ymoslem/acl-abstracts huggingface dataset:
IXA/Cogcomp at SemEval-2023 Task 2: Context-enriched Multilingual Named Entity Recognition Using Knowledge Bases (2023)

Named Entity Recognition (NER) is a core natural language processing task in which pre-trained language models have shown remarkable performance. However, standard benchmarks like CoNLL 2003 do not address many of the challenges that deployed NER systems face, such as having to classify emerging or complex entities in a fine-grained way. In this paper we present a novel NER cascade approach comprising three steps: first, identifying candidate entities in the input sentence; second, linking the each candidate to an existing knowledge base; third, predicting the fine-grained category for each entity candidate. We empirically demonstrate the significance of external knowledge bases in accurately classifying fine-grained and emerging entities. Our system exhibits robust performance in the MultiCoNER2 shared task, even in the low-resource language setting where we leverage knowledge bases of high-resource languages.
ACCEPT at SemEval-2023 Task 3: An Ensemble-based Approach to Multilingual Framing Detection (2023)

This paper describes the system and experimental results of an ensemble-based approach tomultilingual framing detection for the submission of the ACCEPT team to the SemEval-2023 Task 3 on Framing Detection (Subtask 2). The approach is based on an ensemble that combines three different methods: a classifier based on large language models, a classifier based on static word embeddings, and an approach that uses external commonsense knowledge graphs, in particular, ConceptNet. The results of the three classification heads are aggregated into an overall prediction for each frame class. Our best submission yielded a micro F1-score of 50.69{\%} (rank 10) and a macro F1-score of 50.20{\%} (rank 3) for English articles. Our experimental results show that static word embeddings and knowledge graphs are useful components for frame detection, while the ensemble of all three methods combines the strengths of our three proposed methods. Through system ablations, we show that the commonsenseguided knowledge graphs are the outperforming method for many languages.
Noam Chomsky at SemEval-2023 Task 4: Hierarchical Similarity-aware Model for Human Value Detection (2023)

This paper presents a hierarchical similarity-aware approach for the SemEval-2023 task 4 human value detection behind arguments using SBERT. The approach takes similarity score as an additional source of information between the input arguments and the lower level of labels in a human value hierarchical dataset. Our similarity-aware model improved the similarity-agnostic baseline model, especially showing a significant increase in or the value categories with lowest scores by the baseline model.
NLP-Titan at SemEval-2023 Task 6: Identification of Rhetorical Roles Using Sequential Sentence Classification (2023)

The analysis of legal cases poses a considerable challenge for researchers, practitioners, and academicians due to the lengthy and intricate nature of these documents. Developing countries such as India are experiencing a significant increase in the number of pending legal cases, which are often unstructured and difficult to process using conventional methods. To address this issue, the authors have implemented a sequential sentence classification process, which categorizes legal documents into 13 segments, known as Rhetorical Roles. This approach enables the extraction of valuable insights from the various classes of the structured document. The performance of this approach was evaluated using the F1 score, which measures the model`s precision and recall. The authors' approach achieved an F1 score of 0.83, which surpasses the baseline score of 0.79 established by the task organizers. The authors have combined sequential sentence classification and the SetFit method in a hierarchical manner by combining similar classes to achieve this score.
AdamR at SemEval-2023 Task 10: Solving the Class Imbalance Problem in Sexism Detection with Ensemble Learning (2023)

The Explainable Detection of Online Sexism task presents the problem of explainable sexism detection through fine-grained categorisation of sexist cases with three subtasks. Our team experimented with different ways to combat class imbalance throughout the tasks using data augmentation and loss alteration techniques. We tackled the challenge by utilising ensembles of Transformer models trained on different datasets, which are tested to find the balance between performance and interpretability. This solution ranked us in the top 40{\%} of teams for each of the tracks.
I2C Huelva at SemEval-2023 Task 4: A Resampling and Transformers Approach to Identify Human Values behind Arguments (2023)

