}nez, Tania Ram{\'\i}rez-delReal, Mario Graff
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Text Categorization

The information shared on social media is increasingly important; both images and text, and maybe the most popular combination of these two kinds of data are the memes. This manuscript describes our participation in Memotion task at SemEval 2020. This task is about to classify the memes in several categories related to the emotional content of them. For the proposed system construction, we used different strategies, and the best ones were based on deep neural networks and a text categorization algorithm. We obtained results analyzing the text and images separately, and also in combination. Our better performance was achieved in Task A, related to polarity classification.
KAFK at SemEval-2020 Task 8: Extracting Features from Pre-trained Neural Networks to Classify Internet Memes
Authors: Kaushik Amar Das, Arup Baruah, Ferdous Ahmed Barbhuiya, Kuntal Dey
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Classification, Meme Classification

This paper presents two approaches for the internet meme classification challenge of SemEval-2020 Task 8 by Team KAFK (cosec). The first approach uses both text and image features, while the second approach uses only the images. Error analysis of the two approaches shows that using only the images is more robust to the noise in the text on the memes. We utilize pre-trained DistilBERT and EfficientNet to extract features from the text and image of the memes respectively. Our classification systems obtained macro f1 score of 0.3286 for Task A and 0.5005 for Task B.
LT3 at SemEval-2020 Task 8: Multi-Modal Multi-Task Learning for Memotion Analysis
Authors: Pranaydeep Singh, Nina Bauwelinck, Els Lefever
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Continual Learning, Multi-Task Learning

Internet memes have become a very popular mode of expression on social media networks today. Their multi-modal nature, caused by a mixture of text and image, makes them a very challenging research object for automatic analysis. In this paper, we describe our contribution to the SemEval-2020 Memotion Analysis Task. We propose a Multi-Modal Multi-Task learning system, which incorporates {``}memebeddings{''}, viz. joint text and vision features, to learn and optimize for all three Memotion subtasks simultaneously. The experimental results show that the proposed system constantly outperforms the competition{'}s baseline, and the system setup with continual learning (where tasks are trained sequentially) obtains the best classification F1-scores.
Memebusters at SemEval-2020 Task 8: Feature Fusion Model for Sentiment Analysis on Memes Using Transfer Learning
Authors: Mayukh Sharma, Ilanthenral Kandasamy, W.b. Vasantha
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Sentiment Analysis, Sentiment Classification, Transfer Learning

In this paper, we describe our deep learning system used for SemEval 2020 Task 8: Memotion analysis. We participated in all the subtasks i.e Subtask A: Sentiment classification, Subtask B: Humor classification, and Subtask C: Scales of semantic classes. Similar multimodal architecture was used for each subtask. The proposed architecture makes use of transfer learning for images and text feature extraction. The extracted features are then fused together using stacked bidirectional Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) model with attention mechanism for final predictions. We also propose a single model for predicting semantic classes (Subtask B) as well as their scales (Subtask C) by branching the final output of the post LSTM dense layers. Our model was ranked 5 in Subtask B and ranked 8 in Subtask C and performed nicely in Subtask A on the leader board. Our system makes use of transfer learning for feature extraction and fusion of image and text features for predictions.
MemoSYS at SemEval-2020 Task 8: Multimodal Emotion Analysis in Memes
Authors: Irina Bejan
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Emotion Recognition, Transfer Learning

Internet memes are one of the most viral types of content in social media and are equally used in promoting hate speech. Towards a more broad understanding of memes, this paper describes the MemoSys system submitted in Task 8 of SemEval 2020, which aims to classify the sentiment of Internet memes and provide a minimum description of the type of humor it depicts (sarcastic, humorous, offensive, motivational) and its semantic scale. The solution presented covers four deep model architectures which are based on a joint fusion between the VGG16 pre-trained model for extracting visual information and the canonical BERT model or TF-IDF for text understanding. The system placed 5th of 36 participating systems in the task A, offering promising prospects to the use of transfer learning to approach Internet memes understanding.
SIS@IIITH at SemEval-2020 Task 8: An Overview of Simple Text Classification Methods for Meme Analysis
Authors: Sravani Boinepelli, Manish Shrivastava, Vasudeva Varma
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Classification, Optical Character Recognition (OCR), Sentiment Analysis, Sentiment Classification, text-classification, Text Classification

