Canada

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.

ACL 2023

NAP at SemEval-2023 Task 3: Is Less Really More? (Back-)Translation as Data Augmentation Strategies for Detecting Persuasion Techniques

setfit

adapter

back-translation

data augmentation

machine translation

transformer

The 9th Brazilian Conference on Intelligent Systems (BRACIS), the 16h Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), and the 12th Concurso de Teses e Dissertações em Inteligência Artificial e Computacional (CTDIAC) was held from 20 to 23 October, 2020.

BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence - SBIA (22 editions), and the Brazilian Symposium on Neural Networks - SBRN (13 editions). ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. CTDIAC is a satellite event whose goals are to disseminate and award the best dissertations and theses (MSc and PhDs concluded in Brazilian universities from June 2018 to May 2020) in Artificial and Computational Intelligence areas.

In this year, the Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) will be held in conjunction with BRACIS 2020. This sysposium aims at integrating researchers, practitioners, developers, students and users to present their research results, to discuss ideas, and to exchange techniques, tools, and practical experiences – related to the Data Mining and Machine Learning areas.

BRACIS 2020

workshop paper

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
entails a very long process. Moreover, the global economic conditions pose challenges for many individuals to travel to conferences. Recognizing these circumstances, we know many participants may not be
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you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience. 
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

##### **[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.

This work 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.

Rutgers Multimedia Image Processing Lab at SemEval-2023 Task-1: Text-Augmentation-based Approach for Visual Word Sense Disambiguation

The widespread popularity of social media has led to an increase in hateful, abusive, and sexist language, motivating methods for the automatic detection of such phenomena. The goal of the SemEval shared task Towards Explainable Detection of Online Sexism (EDOS 2023) is to detect sexism in English social media posts (subtask A), and to categorize such posts into four coarse-grained sexism categories (subtask B), and eleven fine-grained subcategories (subtask C). In this paper, we present our submitted systems for all three subtasks, based on a multi-task model that has been fine-tuned on a range of related tasks and datasets before being fine-tuned on the specific EDOS subtasks. We implement multi-task learning by formulating each task as binary pairwise text classification, where the dataset and label descriptions are given along with the input text. The results show clear improvements over a fine-tuned DeBERTa-V3 serving as a baseline leading to F1-scores of 85.9% in subtask A (rank 13/84), 64.8% in subtask B (rank 19/69), and 44.9% in subtask C (26/63).

CL-UZH at SemEval-2023 Task 10: Sexism Detection through Incremental Fine-Tuning and Multi-Task Learning with Label Descriptions

Clickbait creates a nuisance in the online experience by creating a lure towards poor content in order to generate ad revenue. With the use of natural language processing models, we can save users time and reduce the need to follow clickbait links. Task 5 at SemEval-2023 focused on precisely this problem and was broken into two steps: identifying the clickbait spoiler type and then identifying the clickbait itself. Our approach involves the use of fine-tuned text classification and question-answering models. Our classification model is able to determine the type of clickbait with 65.3% accuracy. The question-answering model exactly spoiled clickbait generated around 42.5% of the time. Efforts toward solving this task may have an impact by helping to save users' time and quickly give an insight into the answer of what the clickbait/article is about.

Mr-wallace at SemEval-2023 Task 5: Novel Clickbait Spoiling Algorithm Using Natural Language Processing

This paper presents the systems and approaches of the Gallagher team for the SemEval-2023 Task 5: Clickbait Spoiling. We propose a method to classify the type of spoiler (phrase, passage, multi) and a question-answering method to generate spoilers that satisfy the curiosity caused by clickbait posts. We experiment with the state-of-the-art Seq2Seq model T5. To identify the spoiler types we used a fine-tuned T5 classifier (Subtask 1). A mixture of T5 and Flan-T5 was used to generate the spoilers for clickbait posts (Subtask 2). Our system officially ranks first in generating phrase type spoilers in Subtask 2, and achieves the highest precision score for passage type spoilers in Subtask 1.

Gallagher at SemEval-2023 Task 5: Tackling Clickbait with Seq2Seq Models

This paper presents the systems and approaches of the Arizonans team for SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis. We finetune the Multilingual RoBERTa model trained with about 200M tweets, XLM-T. Our final model ranked 9th out of 45 overall, 13th in seen languages, and 8th in unseen languages.