This paper presents the approaches proposedfor I2C Group to address the SemEval-2023Task 4: Identification of Human Values behindArguments (ValueEval){\textquotedblright}, whose goal is to classify 20 different categories of human valuesgiven a textual argument. The dataset of thistask consists of one argument per line, including its unique argument ID, conclusion, stanceof the premise towards the conclusion and thepremise text. To indicate whether the argumentdraws or not on that category a binary indication (1 or 0) is included. Participants can submit approaches that detect one, multiple, or allof these values in arguments. The task providesan opportunity for researchers to explore theuse of automated techniques to identify humanvalues in text and has potential applications invarious domains such as social science, politics,and marketing. To deal with the imbalancedclass distribution given, our approach undersamples the data. Additionally, the three components of the argument (conclusion, stanceand premise) are used for training. The systemoutperformed the BERT baseline according toofficial evaluation metrics, achieving a f1 scoreof 0.46.
MLlab4CS at SemEval-2023 Task 2: Named Entity Recognition in Low-resource Language Bangla Using Multilingual Language Models (2023)

Extracting of NERs from low-resource languages and recognizing their types is one of the important tasks in the entity extraction domain. Recently many studies have been conducted in this area of research. In our study, we introduce a system for identifying complex entities and recognizing their types from low-resource language Bangla, which was published in SemEval Task 2 MulitCoNER II 2023. For this sequence labeling task, we use a pre-trained language model built on a natural language processing framework. Our team name in this competition is MLlab4CS. Our model Muril produces a macro average F-score of $76.27\%$, which is a comparable result for this competition.
Kb at SemEval-2023 Task 3: On Multitask Hierarchical BERT Base Neural Network for Multi-label Persuasion Techniques Detection (2023)

This paper presents a solution for Semeval 2023 subtask3 of task 3: persuasion techniques in paragraphs detection. The aim of this task is to identify all persuasion techniques in each paragraph of a given news article. We use hierarchical multitask neural networks combined with transformers. Span detection is an auxiliary task that helps in the main task: identifying propaganda techniques. Our experiments show that if we change the index of BERT embedding from the first token of the whole input to the first token of the identified span, it can improve performance. Span and label detection can be performed using one network, so we save data and, when data is limited, we can use more of it for training.
PoliToHFI at SemEval-2023 Task 6: Leveraging Entity-Aware and Hierarchical Transformers For Legal Entity Recognition and Court Judgment Prediction (2023)

The use of Natural Language Processing techniques in the legal domain has become established for supporting attorneys and domain experts in content retrieval and decision-making. However, understanding the legal text poses relevant challenges in the recognition of domain-specific entities and the adaptation and explanation of predictive models. This paper addresses the Legal Entity Name Recognition (L-NER) and Court judgment Prediction (CPJ) and Explanation (CJPE) tasks. The L-NER solution explores the use of various transformer-based models, including an entity-aware method attending domain-specific entities. The CJPE proposed method relies on hierarchical BERT-based classifiers combined with local input attribution explainers. We propose a broad comparison of eXplainable AI methodologies along with a novel approach based on NER. For the L-NER task, the experimental results remark on the importance of domain-specific pre-training. For CJP our lightweight solution shows performance in line with existing approaches, and our NER-boosted explanations show promising CJPE results in terms of the conciseness of the prediction explanations.
UO-LouTAL at SemEval-2023 Task 6: Lightweight Systems for Legal Processing (2023)

This paper presents the work produced by students of the University of Orlans Masters in Natural Language Processing program by way of participating in SemEval Task 6, LegalEval, which aims to enhance the capabilities of legal professionals through automated systems. Two out of the three sub-tasks available {--} Rhetorical Role prediction (RR) and Legal Named Entity Recognition (L-NER) {--} were tackled, with the express intent of developing lightweight and interpretable systems. For the L-NER sub-task, a CRF model was trained, augmented with post-processing rules for some named entity types. A macro F1 score of 0.74 was obtained on the DEV set, and 0.64 on the evaluation set. As for the RR sub-task, two sentence classification systems were built: one based on the Bag-of-Words technique with L-NER system output integrated, the other using a sentence-transformer approach. Rule-based post-processing then converted the results of the sentence classification systems into RR predictions. The better-performing Bag-of-Words system obtained a macro F1 score of 0.49 on the DEV set and 0.57 on the evaluation set.
NLP-LTU at SemEval-2023 Task 10: The Impact of Data Augmentation and Semi-Supervised Learning Techniques on Text Classification Performance on an Imbalanced Dataset (2023)