Memes are steadily taking over the feeds of the public on social media. There is always the threat of malicious users on the internet posting offensive content, even through memes. Hence, the automatic detection of offensive images/memes is imperative along with detection of offensive text. However, this is a much more complex task as it involves both visual cues as well as language understanding and cultural/context knowledge. This paper describes our approach to the task of SemEval-2020 Task 8: Memotion Analysis. We chose to participate only in Task A which dealt with Sentiment Classification, which we formulated as a text classification problem. Through our experiments, we explored multiple training models to evaluate the performance of simple text classification algorithms on the raw text obtained after running OCR on meme images. Our submitted model achieved an accuracy of 72.69{\%} and exceeded the existing baseline{'}s Macro F1 score by 8{\%} on the official test dataset. Apart from describing our official submission, we shall elucidate how different classification models respond to this task.
UI at SemEval-2020 Task 8: Text-Image Fusion for Sentiment Classification
Authors: Andi Suciati, Indra Budi
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Language Modeling, Language Modelling, Sentiment Analysis, Sentiment Classification

This paper describes our system, UI, for task A: Sentiment Classification in SemEval-2020 Task 8 Memotion Analysis. We use a common traditional machine learning, which is SVM, by utilizing the combination of text and images features. The data consist text that extracted from memes and the images of memes. We employ n-gram language model for text features and pre-trained model,VGG-16,for image features. After obtaining both features from text and images in form of 2-dimensional arrays, we concatenate and classify the final features using SVM. The experiment results show SVM achieved 35{\%} for its F1 macro, which is 0.132 points or 13.2{\%} above the baseline model.
UoR at SemEval-2020 Task 8: Gaussian Mixture Modelling (GMM) Based Sampling Approach for Multi-modal Memotion Analysis
Authors: Zehao Liu, Emmanuel Osei-Brefo, Siyuan Chen, HuiZhi Liang
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Sentiment Analysis, Sentiment Classification

Memes are widely used on social media. They usually contain multi-modal information such as images and texts, serving as valuable data sources to analyse opinions and sentiment orientations of online communities. The provided memes data often face an imbalanced data problem, that is, some classes or labelled sentiment categories significantly outnumber other classes. This often results in difficulty in applying machine learning techniques where balanced labelled input data are required. In this paper, a Gaussian Mixture Model sampling method is proposed to tackle the problem of class imbalance for the memes sentiment classification task. To utilise both text and image data, a multi-modal CNN-LSTM model is proposed to jointly learn latent features for positive, negative and neutral category predictions. The experiments show that the re-sampling model can slightly improve the accuracy on the trial data of sub-task A of Task 8. The multi-modal CNN-LSTM model can achieve macro F1 score 0.329 on the test set.
Urszula Wali\'nska at SemEval-2020 Task 8: Fusion of Text and Image Features Using LSTM and VGG16 for Memotion Analysis
Authors: Urszula Wali{\'n}ska, J{\k{e}}drzej Potoniec
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Sentiment Analysis, Sentiment Classification

In this paper, we describe the entry to the task of Memotion Analysis. The sentiment analysis of memes task, is motivated by a pervasive problem of offensive content spread in social media, up to the present time. In fact, memes are an important medium of expressing opinion and emotions, therefore they can be hateful at many times. In order to identify emotions expressed by memes we construct a tool based on neural networks and deep learning methods. It takes an advantage of a multi-modal nature of the task and performs fusion of image and text features extracted by models dedicated to this task. Moreover, we show that visual information might be more significant in the sentiment analysis of memes than textual one. Our solution achieved 0.346 macro F1-score in Task A {--} Sentiment Classification, which brought us to the 7th place in the official rank of the competition.
Deep Learning Brasil - NLP at SemEval-2020 Task 9: Sentiment Analysis of Code-Mixed Tweets Using Ensemble of Language Models
Authors: Manoel Ver{\'\i}ssimo dos Santos Neto, Ayrton Amaral, N{\'a}dia Silva, Anderson da Silva Soares
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Sentiment Analysis

In this paper, we describe a methodology to predict sentiment in code-mixed tweets (hindi-english). Our team called verissimo.manoel in CodaLab developed an approach based on an ensemble of four models (MultiFiT, BERT, ALBERT, and XLNET). The final classification algorithm was an ensemble of some predictions of all softmax values from these four models. This architecture was used and evaluated in the context of the SemEval 2020 challenge (task 9), and our system got 72.7{\%} on the F1 score.
IRLab\_DAIICT at SemEval-2020 Task 9: Machine Learning and Deep Learning Methods for Sentiment Analysis of Code-Mixed Tweets
Authors: Apurva Parikh, Abhimanyu Singh Bisht, Prasenjit Majumder
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: regression, Sentiment Analysis

