ymoslem/acl-abstracts huggingface dataset:
Tsingriver at SemEval-2023 Task 10: Labeled Data Augmentation in Consistency Training (2023)

Semi-supervised learning has promising performance in deep learning, one of the approaches is consistency training on a large amount of unlabeled data to constrain model predictions to be invariant to input noise. However, The degree of correlation between unlabeled data and task objective directly affects model prediction performance. This paper describes our system designed for SemEval-2023 Task 10: Explainable Detection of Online Sexism. We utilize a consistency training framework and data augmentation as the main strategy to train a model. The score obtained by our method is 0.8180 in subtask A, ranking 57 in all the teams.
UnedMediaBiasTeam @ SemEval-2023 Task 3: Can We Detect Persuasive Techniques Transferring Knowledge From Media Bias Detection? (2023)

How similar is the detection of media bias to the detection of persuasive techniques? We have explored how transferring knowledge from one task to the other may help to improve the performance. This paper presents the systems developed for participating in the SemEval-2023 Task 3: Detecting the Genre, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup. We have participated in both the subtask 1: News Genre Categorisation, and the subtask 3: Persuasion Techniques Detection. Our solutions are based on two-stage fine-tuned multilingual models. We evaluated our approach on the 9 languages provided in the task. Our results show that the use of transfer learning from media bias detection to persuasion techniques detection is beneficial for the subtask of detecting the genre (macro F1-score of 0.523 in the English test set) as it improves previous results, but not for the detection of persuasive techniques (micro F1-score of 0.24 in the English test set).
NL4IA at SemEval-2023 Task 3: A Comparison of Sequence Classification and Token Classification to Detect Persuasive Techniques (2023)

The following system description presents our approach to the detection of persuasion techniques in online news. The given task has been framed as a multi-label classification problem. In a multi-label classification problem, each input chunkin this case paragraphis assigned one of several class labels. Span level annotations were also provided. In order to assign class labels to the given documents, we opted for RoBERTa (A Robustly Optimized BERT Pretraining Approach) for both approachessequence and token classification. Starting off with a pre-trained model for language representation, we fine-tuned this model on the given classification task with the provided annotated data in supervised training steps.
IITD at SemEval-2023 Task 2: A Multi-Stage Information Retrieval Approach for Fine-Grained Named Entity Recognition (2023)

MultiCoNER-II is a fine-grained Named Entity Recognition (NER) task that aims to identify ambiguous and complex named entities in multiple languages, with a small amount of contextual information available. To address this task, we propose a multi-stage information retrieval (IR) pipeline that improves the performance of language models for fine-grained NER. Our approach involves leveraging a combination of a BM25-based IR model and a language model to retrieve relevant passages from a corpus. These passages are then used to train a model that utilizes a weighted average of losses. The prediction is generated by a decoder stack that includes a projection layer and conditional random field. To demonstrate the effectiveness of our approach, we participated in the English track of the MultiCoNER-II competition. Our approach yielded promising results, which we validated through detailed analysis.
L3I++ at SemEval-2023 Task 2: Prompting for Multilingual Complex Named Entity Recognition (2023)

This paper summarizes the participation of the L3i laboratory of the University of La Rochelle in the SemEval-2023 Task 2, Multilingual Complex Named Entity Recognition (MultiCoNER II). Similar to MultiCoNER I, the task seeks to develop methods to detect semantic ambiguous and complex entities in short and low-context settings. However, MultiCoNER II adds a fine-grained entity taxonomy with over 30 entity types and corrupted data on the test partitions. We approach these complications following prompt-based learning as (1) a ranking problem using a seq2seq framework, and (2) an extractive question-answering task. Our findings show that even if prompting techniques have a similar recall to fine-tuned hierarchical language model-based encoder methods, precision tends to be more affected.
CNLP-NITS at SemEval-2023 Task 10: Online sexism prediction, PREDHATE! (2023)