In this paper, we propose a methodology fortask 10 of SemEval23, focusing on detectingand classifying online sexism in social me-dia posts. The task is tackling a serious is-sue, as detecting harmful content on socialmedia platforms is crucial for mitigating theharm of these posts on users. Our solutionfor this task is based on an ensemble of fine-tuned transformer-based models (BERTweet,RoBERTa, and DeBERTa). To alleviate prob-lems related to class imbalance, and to improvethe generalization capability of our model, wealso experiment with data augmentation andsemi-supervised learning. In particular, fordata augmentation, we use back-translation, ei-ther on all classes, or on the underrepresentedclasses only. We analyze the impact of thesestrategies on the overall performance of thepipeline through extensive experiments. whilefor semi-supervised learning, we found thatwith a substantial amount of unlabelled, in-domain data available, semi-supervised learn-ing can enhance the performance of certainmodels. Our proposed method (for which thesource code is available on Github12) attainsan F 1-score of 0.8613 for sub-taskA, whichranked us 10th in the competition.
John-Arthur at SemEval-2023 Task 4: Fine-Tuning Large Language Models for Arguments Classification (2023)

This paper presents the system submissions of the John-Arthur team to the SemEval Task 4 {\textquotedblleft}ValueEval: Identification of Human Values behind Arguments{\textquotedblright}. The best system of the team was ranked 3rd and the overall rank of the team was 2nd (the first team had the two best systems). John-Arthur team models the ValueEval problem as a multi-class, multi-label text classification problem. The solutions leverage recently proposed large language models that are fine-tuned on the provided datasets. To boost the achieved performance we employ different best practises whose impact on the model performance we evaluate here. The code ispublicly available at github and the model onHuggingface hub.
NAP at SemEval-2023 Task 3: Is Less Really More? (Back-)Translation as Data Augmentation Strategies for Detecting Persuasion Techniques (2023)

Persuasion techniques detection in news in a multi-lingual setup is non-trivial and comes with challenges, including little training data. Our system successfully leverages (back-)translation as data augmentation strategies with multi-lingual transformer models for the task of detecting persuasion techniques. The automatic and human evaluation of our augmented data allows us to explore whether (back-)translation aid or hinder performance. Our in-depth analyses indicate that both data augmentation strategies boost performance; however, balancing human-produced and machine-generated data seems to be crucial.
PoliTo at SemEval-2023 Task 1: CLIP-based Visual-Word Sense Disambiguation Based on Back-Translation (2023)

Visual-Word Sense Disambiguation (V-WSD) entails resolving the linguistic ambiguity in a text by selecting a clarifying image from a set of (potentially misleading) candidates. In this paper, we address V-WSD using a state-of-the-art Image-Text Retrieval system, namely CLIP. We propose to alleviate the linguistic ambiguity across multiple domains and languages via text and image augmentation. To augment the textual content we rely on back-translation with the aid of a variety of auxiliary languages. The approach based on finetuning CLIP on the full phrases is effective in accurately disambiguating words and incorporating back-translation enhance the system`s robustness and performance on the test samples written in Indo-European languages.
FMI-SU at SemEval-2023 Task 7: Two-level Entailment Classification of Clinical Trials Enhanced by Contextual Data Augmentation (2023)

