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This paper summarizes the participation of the L3i laboratory of the University of La Rochelle in the SemEval-2022 Task 11, Multilingual Complex Named Entity Recognition (MultiCoNER). The task focuses on detecting semantically ambiguous and complex entities in short and low-context monolingual and multilingual settings. We argue that using a language-specific and a multilingual language model could improve the performance of multilingual and mixed NER. Also, we consider that using additional contexts from the training set could improve the performance of a NER on short texts. Thus, we propose a straightforward technique for generating additional contexts with and without the presence of entities. Our findings suggest that, in our internal experimental setup, this approach is promising. However, we ranked above average for the high-resource languages and lower than average for low-resource and multilingual models.

NAACL 2022

L3i at SemEval-2022 Task 11: Straightforward Additional Context for Multilingual Named Entity Recognition

topic detection

multilingualism

named entity recognition

language models

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## Welcome to NAACL 2022!
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2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

A logistic regression model only fed with character and word n-grams is proposed for the SemEval-2022 Task 4 on Patronizing and Condescending Language Detection (PCL). It obtained an average level of performance, well above the performance of a system that tries to guess without using any knowledge about the task, but much lower than the best teams. To facilitate the interpretation of the performance scores, the F1 measure, the best level of performance of a system that tries to guess without using any knowledge is calculated and used to correct the F1 scores in the manner of a Kappa. As the proposed model is very similar to the one that performed well on a task requiring to automatically identify hate speech and offensive content, this paper confirms the difficulty of PCL detection.

SATLab at SemEval-2022 Task 4: Trying to Detect Patronizing and Condescending Language with only Character and Word N-grams

This paper describes the approach developed by the LT3 team in the Intended Sarcasm Detection task at SemEval-2022 Task 6. We considered the binary classification subtask A for English data. The presented system is based on the fuzzy-rough nearest neighbor classification method using various text embedding techniques. Our solution reached 9th place in the official leader-board for English subtask A.

LT3 at SemEval-2022 Task 6: Fuzzy-Rough Nearest Neighbor Classification for Sarcasm Detection

The detection of offensive, hateful content on social media is a challenging problem that affects many online users on a daily basis. Hateful content is often used to target a group of people based on ethnicity, gender, religion and other factors. The hate or contempt toward women has been increasing on social platforms. Misogynous content detection is especially challenging when textual and visual modalities are combined to form a single context, e.g., an overlay text embedded on top of an image, also known as meme. In this paper, we present a multimodal architecture that combines textual and visual features to detect misogynous memes. The proposed architecture is evaluated in the SemEval-2022 Task 5: MAMI - Multimedia Automatic Misogyny Identification challenge under the team name TIB-VA. We obtained the best result in the Task-B where the challenge is to classify whether a given document is misogynous and further identify the following sub-classes: shaming, stereotype, objectification, and violence.

TIB-VA at SemEval-2022 Task 5: A Multimodal Architecture for the Detection and Classification of Misogynous Memes

This paper describes the participation of team DUCS at SemEval 2022 Task 6: iSarcasmEval - Intended Sarcasm Detection in English and Arabic. Team DUCS participated in SubTask A of iSarcasmEval which was to determine if the given English text was sarcastic or not. In this work, emojis were utilized to capture how they contributed to the sarcastic nature of a text. It is observed that emojis can augment or reverse the polarity of a given statement. Thus sentiment polarities and intensities of emojis, as well as those of text, were computed to determine sarcasm. Use of capitalization, word repetition, and use of punctuation marks like '!' were factored in as sentiment intensifiers. An NLP augmenter was used to tackle the imbalanced nature of the sarcasm dataset. Several architectures comprising of various ML and DL classifiers, and transformer models like BERT and Multimodal BERT were experimented with. It was observed that Multimodal BERT outperformed other architectures tested and achieved an F1-score of 30.71%. The key takeaway of this study was that sarcastic texts are usually positive sentences. In general emojis with positive polarity are used more than those with negative polarities in sarcastic texts.