Online sexism is a rising issue that threatens women`s safety, fosters hostile situations, and upholds social inequities. We describe a task SemEval-2023 Task 10 for creating English-language models that can precisely identify and categorize sexist content on internet forums and social platforms like Gab and Reddit as well to provide an explainability in order to address this problem. The problem is divided into three hierarchically organized subtasks: binary sexism detection, sexism by category, and sexism by fine-grained vector. The dataset consists of 20,000 labelled entries. For Task A, pertained models like Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM), which is called CNN-BiLSTM and Generative Pretrained Transformer 2 (GPT-2) models were used, as well as the GPT-2 model for Task B and C, and have provided experimental configurations. According to our findings, the GPT-2 model performs better than the CNN-BiLSTM model for Task A, while GPT-2 is highly accurate for Tasks B and C on the training, validation and testing splits of the training data provided in the task. Our proposed models allow researchers to create more precise and understandable models for identifying and categorizing sexist content in online forums, thereby empowering users and moderators.
garNER at SemEval-2023: Simplified Knowledge Augmentation for Multilingual Complex Named Entity Recognition (2023)

This paper presents our solution, garNER, to the SemEval-2023 MultiConer task. We propose a knowledge augmentation approach by directly querying entities from the Wikipedia API and appending the summaries of the entities to the input sentence. These entities are either retrieved from the labeled training set (Gold Entity) or from off-the-shelf entity taggers (Entity Extractor). Ensemble methods are then applied across multiple models to get the final prediction. Our analysis shows that the added contexts are beneficial only when such contexts are relevant to the target-named entities, but detrimental when the contexts are irrelevant.
D2KLab at SemEval-2023 Task 2: Leveraging T-NER to Develop a Fine-Tuned Multilingual Model for Complex Named Entity Recognition (2023)

This paper presents D2KLab`s system used for the shared task of {\textquotedblleft}Multilingual Complex Named Entity Recognition (MultiCoNER II){\textquotedblright}, as part of SemEval 2023 Task 2. The system relies on a fine-tuned transformer based language model for extracting named entities. In addition to the architecture of the system, we discuss our results and observations.
LTRC at SemEval-2023 Task 6: Experiments with Ensemble Embeddings (2023)

In this paper, we present our team`s involvement in Task 6: LegalEval: Understanding Legal Texts. The task comprised three subtasks, and we focus on subtask A: Rhetorical Roles prediction. Our approach included experimenting with pre-trained embeddings and refining them with statistical and neural classifiers. We provide a thorough examination ofour experiments, solutions, and analysis, culminating in our best-performing model and current progress. We achieved a micro F1 score of 0.6133 on the test data using fine-tuned LegalBERT embeddings.
TeamAmpa at SemEval-2023 Task 3: Exploring Multilabel and Multilingual RoBERTa Models for Persuasion and Framing Detection (2023)

This paper describes our submission to theSemEval 2023 Task 3 on two subtasks: detectingpersuasion techniques and framing. Bothsubtasks are multi-label classification problems. We present a set of experiments, exploring howto get robust performance across languages usingpre-trained RoBERTa models. We test differentoversampling strategies, a strategy ofadding textual features from predictions obtainedwith related models, and present bothinconclusive and negative results. We achievea robust ranking across languages and subtaskswith our best ranking being nr. 1 for Subtask 3on Spanish.
UM6P at SemEval-2023 Task 3: News genre classification based on transformers, graph convolution networks and number of sentences (2023)

This paper presents our proposed method for english documents genre classification in the context of SemEval 2023 task 3, subtask 1. Our method use ensemble technique to combine four distinct models predictions: Longformer, RoBERTa, GCN, and a sentences number-based model. Each model is optimized on simple objectives and easy to grasp. We provide snippets of code that define each model to make the reading experience better. Our method ranked 12th in documents genre classification for english texts.
Viettel-AI at SemEval-2023 Task 6: Legal Document Understanding with Longformer for Court Judgment Prediction with Explanation (2023)