The paper presents an approach for solving SemEval 2023 Task 7 - identifying the inference relation in a clinical trials dataset. The system has two levels for retrieving relevant clinical trial evidence for a statement and then classifying the inference relation based on the relevant sentences. In the first level, the system classifies the evidence-statement pairs as relevant or not using a BERT-based classifier and contextual data augmentation (subtask 2). Using the relevant parts of the clinical trial from the first level, the system uses an additional BERT-based classifier to determine whether the relation is entailment or contradiction (subtask 1). In both levels, the contextual data augmentation is showing a significant improvement in the F1 score on the test set of 3.7{\%} for subtask 2 and 7.6{\%} for subtask 1, achieving final F1 scores of 82.7{\%} for subtask 2 and 64.4{\%} for subtask 1.
ML Mob at SemEval-2023 Task 1: Probing CLIP on Visual Word-Sense Disambiguation (2023)

Successful word sense disambiguation (WSD)is a fundamental element of natural languageunderstanding. As part of SemEval-2023 Task1, we investigate WSD in a multimodal setting,where ambiguous words are to be matched withcandidate images representing word senses. Wecompare multiple systems based on pre-trainedCLIP models. In our experiments, we findCLIP to have solid zero-shot performance onmonolingual and multilingual data. By em-ploying different fine-tuning techniques, we areable to further enhance performance. However,transferring knowledge between data distribu-tions proves to be more challenging.
Alexander Knox at SemEval-2023 Task 5: The comparison of prompting and standard fine-tuning techniques for selecting the type of spoiler needed to neutralize a clickbait (2023)

Clickbait posts are a common problem on social media platforms, as they often deceive users by providing misleading or sensational headlines that do not match the content of the linked web page. The aim of this study is to create a technique for identifying the specific type of suitable spoiler - be it a phrase, a passage, or a multipart spoiler - needed to neutralize clickbait posts. This is achieved by developing a machine learning classifier analyzing both the clickbait post and the linked web page. Modern approaches for constructing a text classifier usually rely on fine-tuning a transformer-based model pre-trained on large unsupervised corpora. However, recent advances in the development of large-scale language models have led to the emergence of a new transfer learning paradigm based on prompt engineering. In this work, we study these two transfer learning techniques and compare their effectiveness for clickbait spoiler-type detection task. Our experimental results show that for this task, using the standard fine-tuning method gives better results than using prompting. The best model can achieve a similar performance to that presented by Hagen et al. (2022).
hhuEDOS at SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS) Binary Sexism Detection (Subtask A) (2023)

In this paper, we describe SemEval-2023 Task 10, a shared task on detecting and predicting sexist language. The dataset consists of labeled sexist and non-sexist data targeted towards women acquired from both Reddit and Gab. We present and compare several approaches we experimented with and our final submitted model. Additional error analysis is given to recognize challenges we dealt with in our process. A total of 84 teams participated. Our model ranks 55th overall in Subtask A of the shared task.
Rutgers Multimedia Image Processing Lab at SemEval-2023 Task-1: Text-Augmentation-based Approach for Visual Word Sense Disambiguation (2023)

This paper describes our system used in SemEval-2023 Task-1: Visual Word Sense Disambiguation (VWSD). The VWSD task is to identify the correct image that corresponds to an ambiguous target word given limited textual context. To reduce word ambiguity and enhance image selection, we proposed several text augmentation techniques, such as prompting, WordNet synonyms, and text generation. We experimented with different vision-language pre-trained models to capture the joint features of the augmented text and image. Our approach achieved the best performance using a combination of GPT-3 text generation and the CLIP model. On the multilingual test sets, our system achieved an average hit rate (at top-1) of 51.11 and a mean reciprocal rank of 65.69.
Uppsala University at SemEval-2023 Task12: Zero-shot Sentiment Classification for Nigerian Pidgin Tweets (2023)

