Canada

We describe the methods we used for legal text understanding, specifically Task 6 Legal-Eval at SemEval 2023. The outcomes could assist law practitioners and help automate the working process of judicial systems. The shared task defined three main sub-tasks: sub-task A, Rhetorical Roles Prediction (RR); sub-task B, Legal Named Entities Extraction (L-NER); and sub-task C, Court Judgement Prediction with Explanation (CJPE). Our team addressed all three sub-tasks by exploring various Deep Learning (DL) based models. Overall, our team&#39;s approaches achieved promising results on all three sub-tasks, demonstrating the potential of deep learning-based models in the judicial domain.

ACL 2023

uOttawa at SemEval-2023 Task 6: Deep Learning for Legal Text Understanding

legal text mining

explainability

deep learning

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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.

We describe the methods we used for legal text understanding, specifically Task 6 Legal-Eval at SemEval 2023. The outcomes could assist law practitioners and help automate the working process of judicial systems. The shared task defined three main sub-tasks: sub-task A, Rhetorical Roles Prediction (RR); sub-task B, Legal Named Entities Extraction (L-NER); and sub-task C, Court Judgement Prediction with Explanation (CJPE). Our team addressed all three sub-tasks by exploring various Deep Learning (DL) based models. Overall, our team's approaches achieved promising results on all three sub-tasks, demonstrating the potential of deep learning-based models in the judicial domain.

workshop paper

We describe our participation in the Clickbait challenge at SemEval 2023. In this work, we address the Clickbait classification task using transformers models in an ensemble configuration. We tackle the Spoiler Generation task using a two-level ensemble strategy of models trained for extractive QA, and selecting the best K candidates for multi-part spoilers. In the test partitions, our approaches obtained a classification accuracy of 0.716 for classification and a BLEU-4 score of 0.439 for spoiler generation.

Walter Burns at SemEval-2023 Task 5: NLP-CIMAT - Leveraging Model Ensembles for Clickbait Spoiling

This poster describes the participation of SINAI research team in the Explainable Detection of Online Sexism (EDOS) Shared Task at SemEval 2023. Specifically, we participate in subtask A (binary sexism detection), subtask B (category of sexism), and subtask C (fine-grained vector of sexism). For the three subtasks, we propose a system that integrates information related to emotions, sentiments, and irony in order to check whether these features help detect sexism content. Our team ranked 46th in subtask A, 37th in subtask B, and 29th in subtask C, achieving 0.8245, 0.6043, and 0.4376 of macro f1-score, respectively, among the participants.


SINAI at SemEval-2023 Task 10: Leveraging Emotions, Sentiments, and Irony Knowledge for Explainable Detection of Online Sexism

This presentation describes the submission of UZH\_CLyp for the SemEval 2023 Task 9 ``Multilingual Tweet Intimacy Analysis''. We achieved second-best results in all 10 languages according to the official Pearson's correlation regression evaluation measure. Our cross-lingual transfer learning approach explores the benefits of using a Head-First Fine-Tuning method (HeFiT) that first updates only the regression head parameters and then also updates the pre-trained transformer encoder parameters at a reduced learning rate. Additionally, we study the impact of using a small set of automatically generated examples (in our case, from ChatGPT) for low-resource settings where no human-labeled data is available. Our study shows that HeFiT stabilizes training and consistently improves results for pre-trained models that lack domain adaptation to tweets. Our study also shows a noticeable performance increase in cross-lingual learning when synthetic data is used, confirming the usefulness of current text generation systems to improve zero-shot baseline results. Finally, we examine how possible inconsistencies in the annotated data contribute to cross-lingual interference issues.

UZH_CLyp at SemEval-2023 Task 9: Head-First Fine-Tuning and ChatGPT Data Generation for Cross-Lingual Learning in Tweet Intimacy Prediction

Human Value Detection in Arguments is a complex task requiring a high-level understanding of language due to the hidden nature of opinions and beliefs in arguments. In this presentation, the Augustine of Hippo team shows its solution to SemEval2023 Task 4: the proposed methodology leverages an unsurpervised explainable knowledge extraction system capable of identifying concepts to associate with human values.

Augustine of Hippo at SemEval-2023 Task 4: An Explainable Knowledge Extraction Method to Identify Human Values in Arguments with SuperASKE

We present our submission to SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS). We address all three tasks: Task A consists of identifying whether a post is sexist. If so, Task B attempts to assign it one of four classes: threats, derogation, animosity, and prejudiced discussions. Task C aims for an even more fine-grained classification, divided among 11 classes. We experiment with fine-tuning of hate-tuned Transformer-based models and priming for generative models. In addition, we explore model-agnostic strategies, such as data augmentation techniques combined with active learning, as well as obfuscation of identity terms. Our official submissions obtain an F1 score of 0.83 for Task A, 0.58 for Task B and 0.32 for Task C.

UniBoe's at SemEval-2023 Task 10: Model-Agnostic Strategies for the Improvement of Hate-Tuned and Generative Models in the Classification of Sexist Posts

The MultiCoNER II shared task aims at de- tecting complex, ambiguous named entities with fine-grained types in a low context setting. Previous winning systems incorporated external knowledge bases to retrieve helpful contexts. In our submission we additionally propose splitting the NER task into two stages, a Span Extraction Step, and an Entity Classification step. Our results show that the former does not suffer from the low context setting comparably, and in so leading to a higher overall performance for an external KB-assisted system. We achieve 3rd place on the multilin- gual track and an average of 6th place overall.

NLPeople at SemEval-2023 Task 2: A Staged Approach for Multilingual Named Entity Recognition

Using pre-trained language models to implement classifiers from small to modest amounts of training data is an area of active research. The ability of large language models to generalize from few-shot examples and to produce strong classifiers is extended using the engineering approach of parameter-efficient tuning. Using the Explainable Detection of Online Sexism (EDOS) training data and a small number of trainable weights to create a tuned prompt vector, a competitive model for this task was built, which was top-ranked in Subtask B.

JUAGE at SemEval 2023 Task 10: Parameter Efficient Classification

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.

IXA/Cogcomp at SemEval-2023 Task 2: Context-enriched Multilingual Named Entity Recognition Using Knowledge Bases

This paper describes the system and experimental results of an ensemble-based approach to multilingual 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.

Team ACCEPT at SemEval-2023 Task 3: An Ensemble-based Approach to Multilingual Framing Detection

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. As a result, we present the new balanced dataset for sexism detection and the best model fine-tuned on it.

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Walter Burns at SemEval-2023 Task 5: NLP-CIMAT - Leveraging Model Ensembles for Clickbait Spoiling

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SemEval 2023 Task 6: LegalEval - Understanding Legal Texts