Court Judgement Prediction with Explanation (CJPE) is a task in the field of legal analysis and evaluation, which involves predicting the outcome of a court case based on the available legal text and providing a detailed explanation of the prediction. This is an important task in the legal system as it can aid in decision-making and improve the efficiency of the court process. In this paper, we present a new approach to understanding legal texts, which are normally long documents, based on data-oriented methods. Specifically, we first try to exploit the characteristic of data to understand the legal texts. The output is then used to train the model using the Longformer architecture. Regarding the experiment, the proposed method is evaluated on the sub-task CJPE of the SemEval-2023 Task 6. Accordingly, our method achieves top 1 and top 2 on the classification task and explanation task, respectively. Furthermore, we present several open research issues for further investigations in order to improve the performance in this research field.
GunadarmaXBRIN at SemEval-2023 Task 12: Utilization of SVM and AfriBERTa for Monolingual, Multilingual, and Zero-shot Sentiment Analysis in African Languages (2023)

This paper describes our participation in Task 12: AfriSenti-SemEval 2023, i.e., track 12 of subtask A, track 16 of subtask B, and track 18 of subtask C. To deal with these three tracks, we utilize Support Vector Machine (SVM) + One vs Rest, SVM + One vs Rest with SMOTE, and AfriBERTa-large models. In particular, our SVM + One vs Rest with SMOTE model could obtain the highest weighted F1-Score for tracks 16 and 18 in the evaluation phase, that is, 65.14{\%} and 33.49{\%}, respectively. Meanwhile, our SVM + One vs Rest model could perform better than other models for track 12 in the evaluation phase.
MEERQAT-IRIT at SemEval-2023 Task 2: Leveraging Contextualized Tag Descriptors for Multilingual Named Entity Recognition (2023)

This paper describes the system we submitted to the SemEval 2023 Task 2 Multilingual Complex Named Entity Recognition (MultiCoNER II) in four monolingual tracks (English, Spanish, French, and Portuguese). Considering the low context setting and the fine-grained taxonomy presented in this task, we propose a system that leverages the language model representations using hand-crafted tag descriptors. We explored how integrating the contextualized representations of tag descriptors with a language model can help improve the model performance for this task. We performed our evaluations on the development and test sets used in the task for the Practice Phase and the Evaluation Phase respectively.
Unisa at SemEval-2023 Task 3: A SHAP-based method for Propaganda Detection (2023)

This paper presents proposed solutions for addressing two subtasks in SemEval-2023 Task 3: {\textquotedblleft}Detecting the Genre, the Framing, and the Persuasion techniques in online news in a multi-lingual setup. In subtask 1, {\textquotedblleft}News Genre Categorisation, the goal is to classify a news article as an opinion, a report, or a satire. In subtask 3, {\textquotedblleft}Detection of Persuasion Technique, the system must reveal persuasion techniques used in each news article paragraph choosing among23 defined methods. Solutions leverage the application of the eXplainable Artificial Intelligence (XAI) method, Shapley Additive Explanations (SHAP). In subtask 1, SHAP was used to understand what was driving the model to fail so that it could be improved accordingly. In contrast, in subtask 3, a re-calibration of the Attention Mechanism was realized by extracting critical tokens for each persuasion technique. The underlying idea is the exploitation of XAI for countering the overfitting of the resulting model and attempting to improve the performance when there are few samples in the training data. The achieved performance on English for subtask 1 ranked 6th with an F1-score of 58.6{\%} (despite 78.4{\%} of the 1st) and for subtask 3 ranked 12th with a micro-averaged F1-score of 29.8{\%} (despite 37.6{\%} of the 1st).
DUTIR at SemEval-2023 Task 10: Semi-supervised Learning for Sexism Detection in English (2023)

Sexism is an injustice afflicting women and has become a common form of oppression in social media. In recent years, the automatic detection of sexist instances has been utilized to combat this oppression. The Subtask A of SemEval-2023 Task 10, Explainable Detection of Online Sexism, aims to detect whether an English-language post is sexist. In this paper, we describe our system for the competition. The structure of the classification model is based on RoBERTa, and we further pre-train it on the domain corpus. For fine-tuning, we adopt Unsupervised Data Augmentation (UDA), a semi-supervised learning approach, to improve the robustness of the system. Specifically, we employ Easy Data Augmentation (EDA) method as the noising operation for consistency training. We train multiple models based on different hyperparameter settings and adopt the majority voting method to predict the labels of test entries. Our proposed system achieves a Macro-F1 score of 0.8352 and a ranking of 41/84 on the leaderboard of Subtask A.
NetEase.AI at SemEval-2023 Task 2: Enhancing Complex Named Entities Recognition in Noisy Scenarios via Text Error Correction and External Knowledge (2023)

