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This presentation describes our approach for SemEval-2023 Task 3: Detecting the category, the framing, and the persuasion techniques in online news in a multilingual setup. For Subtask 1 (News Genre), we propose an ensemble of fully trained and adapter mBERT models which was ranked joint-first for German, and had the highest mean rank of multi-language teams. For Subtask 2 (Framing), we achieved first place in 3 languages, and the best average rank across all the languages, by using two separate ensembles: a monolingual RoBERTa-MUPPETlarge and an ensemble of XLM-RoBERTaLARGE with adapters and task adaptive pretraining. For Subtask 3 (Persuasion Techniques), we trained a monolingual RoBERTa-Base model for English and a multilingual mBERT model for the remaining languages, which achieved top 10 for all languages, including 2nd for English. For each subtask, we compared monolingual and multilingual approaches, and considered class imbalance techniques

ACL 2023

SheffieldVeraAI at SemEval-2023 Task 3: Mono and Multilingual Approaches for News Genre, Topic and Persuasion Technique Classification

adapters

persuasion

framing

workshop paper

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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.

The MultiCoNER \RNum{2} shared task aims to tackle multilingual named entity recognition (NER) in fine-grained and noisy scenarios, and it inherits the semantic ambiguity and low-context setting of the MultiCoNER \RNum{1} task. To cope with these problems, the previous top systems in the MultiCoNER \RNum{1} either incorporate the knowledge bases or gazetteers. However, they still suffer from insufficient knowledge, limited context length, single retrieval strategy. In this paper, our team \textbf{DAMO-NLP} proposes a unified retrieval-augmented system (U-RaNER) for fine-grained multilingual NER. We perform error analysis on the previous top systems and reveal that their performance bottleneck lies in insufficient knowledge. Also, we discover that the limited context length causes the retrieval knowledge to be invisible to the model. To enhance the retrieval context, we incorporate the entity-centric Wikidata knowledge base, while utilizing the infusion approach to broaden the contextual scope of the model. Also, we explore various search strategies and refine the quality of retrieval knowledge. Our system\footnote{We will release the dataset, code, and scripts of our system at {\small \url{https://github.com/modelscope/AdaSeq/tree/master/examples/U-RaNER}}.} wins 9 out of 13 tracks in the MultiCoNER \RNum{2} shared task. 
Additionally, we compared our system with ChatGPT, one of the large language models which have unlocked strong capabilities on many tasks. The results show that there is still much room for improvement for ChatGPT on the extraction task.

DAMO-NLP at SemEval-2023 Task 2: A Unified Retrieval-augmented System for Multilingual Named Entity Recognition

nlp datasets annotated with human judgments are rife with disagreements between the judges. This is especially true for tasks depending on subjective judgments such as sentiment analysis or offensive language detection. Particularly in these latter cases, the nlp community has come to realize that the approach of ‘reconcil- ing’ these different subjective interpretations is inappropriate. Many nlp researchers have therefore concluded that rather than eliminat- ing disagreements from annotated corpora, we should preserve them–indeed, some argue that corpora should aim to preserve all annotator judgments. But this approach to corpus cre- ation for nlp has not yet been widely accepted. The objective of the LeWiDi series of shared tasks is to promote this approach to developing nlp models by providing a unified framework for training and evaluating with such datasets. We report on the second LeWiDi shared task, which differs from the first edition in three crucial respects: (i) it focuses entirely on nlp, instead of both nlp and computer vision tasks in its first edition; (ii) it focuses on subjective tasks, instead of covering different types of disagreements–as training with aggregated la- bels for subjective nlp tasks is a particularly obvious misrepresentation of the data; and (iii) for the evaluation, we concentrate on soft ap- proaches to evaluation. This second edition of LeWiDi attracted a wide array of participants resulting in 13 shared task submission papers.

SemEval-2023 Task 11: Learning with Disagreements (LeWiDi)

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.

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

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.

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