While sentiment classification has been considered a practically solved task for high-resource languages such as English, the scarcity of data for many languages still makes it a challenging task. The AfriSenti-SemEval shared task aims to classify sentiment on Twitter data for 14 low-resource African languages. In our participation, we focus on Nigerian Pidgin as the target language. We have investigated the effect of English monolingual and multilingual pre-trained models on the sentiment classification task for Nigerian Pidgin. Our setup includes zero-shot models (using English, Igbo and Hausa data) and a Nigerian Pidgin fine-tuned model. Our results show that English fine-tuned models perform slightly better than models fine-tuned on other Nigerian languages, which could be explained by the lexical and structural closeness between Nigerian Pidgin and English. The best results were reported on the monolingual Nigerian Pidgin data. The model pre-trained on English and fine-tuned on Nigerian Pidgin was submitted to Task A Track 4 of the AfriSenti-SemEval Shared Task 12, and scored 25 out of 32 in the ranking.
KDDIE at SemEval-2023 Task 2: External Knowledge Injection for Named Entity Recognition (2023)

This paper introduces our system for the SemEval 2023 Task 2: Multilingual Complex Named Entity Recognition (MultiCoNER II) competition. Our team focused on the sub-task of Named Entity Recognition (NER) for the language of English in the challenge and reported our results. To achieve our goal, we utilized transfer learning by fine-tuning pre-trained language models (PLMs) on the competition dataset. Our approach involved combining a BERT-based PLM with external knowledge to provide additional context to the model. In this report, we present our findings and results.
Bhattacharya\_Lab at SemEval-2023 Task 12: A Transformer-based Language Model for Sentiment Classification for Low Resource African Languages: Nigerian Pidgin and Yoruba (2023)

Sentiment Analysis is an aspect of natural languageprocessing (NLP) that has been a topicof research. While most studies focus on highresourcelanguages with an extensive amountof available data, the study on low-resource languageswith insufficient data needs attention. To address this issue, we propose a transformerbasedmethod for sentiment analysis for lowresourcesAfrican languages, Nigerian Pidginand Yoruba. To evaluate the effectiveness ofour multilingual language models for monolingualsentiment classification, we participated inthe AfriSenti SemEval shared task 2023 competition. On the official e valuation s et, ourgroup (named as Bhattacharya{\_}Lab) ranked1 out of 33 participating groups in the MonolingualSentiment Classification task (i.e., TaskA) for Nigerian Pidgin (i.e., Track 4), and inthe Top 5 among 33 participating groups inthe Monolingual Sentiment Classification taskfor Yoruba (i.e., Track 2) respectively, demonstratingthe potential for our transformer-basedlanguage models to improve sentiment analysisin low-resource languages. Overall, ourstudy highlights the importance of exploringthe potential of NLP in low-resource languagesand the impact of transformer-based multilinguallanguage models in sentiment analysis forthe low-resource African languages, NigerianPidgin and Yoruba.
Seals\_Lab at SemEval-2023 Task 12: Sentiment Analysis for Low-resource African Languages, Hausa and Igbo (2023)

One of the most extensively researched applications in natural language processing (NLP) is sentiment analysis. While the majority of the study focuses on high-resource languages (e.g., English), this research will focus on low-resource African languages namely Igbo and Hausa. The annotated tweets of both languages have a significant number of code-mixed tweets. The curated datasets necessary to build complex AI applications are not available for the majority of African languages. To optimize the use of such datasets, research is needed to determine the viability of present NLP procedures as well as the development of novel techniques. This paper outlines our efforts to develop a sentiment analysis (for positive and negative as well as neutral) system for tweets from the Hausa, and Igbo languages. Sentiment analysis can computationally analyze and discover sentiments in a text or document. We worked on the first thorough compilation of AfriSenti-SemEval 2023 Shared Task 12 Twitter datasets that are human-annotated for the most widely spoken languages in Nigeria, such as Hausa and Igbo. Here we trained the modern pre-trained language model AfriBERTa large on the AfriSenti-SemEval Shared Task 12 Twitter dataset to create sentiment classification. In particular, the results demonstrate that our model trained on AfriSenti-SemEval Shared Task 12 datasets and produced with an F1 score of 80.85{\%} for Hausa and 80.82{\%} for Igbo languages on the sentiment analysis test. In AfriSenti-SemEval 2023 shared task 12 (Task A), we consistently ranked top 10 by achieving a mean F1 score of more than 80{\%} for both the Hausa and Igbo languages.