Complex named entities (NE), like the titles of creative works, are not simple nouns and pose challenges for NER systems. In the SemEval 2023, Task 2: MultiCoNER II was proposed, whose goal is to recognize complex entities against out of knowledge-base entities and noisy scenarios. To address the challenges posed by MultiCoNER II, our team NetEase.AI proposed an entity recognition system that integrates text error correction system and external knowledge, which can recognize entities in scenes that contain entities out of knowledge base and text with noise. Upon receiving an input sentence, our systems will correct the sentence, extract the entities in the sentence as candidate set using the entity recognition model that incorporates the gazetteer information, and then use the external knowledge to classify the candidate entities to obtain entity type features. Finally, our system fused the multi-dimensional features of the candidate entities into a stacking model, which was used to select the correct entities from the candidate set as the final output. Our system exhibited good noise resistance and excellent entity recognition performance, resulting in our team`s first place victory in the Chinese track of MultiCoNER II.
IRIT\_IRIS\_A at SemEval-2023 Task 6: Legal Rhetorical Role Labeling Supported by Dynamic-Filled Contextualized Sentence Chunks (2023)

This work presents and evaluates an approach to efficiently leverage the context exploitation ability of pre-trained Transformer models as a way of boosting the performance of models tackling the Legal Rhetorical Role Labeling task. The core idea is to feed the model with sentence chunks that are assembled in a way that avoids the insertion of padding tokens and the truncation of sentences and, hence, obtain better sentence embeddings. The achieved results show that our proposal is efficient, despite its simplicity, since models based on it overcome strong baselines by 3.76{\%} in the worst case and by 8.71{\%} in the best case.
Togedemaru at SemEval-2023 Task 8: Causal Medical Claim Identification and Extraction from Social Media Posts (2023)

The {\textquotedblleft}Causal Medical Claim Identification and Extraction from Social Media Posts task at SemEval 2023 competition focuses on identifying and validating medical claims in English, by posing two subtasks on causal claim identification and PIO (Population, Intervention, Outcome) frame extraction. In the context of SemEval, we present a method for sentence classification in four categories (claim, experience, experience{\_}based{\_}claim or a question) based on BioBERT model with a MLP layer. The website from which the dataset was gathered, Reddit, is a social news and content discussion site. The evaluation results show the effectiveness of the solution of this study (83.68{\%}).
FramingFreaks at SemEval-2023 Task 3: Detecting the Category and the Framing of Texts as Subword Units with Traditional Machine Learning (2023)

This paper describes our participation as team FramingFreaks in the SemEval-2023 task 3 {\textquotedblleft}Category and Framing Predictions in online news in a multi-lingual setup.{\textquotedblright} We participated in subtasks 1 and 2. Our approach was to classify texts by splitting them into subwords to reduce the feature set size and then using these tokens as input in Support Vector Machine (SVM) or logistic regression classifiers. Our results are similar to the baseline results.
Lazybob at SemEval-2023 Task 9: Quantifying Intimacy of Multilingual Tweets with Multi-Task Learning (2023)

This study presents a systematic method for analyzing the level of intimacy in tweets across ten different languages, using multi-task learning for SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis. The system begins with the utilization of the official training data, and then we experiment with different fine-tuning tricks and effective strategies, such as data augmentation, multi-task learning, etc. Through additional experiments, the approach is shown to be effective for the task. To enhance the model`s robustness, different transformer-based language models and some widely-used plug-and-play priors are incorporated into our system. Our final submission achieved a Pearson R of 0.6160 for the intimacy score on the official test set, placing us at the top of the leader board among 45 teams.
HITSZQ at SemEval-2023 Task 10: Category-aware Sexism Detection Model with Self-training Strategy (2023)

