Canada

We introduce our work submitted to SemEval-2023 Task 12: &quot;Sentiment Analysis for Low-resource African Languages using Twitter dataset&quot;. In our system, we leverage adaptive pretraining on African texts and study transfer learning with source language selection. Our system wins 2 of 3 subtasks and performs best in 7 out of 14 languages in the shared task.

ACL 2023

NLNDE at SemEval-2023 Task 12: Adaptive Pretraining and Source Language Selection for Low-Resource Multilingual Sentiment Analysis

multilingual transfer learning

african languages sentiment analysis

low-resource nlp

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
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##### **[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.

We introduce our work submitted to SemEval-2023 Task 12: "Sentiment Analysis for Low-resource African Languages using Twitter dataset". In our system, we leverage adaptive pretraining on African texts and study transfer learning with source language selection. Our system wins 2 of 3 subtasks and performs best in 7 out of 14 languages in the shared task.

workshop paper

This paper presents the best-performing approach alias "Adam Smith" for the SemEval-2023 Task 4: "Identification of Human Values behind Arguments". The goal of the task was to create systems that automatically identify the values within textual arguments. We train transformer-based models until they reach their loss minimum or f1-score maximum. Ensembling the models by selecting one global decision threshold that maximizes the f1-score leads to the best-performing system in the competition. Ensembling based on stacking with logistic regressions shows the best performance on an additional dataset provided to evaluate the robustness ("Nahj al-Balagha"). Apart from outlining the submitted system, we demonstrate that the use of the large ensemble model is not necessary and that the system size can be significantly reduced.

Adam-Smith at SemEval-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Models

Modeling the most likely label when an annotation task is perspective-dependent discards relevant sources of variation that come from the annotators themselves. 
We present three approaches to modeling the controversiality of a particular text. First, we explicitly represented annotators using annotator embeddings to predict the training signals of each annotator's selections in addition to a majority class label. This method leads to reduction in error relative to models without these features, allowing the overall result to influence the weights of each annotator on the final prediction. In a second set of experiments, annotators were not modeled individually but instead annotator judgments were combined in a pairwise fashion that allowed us to implicitly combine annotators. Overall, we found that aggregating and explicitly comparing annotators' responses to a static document representation produced high-quality predictions in all datasets, though some systems struggle to account for large or variable numbers of annotators.

University at Buffalo at SemEval-2023 Task 11: MASDA--Modelling Annotator Sensibilities through DisAggregation

The goal of the NLI4CT task is to build a Natural Language Inference system for Clinical Trial Reports that will be used for evidence interpretation and retrieval. Large Language models have demonstrated state-of-the-art performance in various natural language processing tasks across multiple domains. We suggest using an instruction-finetuned Large Language Models (LLMs) to take on this particular task in light of these developments. We have evaluated the publicly available LLMs under zeroshot setting, and finetuned the best performing Flan-T5 model for this task. On the leaderboard, our system ranked second, with an F1 Score of 0.834 on the official test set.

Saama AI Research at SemEval-2023 Task 7: Exploring the Capabilities of Flan-T5 for Multi-evidence Natural Language Inference in Clinical Trial Data

This presentation describes our system submitted to SemEval-2023 Task 5: Clickbait Spoiling. We work on spoiler generation of the subtask 2 and develop a system which comprises two parts: 1) simple seq2seq spoiler generation and 2) post-hoc model ensembling. Using this simple method, we address the challenge of generating multipart spoiler. In the test set, our submitted system1 outperformed the baseline by a large margin (approximately 10 points above on the BLEU score) for mixed types of spoilers. We also found that our system successfully handled the challenge of the multipart spoiler, confirm- ing the effectiveness of our approach.

TohokuNLP at SemEval-2023 Task 5: Clickbait Spoiling via Simple Seq2Seq Generation and Ensembling

Online sexism is a widespread and harmful phenomenon. Automated tools can assist the detection of sexism at scale. Binary detection, however, disregards the diversity of sexist content, and fails to provide clear explanations for why something is sexist. To address this issue, we introduce SemEval Task 10 on the Explainable Detection of Online Sexism (EDOS). We make three main contributions: i) a novel hierarchical taxonomy of sexist content, which includes granular vectors of sexism to aid explainability; ii) a new dataset of 20,000 social media comments with fine-grained labels, along with larger unlabelled datasets for model adaptation; and iii) baseline models as well as an analysis of the methods, results and errors for participant submissions to our task.

