Canada

We present the first Afri-centric SemEval Shared task, Sentiment Analysis for African Languages - AfriSenti-SemEval. AfriSenti-SemEval is a sentiment classification challenge in 14 African languages (Muhammad et al., 2023; Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yoruba), using data labeled with 3 sentiment classes. We present three subtasks: (1) Task A: monolingual classification, which received 44 submissions; (2) Task B: multilingual classification, which received 32 submissions; and (3) Task C: zero-shot classification, which received 34 submissions. The best performance for tasks A and B was achieved by NLNDE team with 71.31 and 75.06 weighted F1, respectively. UCAS-IIE-NLP achieved the best average score for task C with 58.15 weighted F1. We describe the various approaches adopted by the top 10 systems and their approaches. The dataset is available at https://github.com/afrisenti-semeval/afrisent-semeval-2023.

ACL 2023

SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)

afrisenti

africanlp

datasets

sentiment

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workshop paper

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
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##### **[Conference Handbook](https://2023.aclweb.org/downloads/acl2023-handbook.pdf)**

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

Subjectivity and difference of opinion are key social phenomena, and it is crucial to take these into account in the annotation and detection process of derogatory textual content. In this paper, we use four datasets provided by SemEval-2023 Task 11 and fine-tune a BERT model to capture the disagreement in the annotation. We find individual annotator modeling and aggregation lowers the Cross-Entropy score by an average of 0.21, compared to the direct training on the soft labels. Our findings further demonstrate that annotator metadata contributes to the average 0.029 reduction in the Cross-Entropy score.

SafeWebUH at SemEval-2023 Task 11: Learning Annotator Disagreement in Derogatory Text: Comparison of Direct Training vs Aggregation

PAI at SemEval-2023 Task 4: A general multi-label classification system
with class-balanced loss function and ensemble module. We employ a multi-label classification model and utilize a class-balanced loss function. Our system wins 5 first places, 2 second places, and 6 third places out of 20 categories of the Human Value
Detection shared task.

PAI at SemEval-2023 Task 4: A General Multi-label Classification System with Class-balanced Loss Function and Ensemble Module

This paper describes our system submission
for SemEval-2023 Task 12 AfriSenti-SemEval:
Sentiment Analysis for African Languages. We
propose an XGBoost-based ensemble model
trained on emoticon frequency-based features
and the predictions of several statistical models
such as SVMs, Logistic Regression, Random
Forests, and BERT-based pre-trained language
models such as AfriBERTa and AfroXLMR.
We also report results from additional experiments not in the system. Our system achieves
a mixed bag of results, achieving a best rank of
7th in three of the languages - Igbo, Twi, and
Yoruba.

Witcherses at SemEval-2023 Task 12: Ensemble Learning for African Sentiment Analysis

We present our approach to the task of identification of persuasion techniques in text, which is a subtask of the SemEval2023 Task 3 on the multilingual detection of genre, framing, and persuasion techniques in online news. Our solution is based on an ensemble of a variety of pre-trained language models (PLMs) fine-tuned on the propaganda dataset. We first describe our system, the different experimental setups we considered, and then provide the results on the dev and test sets released by the organizers. The official evaluation shows our solution ranks 1st in English and attains high scores in all the other languages, i.e. French, German, Italian, Polish, and Russian. We also perform an extensive analysis of the data and the annotations to investigate how they can influence the quality of our systems.

APatt at SemEval-2023 Task 3: The Sapienza NLP System for Ensemble-based Multilingual Propaganda Detection

This paper describes the system we designed for our participation in SemEval-2023 Task 12 Track 6 about Algerian dialect sentiment analysis. We propose a transformer language model approach combined with a lexicon mixing terms and emojis which is used in a post-processing filtering stage. The Algerian sentiment lexicons were extracted manually from tweets. We report on our experiments on the Algerian dialect, where we compare the performance of MarBert to the one of ArabicBert ~and CamelBert ~on the training and development datasets of Task 12. We also analyze the contribution of our post-processing lexical filtering for sentiment analysis. Our system obtained an F1 score equal to 70 %, ranking 9-raise among 30 participants.