DUCS at SemEval-2022 Task 6: Exploring Emojis and Sentiments for Sarcasm Detection

We present a system that creates pair-wise co- sine and arccosine sentence similarity matri- ces using multilingual sentence embeddings obtained from pre-trained SBERT and Universal Sentence Encoder models respectively. For each news article sentence, it searches the most similar sentence from the other article and com- putes an average score. Further, a convolutional neural network calculates a total similarity score for the article pairs on these matrices. Finally, a random forest regressor merges the previous results to a final score that can optionally be extended with a publishing date score.

WueDevils at SemEval-2022 Task 8: Multilingual News Article Similarity via Pair-Wise Sentence Similarity Matrices

In this work, we introduce our system to the SemEval 2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER) competition. Our team (KDDIE) attempted the sub-task of Named Entity Recognition (NER) for the language of English in the challenge and reported our results. For this task, we use transfer learning method: fine-tuning the pre-trained language models (PLMs) on the competition dataset. Our two approaches are the BERT-based PLMs and PLMs with additional layer such as Condition Random Field. We report our finding and results in this report.

KDDIE at SemEval-2022 Task 11: Using DeBERTa for Named Entity Recognition

Described are our two entries "emukans" and "guntis" for the definition modeling track of CODWOE SemEval-2022 Task 1. Our ap- proach is based on careful scaling of a GRU re- current neural network, which exhibits double descent of errors, corresponding to significant improvements also per human judgement. Our results are in the middle of the ranking table per official automatic metrics.

RIGA at SemEval-2022 Task 1: Scaling Recurrent Neural Networks for CODWOE Dictionary Modeling

Memes have become quite common in day-to- day communications on social media platforms. They often appear to be amusing, evoking and attractive to audiences. However, some memes containing malicious content can be harmful to targeted groups. In this paper, we study misogynous meme detection, a shared task in SemEval 2022 - Multimedia Automatic Misogyny Identification (MAMI). The challenge of misogynous meme detection is to co-represent multi-modal features. To tackle with this challenge, we propose a Multi-modal Multi-task Variational AutoEncoder (MMVAE) to learn an effective co- representation of visual and textual features in the latent space. Our goal is to automatically determine if a meme contains misogynous information and then identify its fine-grained category. Our model achieves F1 scores of 0.723 on the MAMI sub-task A and 0.634 on sub-task B. We carry out comprehensive experiments on our model’s architecture and show that our approach significantly outperforms several strong uni-modal and multi-modal approaches. Our code is released on github.

MMVAE at SemEval-2022 Task 5: A Multi-modal Multi-task VAE on Misogynous Meme Detection

Style transfer is the task of transferring a sentence into the target style while keeping its content. The major challenge is that parallel corpora are not available for various domains. In this paper, we propose a Mask-And-Regenerate approach (MAR). It learns from unpaired sentences by modifying the word-level style attributes. We cautiously integrate the deletion, insertion and substitution operations into our model. This enables our model to automatically apply different edit operations for different sentences. Specifically, we train a multilayer perceptron (MLP) as a style classifier to find out and mask style-characteristic words in the source inputs. Then we learn a language model on non-parallel data sets to score sentences and remove unnecessary masks. Finally, the masked source sentences are input to a Transformer to perform style transfer. The final results show that our proposed model exceeds baselines by about 2 per cent of accuracy for both sentiment and style transfer tasks with comparable or better content retention.

Mask and Regenerate: A Classifier-based Approach for Unpaired Sentiment Transformation of Reviews for Electronic Commerce Websites

In recent years, grey social media platforms, those with a loose moderation policy on cyberbullying, have been attracting more users. Recently, data collected from these types of platforms have been used to pre-train word embeddings (social-media-based), yet these word embeddings have not been investigated for social NLP related tasks. In this paper, we carried out a comparative study between social-media-based and non-social-media-based word embeddings on two social NLP tasks: Detecting cyberbullying and Measuring social bias. Our results show that using social-media-based word embeddings as input features, rather than non-social-media-based embeddings, leads to better cyberbullying detection performance. We also show that some word embeddings are more useful than others for categorizing offensive words. However, we do not find strong evidence that certain word embeddings will necessarily work best when identifying certain categories of cyberbullying within our datasets. Finally, We show even though most of the state-of-the-art bias metrics ranked social-media-based word embeddings as the most socially biased, these results remain inconclusive and further research is required.

L3i at SemEval-2022 Task 11: Straightforward Additional Context for Multilingual Named Entity Recognition

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