The paper describes systems that our team IRLab{\_}DAIICT employed for the shared task Sentiment Analysis for Code-Mixed Social Media Text in SemEval 2020. We conducted our experiments on a Hindi-English CodeMixed Tweet dataset which was annotated with sentiment labels. F1-score was the official evaluation metric and our best approach, an ensemble of Logistic Regression, Random Forest and BERT, achieved an F1-score of 0.693.
JUNLP at SemEval-2020 Task 9: Sentiment Analysis of Hindi-English Code Mixed Data Using Grid Search Cross Validation
Authors: Avishek Garain, Sainik Mahata, Dipankar Das
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Machine Translation, Sentiment Analysis, Text Summarization, Translation

Code-mixing is a phenomenon which arises mainly in multilingual societies. Multilingual people, who are well versed in their native languages and also English speakers, tend to code-mix using English-based phonetic typing and the insertion of anglicisms in their main language. This linguistic phenomenon poses a great challenge to conventional NLP domains such as Sentiment Analysis, Machine Translation, and Text Summarization, to name a few. In this work, we focus on working out a plausible solution to the domain of Code-Mixed Sentiment Analysis. This work was done as participation in the SemEval-2020 Sentimix Task, where we focused on the sentiment analysis of English-Hindi code-mixed sentences. our username for the submission was {``}sainik.mahata{''} and team name was {``}JUNLP{''}. We used feature extraction algorithms in conjunction with traditional machine learning algorithms such as SVR and Grid Search in an attempt to solve the task. Our approach garnered an f1-score of 66.2{\%} when tested using metrics prepared by the organizers of the task.
LIMSI\_UPV at SemEval-2020 Task 9: Recurrent Convolutional Neural Network for Code-mixed Sentiment Analysis
Authors: Somnath Banerjee, Sahar Ghannay, Sophie Rosset, Anne Vilnat, Paolo Rosso
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Sentiment Analysis

This paper describes the participation of LIMSI{\_}UPV team in SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text. The proposed approach competed in SentiMix HindiEnglish subtask, that addresses the problem of predicting the sentiment of a given Hindi-English code-mixed tweet. We propose Recurrent Convolutional Neural Network that combines both the recurrent neural network and the convolutional network to better capture the semantics of the text, for code-mixed sentiment analysis. The proposed system obtained 0.69 (best run) in terms of F1 score on the given test data and achieved the 9th place (Codalab username: somban) in the SentiMix Hindi-English subtask.
Team\_Swift at SemEval-2020 Task 9: Tiny Data Specialists through Domain-Specific Pre-training on Code-Mixed Data
Authors: Aditya Malte, Pratik Bhavsar, Sushant Rathi
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020

Code-mixing is an interesting phenomenon where the speaker switches between two or more languages in the same text. In this paper, we describe an unconventional approach to tackling the SentiMix Hindi-English challenge (UID: aditya{\_}malte). Instead of directly fine-tuning large contemporary Transformer models, we train our own domain-specific embeddings and make use of them for downstream tasks. We also discuss how this technique provides comparable performance while making for a much more deployable and lightweight model. It should be noted that we have achieved the stated results without using any ensembling techniques, thus respecting a paradigm of efficient and production-ready NLP. All relevant source code shall be made publicly available to encourage the usage and reproduction of the results.
TueMix at SemEval-2020 Task 9: Logistic Regression with Linguistic Feature Set
Authors: Elizabeth Bear, Diana Constantina Hoefels, Mihai Manolescu
Date: 2020-12-01 00:00:00
Proceeding: SEMEVAL 2020
Tasks: regression

Commonly occurring in settings such as social media platforms, code-mixed content makes the task of identifying sentiment notably more challenging and complex due to the lack of structure and noise present in the data. SemEval-2020 Task 9, SentiMix, was organized with the purpose of detecting the sentiment of a given code-mixed tweet comprising Hindi and English. We tackled this task by comparing the performance of a system, TueMix - a logistic regression algorithm trained with three feature components: TF-IDF n-grams, monolingual sentiment lexicons, and surface features - with a neural network approach. Our results showed that TueMix outperformed the neural network approach and yielded a weighted F1-score of 0.685.