Foul at SemEval-2023 Task 12 : MARBERT Language Model and Lexical Filtering for Sentiments Analysis of Tweets in Algerian Arabic

Visual Word Sense Disambiguation (WSD), as a fine-grained image-text retrieval task, aims to identify the images that are relevant to ambiguous target words or phrases. However, the difficulties of limited contextual information and cross-linguistic background knowledge in text processing make this task challenging. To alleviate this issue, we propose a Fine-grained Contrastive Language-Image Learning (FCLL) model, which learns fine-grained image-text knowledge by employing a new fine-grained contrastive learning mechanism and enriches contextual information by establishing relationship between concepts and sentences. In addition, a new multimodal-multilingual knowledge base involving ambiguous target words is constructed for visual WSD. Experiment results on the benchmark datasets from SemEval-2023 Task 1 show that our FCLL ranks at the first in overall evaluation
with an average H@1 of 72.56% and an average MRR of 82.22%. The results demonstrate that FCLL is effective in inference on fine-grained language-vision knowledge. Source codes and the knowledge base are publicly available at https://github.com/CharlesYang030/FCLL.

TAM of SCNU at SemEval-2023 Task 1: FCLL: A Fine-grained Contrastive Language-Image Learning Model for Cross-language Visual Word Sense Disambiguation

In this paper, we proposed and explored the impact of four different dataset augmentation and extension strategies that we used for solving the subtask 3 of SemEval-2023 Task 3: multi-label persuasion techniques classification in a multi-lingual context. 
We consider two types of augmentation methods (one based on a modified version of synonym replacement and one based on translations) and two ways of extending the training dataset (using filtered data generated by GPT-3 and using a dataset from a previous competition).
We studied the effects of the aforementioned techniques by using the augmented and/or extended training dataset
to fine-tune a pretrained XLM-RoBERTa-Large model. Using the augmentation methods alone, we managed to obtain 3rd place for English, 13th place for Italian and between the 5th to 9th places for the other 7 languages during the competition.

Appeal for Attention at SemEval-2023 Task 3: Data Augmentation and Extension Strategies for Detection of Online News Persuasion Techniques

We present the best-performing solution to the SemEval 2023 Task 3 on the subtask 3 dedicated to persuasion techniques detection. Due to a high multilingual character of the input data and a large number of 23 predicted labels (causing a lack of labelled data for some language-label combinations), we opted for fine-tuning pre-trained transformer-based language models. Conducting multiple experiments, we find the best configuration, which consists of large multilingual model (XLM-RoBERTa large) trained jointly on all input data, with carefully calibrated confidence thresholds for seen and surprise languages separately. Our final system performed the best on 6 out of 9 languages (including two surprise languages) and achieved highly competitive results on the remaining three languages.

KInITVeraAI at SemEval-2023 Task 3: Simple Yet Powerful Multilingual Fine-Tuning for Persuasion Techniques Detection

This paper describes the system developed by the USTC-NELSLIP team for SemEval-2023 Task 2 Multilingual Complex Named Entity Recognition (MultiCoNER II). A method named Statistical Construction and Dual Adaptation of Gazetteer (SCDAG) is proposed for Multilingual Complex NER. The method first utilizes a statistics-based approach to construct a gazetteer. Secondly, the representations of gazetteer networks and language models are adapted by minimizing the KL divergence between them at both the sentence-level and entity-level. Finally, these two networks are then integrated for supervised named entity recognition (NER) training. The proposed method is applied to XLM-R with a gazetteer built from Wikidata, and shows great generalization ability across different tracks. Experimental results and detailed analysis verify the effectiveness of the proposed method. The official results show that our system ranked 1st on one track (Hindi) in this task.

USTC-NELSLIP at SemEval-2023 Task 2: Statistical Construction and Dual Adaptation of Gazetteer for Multilingual Complex NER

The MultiCoNER II shared task aims at detecting semantically ambiguous and complex named entities in short and low-context settings for multiple languages. The lack of context makes the recognition of ambiguous named entities challenging. To alleviate this issue, our team SRCB proposes an external knowledge based system, where we utilize 3 different types of external knowledge retrieved in different ways. Given an original text, our system retrieves the possible labels and the descriptions for each potential entity detected by a mention detection model. And we also retrieve a related document as extra context from Wikipedia for each original text. We concatenate the original text with the external knowledge as the input of NER models. The informative contextual representations with external knowledge significantly improve the NER performance in both Chinese and English tracks.

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Next from ACL 2023

SafeWebUH at SemEval-2023 Task 11: Learning Annotator Disagreement in Derogatory Text: Comparison of Direct Training vs Aggregation

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