Canada

In this paper, we proposed and explored the impact of four different dataset augmentation and extension strategies that we used for solving the subtask 3 of SemEval-2023 Task 3: multi-label persuasion techniques classification in a multi-lingual context. 
We consider two types of augmentation methods (one based on a modified version of synonym replacement and one based on translations) and two ways of extending the training dataset (using filtered data generated by GPT-3 and using a dataset from a previous competition).
We studied the effects of the aforementioned techniques by using the augmented and/or extended training dataset
to fine-tune a pretrained XLM-RoBERTa-Large model. Using the augmentation methods alone, we managed to obtain 3rd place for English, 13th place for Italian and between the 5th to 9th places for the other 7 languages during the competition.

ACL 2023

Appeal for Attention at SemEval-2023 Task 3: Data Augmentation and Extension Strategies for Detection of Online News Persuasion Techniques

data extension

data augmentation

transformers

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
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workshop paper

We present the best-performing solution to the SemEval 2023 Task 3 on the subtask 3 dedicated to persuasion techniques detection. Due to a high multilingual character of the input data and a large number of 23 predicted labels (causing a lack of labelled data for some language-label combinations), we opted for fine-tuning pre-trained transformer-based language models. Conducting multiple experiments, we find the best configuration, which consists of large multilingual model (XLM-RoBERTa large) trained jointly on all input data, with carefully calibrated confidence thresholds for seen and surprise languages separately. Our final system performed the best on 6 out of 9 languages (including two surprise languages) and achieved highly competitive results on the remaining three languages.

KInITVeraAI at SemEval-2023 Task 3: Simple Yet Powerful Multilingual Fine-Tuning for Persuasion Techniques Detection

This paper describes the system developed by the USTC-NELSLIP team for SemEval-2023 Task 2 Multilingual Complex Named Entity Recognition (MultiCoNER II). A method named Statistical Construction and Dual Adaptation of Gazetteer (SCDAG) is proposed for Multilingual Complex NER. The method first utilizes a statistics-based approach to construct a gazetteer. Secondly, the representations of gazetteer networks and language models are adapted by minimizing the KL divergence between them at both the sentence-level and entity-level. Finally, these two networks are then integrated for supervised named entity recognition (NER) training. The proposed method is applied to XLM-R with a gazetteer built from Wikidata, and shows great generalization ability across different tracks. Experimental results and detailed analysis verify the effectiveness of the proposed method. The official results show that our system ranked 1st on one track (Hindi) in this task.

USTC-NELSLIP at SemEval-2023 Task 2: Statistical Construction and Dual Adaptation of Gazetteer for Multilingual Complex NER

The MultiCoNER II shared task aims at detecting semantically ambiguous and complex named entities in short and low-context settings for multiple languages. The lack of context makes the recognition of ambiguous named entities challenging. To alleviate this issue, our team SRCB proposes an external knowledge based system, where we utilize 3 different types of external knowledge retrieved in different ways. Given an original text, our system retrieves the possible labels and the descriptions for each potential entity detected by a mention detection model. And we also retrieve a related document as extra context from Wikipedia for each original text. We concatenate the original text with the external knowledge as the input of NER models. The informative contextual representations with external knowledge significantly improve the NER performance in both Chinese and English tracks.

SRCB at SemEval-2023 Task 2: A System of Complex Named Entity Recognition with External Knowledge

PAI at SemEval-2023 Task 2: A Universal System for Named Entity
Recognition with External Entity Information

PAI at SemEval-2023 Task 2: A Universal System for Named Entity Recognition with External Entity Information

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.

L3I++ at SemEval-2023 Task 2: Prompting for Multilingual Complex Named Entity Recognition

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.

mCPT at SemEval-2023 Task 3: Multilingual Label-Aware Contrastive Pre-Training of Transformers for Few- and Zero-shot Framing Detection

Our contribution to the 2023 AfriSenti-SemEval shared task 12: Sentiment Analysis for African Languages, provides insight into how a multilingual large language model can be a resource for sentiment analysis in languages not seen during pretraining. The shared task provides datasets of a variety of African languages from different language families. The languages are to various degrees related to languages used during pretraining, and the language data contain various degrees of code-switching. We experiment with both monolingual and multilingual datasets for the final fine-tuning, and find that with the provided datasets that contain samples in the thousands, monolingual fine-tuning yields the best results.

UIO at SemEval-2023 Task 12: Multilingual Fine-tuning for Sentiment Classification in Low-resource Languages

In this paper, we describe our approach to the clickbait spoiling task of SemEval 2023. The core idea behind our system is to leverage pre-trained models capable of Question Answering (QA) to extract the spoiler from article texts based on the clickbait title without any task-specific training. Since oftentimes, these titles are not phrased as questions, we automatically rephrase the clickbait titles as questions in order to better suit the pretraining task of the QA-capable models. Also, to fit as much relevant context into the model's limited input size as possible, we propose to reorder the sentences by their relevance using a semantic similarity model. Finally, we evaluate QA as well as text generation models (via prompting) to extract the spoiler from the text. Based on the validation data, our final model selects each of these components depending on the spoiler type and achieves satisfactory zero-shot results. The ideas described in this paper can easily be applied in fine-tuning settings.

Jack-Ryder at SemEval-2023 Task 5: Zero-Shot Clickbait Spoiling by Rephrasing Titles as Questions

This report describes GMU’s sentiment analysis system for the SemEval-2023 shared task AfriSenti-SemEval. We participated in all three sub-tasks: Monolingual, Multilingual, and Zero-Shot. Our approach uses models initialized with AfroXLMR-large, a pre-trained multilingual language model trained on African languages and fine-tuned correspondingly. We also introduce augmented training data along with original training data. Alongside fine-tuning, we perform phylogeny-based adapter-tuning to create several models and ensemble the best models for the final submission. Our system achieves the best F1-score on track 5: Amharic, with 6.2 points higher F1-score than the second-best performing system on this track. Overall, our system ranks 5th among the 10 systems participating in all 15 tracks.

GMNLP at SemEval-2023 Task 12: Sentiment Analysis with Phylogeny-Based Adapters

Misinformation spreading in mainstream and social media has been misleading users in different ways. Manual detection and verification efforts by journalists and fact-checkers can no longer cope with the great scale and quick spread of misleading information. This motivated research and industry efforts to develop systems for analyzing and verifying news spreading online. The SemEval-2023 Task 3 is an attempt to address several subtasks under this overarching problem, targeting writing techniques used in news articles to affect readers’ opinions. The task addressed three subtasks with six languages, in addition to three “surprise” test languages, resulting in 27 different test setups. This paper describes our participating system to this task. Our team is one of the 6 teams that successfully submitted runs for all setups. The official results show that our system is ranked among the top 3 systems for 10 out of the 27 setups.

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KInITVeraAI at SemEval-2023 Task 3: Simple Yet Powerful Multilingual Fine-Tuning for Persuasion Techniques Detection

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