SemEval-2023 Task 10: Explainable Detection of Online Sexism

This paper describes the results of SemEval 2023 task 7 – Multi-Evidence Natu-
ral Language Inference for Clinical Trial Data (NLI4CT) – consisting of 2 tasks, a Natural Language Inference (NLI) task, and an evidence selection task on clinical trial data. The proposed challenges require multi-hop biomedical and numerical reasoning, which are of significant importance to the development of systems capable of large-scale interpretation and retrieval of medical evidence, to provide personalized evidence-based care. Task 1, the entailment task, received 643 sub-
missions from 40 participants, and Task 2, the evidence selection task, received 364 submissions from 23 participants. The tasks are challenging, with the majority of submitted systems failing to significantly outperform the majority class baseline on the entailment task, and we observe significantly better performance on the
evidence selection task than on the entailment task. Increasing the number of model parameters leads to a direct increase in performance, far more significant than the effect of biomedical pre-training. Future works could explore the limitations of large models for generalization and numerical inference, and investigate methods to augment clinical datasets to allow for more rigorous testing and to facilitate fine-
tuning. We envisage that the dataset, models, and results of this task will be useful to the biomedical NLI and evidence retrieval communities.

SemEval-2023 Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data

This paper presents the Visual Word Sense Disambiguation (Visual-WSD) task. The objective of Visual-WSD is to identify among a set of ten images the one that corresponds to the intended meaning of a given ambiguous word which is accompanied with minimal context. The task provides datasets for three different languages: English, Italian, and Farsi. We received a total of 96 different submissions. Out of these, 40 systems outperformed a strong zero-shot CLIP based baseline (Radford et al., 2021). Participating systems proposed different zero- and few-shot approaches, often involving generative models and data augmentation. More information can be found on the task’s website: https://raganato.github.io/vwsd/.

SemEval-2023 Task 1: Visual Word Sense Disambiguation

Intimacy is an important social aspect of language. Computational modeling of intimacy in language could help many downstream applications like dialogue systems and offensiveness detection. Despite its importance, resources, and approaches on modeling textual intimacy remain rare. To address this gap, we introduce MINT, a new Multilingual intimacy analysis dataset covering 13,372 tweets in 10 languages including English, French, Spanish, Italian, Portuguese, Korean, Dutch, Chinese, Hindi, and Arabic along with SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis. Our task attracted 45 participants from around the world. While the participants are able to achieve overall good performance on languages in the training set, zero-shot prediction of intimacy in unseen languages remains challenging. Here we provide an overview of the task, summaries of the common approaches, and potential future directions on modeling intimacy across languages. All the relevant resources are available at https: //sites.google.com/umich.edu/ semeval-2023-tweet-intimacy.

SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis

We present the findings of SemEval-2023 Task 2 on Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2). Divided into 13 tracks, the task focused on methods to identify complex fine-grained named entities (like WRITTENWORK, VEHICLE, MUSICALGRP) across 12 languages, in both monolingual and multilingual scenarios, as well as noisy settings. The task used the MultiCoNER V2 dataset, composed of 2.2 million instances in Bangla, Chinese, English, Farsi, French, German, Hindi, Italian., Portuguese, Spanish, Swedish, and Ukrainian. MultiCoNER 2 was one of the most popular tasks of SemEval-2023. It attracted 842 submissions from 47 teams, and 34 teams submitted system papers. Results showed that complex entity types such as media titles and product names were the most challenging. Methods fusing external knowledge into transformer models achieved the best performance, and the largest gains were on the Creative Work and Group classes, which are still challenging even with external knowledge. Some fine-grained classes proved to be more challenging than others, such as SCIENTIST, ARTWORK, and PRIVATECORP. We also observed that noisy data has a significant impact on model performance, with an average drop of 10% on the noisy subset. The task highlights the need for future research on improving NER robustness on noisy data containing complex entities.

SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)

Identification of medical claims from user-generated text data is an onerous but essential step for various tasks including content moderation, and hypothesis generation. SemEval-2023 Task 8 is an effort toward building those capabilities and motivating further research in this direction. This paper summarizes the details and results of shared task 8 at SemEval-2023 which involved identifying causal medical claims and extracting related Populations, Interventions, and Outcomes (``PIO'') frames from social media (Reddit) text.

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