This paper describes our system used in the SemEval-2023 {\textbackslash}textit{\{}Task 10 Explainable Detection of Online Sexism (EDOS){\}}. Specifically, we participated in subtask B: a 4-class sexism classification task, and subtask C: a more fine-grained (11-class) sexism classification task, where it is necessary to predict the category of sexism. We treat these two subtasks as one multi-label hierarchical text classification problem, and propose an integrated sexism detection model for improving the performance of the sexism detection task. More concretely, we use the pre-trained BERT model to encode the text and class label and a hierarchy-relevant structure encoder is employed to model the relationship between classes of subtasks B and C. Additionally, a self-training strategy is designed to alleviate the imbalanced problem of distribution classes. Extensive experiments on subtasks B and C demonstrate the effectiveness of our proposed approach.
mCPT at SemEval-2023 Task 3: Multilingual Label-Aware Contrastive Pre-Training of Transformers for Few- and Zero-shot Framing Detection (2023)

This paper presents the winning system for the zero-shot Spanish framing detection task, which also achieves competitive places in eight additional languages. The challenge of the framing detection task lies in identifying a set of 14 frames when only a few or zero samples are available, i.e., a multilingual multi-label few- or zero-shot setting. Our developed solution employs a pre-training procedure based on multilingual Transformers using a label-aware contrastive loss function. In addition to describing the system, we perform an embedding space analysis and ablation study to demonstrate how our pre-training procedure supports framing detection to advance computational framing analysis.
SSNSheerinKavitha at SemEval-2023 Task 7: Semantic Rule Based Label Prediction Using TF-IDF and BM25 Techniques (2023)

The advancement in the healthcare sector assures improved diagnosis and supports appropriate decision making in medical domain. The medical domain data can be either radiology images or clinical data. The clinical data plays a major role in the healthcare sector by preventing and treating the health problem based on the evidence learned from the trials. This paper is related to multi-evidence natural language inference for clinical trial data analysis and its solution for the given subtasks (SemEval 2023 Task 7 - NLI4CT). In subtask 1 of NLI4CT, the inference relationship (entailment or contradiction) between the Clinical Trial Reports (CTRs) statement pairs with respect to the Clinical Trial Data (CTD) statement are determined. In subtask 2 of NLI4CT, predicted label (inference relationship) are defined and justified using set of supporting facts extracted from the premises. The objective of this work is to derive the conclusion from premises (CTRs statement pairs) and extracting the supporting premises using proposed Semantic Rule based Clinical Data Analysis (SRCDA) approach. From the results, the proposed model attained an highest F1-score of 0.667 and 0.716 for subtasks 1 and 2 respectively. The novelty of this proposed approach includes, creation of External Knowledge Base (EKB) along with its suitable semantic rules based on the input statements.
Janko at SemEval-2023 Task 2: Bidirectional LSTM Model Based on Pre-training for Chinese Named Entity Recognition (2023)

This paper describes the method we submitted as the Janko team in the SemEval-2023 Task 2,Multilingual Complex Named Entity Recognition (MultiCoNER 2). We only participated in the Chinese track. In this paper, we implement the BERT-BiLSTM-RDrop model. We use the fine-tuned BERT models, take the output of BERT as the input of the BiLSTM network, and finally use R-Drop technology to optimize the loss function. Our submission achieved a macro-averaged F1 score of 0.579 on the testset.
HHS at SemEval-2023 Task 10: A Comparative Analysis of Sexism Detection Based on the RoBERTa Model (2023)

This paper describes the methods and models applied by our team HHS in SubTask-A of SemEval-2023 Task 10 about sexism detection. In this task, we trained with the officially released data and analyzed the performance of five models, TextCNN, BERT, RoBERTa, XLNet, and Sup-SimCSE-RoBERTa. The experiments show that most of the models can achieve good results. Then, we tried data augmentation, model ensemble, dropout, and other operations on several of these models, and compared the results for analysis. In the end, the most effective approach that yielded the best results on the test set involved the following steps: enhancing the sexist data using dropout, feeding it as input to the Sup-SimCSE-RoBERTa model, and providing the raw data as input to the XLNet model. Then, combining the outputs of the two methods led to even better results. This method yielded a Macro-F1 score of 0.823 in the final evaluation phase of the SubTask-A of the competition.