Arizonans at SemEval-2023 Task 9: Multilingual Tweet Intimacy Analysis with XLM-T

There are two competing approaches for modelling annotator disagreement: distributional soft-labelling approaches (which aim to capture the level of disagreement) or modelling perspectives of individual annotators or groups thereof. We adapt a multi-task architecture -- which has previously shown success in modelling perspectives -- to evaluate its performance on the SEMEVAL Task 11. We do so by combining both approaches, i.e. predicting individual annotator perspectives as an interim step towards predicting annotator disagreement. Despite its previous success, we found that a multi-task approach performed poorly on datasets which contained distinct annotator opinions, suggesting that this approach may not always be suitable when modelling perspectives. Furthermore, our results explain that while strongly perspectivist approaches might not achieve state-of-the-art performance according to evaluation metrics used by distributional approaches, our approach allows for a more nuanced understanding of individual perspectives present in the data. We argue that perspectivist approaches are preferable because they enable decision makers to amplify minority views, and that it is important to re-evaluate metrics to reflect this goal.

iLab at SemEval-2023 Task 11 Le-Wi-Di: Modelling Disagreement or Modelling Perspectives?

The NLI4CT task aims to entail hypotheses based on Clinical Trial Reports (CTRs) and retrieve the corresponding evidence supporting the justification. This task poses a significant challenge, as verifying hypotheses in the NLI4CT task requires the integration of multiple pieces of evidence from one or two CTR(s) and the application of diverse levels of reasoning, including textual and numerical. To address these problems, we present a multi-granularity system for CTR-based textual entailment and evidence retrieval in this paper. Specifically, we construct a Multi-granularity Inference Network (MGNet) that exploits sentence-level and token-level encoding to handle both textual entailment and evidence retrieval tasks. Moreover, we enhance the numerical inference capability of the system by leveraging a T5-based model, SciFive, which is pre-trained on the medical corpus. Model ensembling and a joint inference method are further utilized in the system to increase the stability and consistency of inference. The system achieves f1-scores of 0.856 and 0.853 on textual entailment and evidence retrieval tasks, resulting in the best performance on both subtasks. The experimental results corroborate the effectiveness of our proposed method.

THiFLY Research at SemEval-2023 Task 7: A Multi-granularity System for CTR-based Textual Entailment and Evidence Retrieval

This paper explains the participation of team Hitachi to SemEval-2023 Task 3 "Detecting the genre, the framing, and the persuasion techniques in online news in a multi-lingual setup." Based on the multilingual, multi-task nature of the task and the low-resource setting, we investigated different cross-lingual and multi-task strategies for training the pretrained language models. Through extensive experiments, we found that (a) cross-lingual/multi-task training, and (b) collecting an external balanced dataset, can benefit the genre and framing detection. We constructed ensemble models from the results and achieved the highest macro-averaged F1 scores in Italian and Russian genre categorization subtasks.

Hitachi at SemEval-2023 Task 3: Exploring Cross-lingual Multi-task Strategies for Genre and Framing Detection in Online News

This paper describes our participation in SemEval-2023 Task 4, ValueEval: Identification of Human Values behind Arguments. The aim of this task is to identify whether or not an input text supports each of the 20 pre-defined human values. Previous work on human value detection has shown the effectiveness of a sequence classification approach using BERT. However, little is known about what type of task formulation is suitable for the task. To this end, this paper explores various task formulations, including sequence classification, question answering, and question
answering with chain-of-thought prompting and evaluates their performances on the shared task dataset. Experiments show that a zero-shot approach is not as effective as other methods, and there is no one approach that is optimal in every scenario. Our analysis also reveals that utilizing the descriptions of human values can help to improve performance.

Hitachi at SemEval-2023 Task 4: Exploring Various Task Formulations Reveals the Importance of Description Texts on Human Values

This presentation describes the system that we used to participate in SemEval 2023 task 10. Our system is an ensemble of different variants of fine-tuned DeBERTa models that employs a $k$-fold cross-validation. We have participated in the three tasks A, B and C. Our model ranked $2^{nd}$ position in tasks A, $7^{th}$ in task B and $4^{th}$ in task C

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Rutgers Multimedia Image Processing Lab at SemEval-2023 Task-1: Text-Augmentation-based Approach for Visual Word Sense Disambiguation

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