Sauleh Eetemadi


2023

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IUST at ImageArg: The First Shared Task in Multimodal Argument Mining
Melika Nobakhtian | Ghazal Zamaninejad | Erfan Moosavi Monazzah | Sauleh Eetemadi
Proceedings of the 10th Workshop on Argument Mining

ImageArg is a shared task at the 10th ArgMining Workshop at EMNLP 2023. It leverages the ImageArg dataset to advance multimodal persuasiveness techniques. This challenge comprises two distinct subtasks: 1) Argumentative Stance (AS) Classification: Assessing whether a given tweet adopts an argumentative stance. 2) Image Persuasiveness (IP) Classification: Determining if the tweet image enhances the persuasive quality of the tweet. We conducted various experiments on both subtasks and ranked sixth out of the nine participating teams.

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PMCoders at SemEval-2023 Task 1: RAltCLIP: Use Relative AltCLIP Features to Rank
Mohammad Javad Pirhadi | Motahhare Mirzaei | Mohammad Reza Mohammadi | Sauleh Eetemadi
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

Visual Word Sense Disambiguation (VWSD) task aims to find the most related image among 10 images to an ambiguous word in some limited textual context. In this work, we use AltCLIP features and a 3-layer standard transformer encoder to compare the cosine similarity between the given phrase and different images. Also, we improve our model’s generalization by using a subset of LAION-5B. The best official baseline achieves 37.20% and 54.39% macro-averaged hit rate and MRR (Mean Reciprocal Rank) respectively. Our best configuration reaches 39.61% and 56.78% macro-averaged hit rate and MRR respectively. The code will be made publicly available on GitHub.

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ROZAM at SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis
Mohammadmostafa Rostamkhani | Ghazal Zamaninejad | Sauleh Eetemadi
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

We build a model using large multilingual pretrained language model XLM-T for regression task and fine-tune it on the MINT (Multilingual INTmacy) analysis dataset which covers 6 languages for training and 4 languages for testing zero-shot performance of the model. The dataset was annotated and the annotations are intimacy scores. We experiment with several deep learning architectures to predict intimacy score. To achieve optimal performance we modify several model settings including loss function, number and type of layers. In total, we ran 16 end-to-end experiments. Our best system achieved a Pearson Correlation score of 0.52.

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Prodicus at SemEval-2023 Task 4: Enhancing Human Value Detection with Data Augmentation and Fine-Tuned Language Models
Erfan Moosavi Monazzah | Sauleh Eetemadi
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

This paper introduces a data augmentation technique for the task of detecting human values. Our approach involves generating additional examples using metadata that describes the labels in the datasets. We evaluated the effectiveness of our method by fine-tuning BERT and RoBERTa models on our augmented dataset and comparing their F1 -scores to those of the non-augmented dataset. We obtained competitive results on both the Main test set and the Nahj al-Balagha test set, ranking 14th and 7th respectively among the participants. We also demonstrate that by incorporating our augmentation technique, the classification performance of BERT and RoBERTa is improved, resulting in an increase of up to 10.1% in their F1-score.

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GYM at Qur’an QA 2023 Shared Task: Multi-Task Transfer Learning for Quranic Passage Retrieval and Question Answering with Large Language Models
Ghazaleh Mahmoudi | Yeganeh Morshedzadeh | Sauleh Eetemadi
Proceedings of ArabicNLP 2023

This work addresses the challenges of question answering for vintage texts like the Quran. It introduces two tasks: passage retrieval and reading comprehension. For passage retrieval, it employs unsupervised fine-tuning sentence encoders and supervised multi-task learning. In reading comprehension, it fine-tunes an Electra-based model, demonstrating significant improvements over baseline models. Our best AraElectra model achieves 46.1% partial Average Precision (pAP) on the unseen test set, outperforming the baseline by 23%.

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A longitudinal study about gradual changes in the Iranian Online Public Sphere pre and post of ‘Mahsa Moment’: Focusing on Twitter
Sadegh Jafari | Amin Fathi | Abolfazl Hajizadegan | Amirmohammad Kazemeini | Sauleh Eetemadi
Proceedings of the 4th Workshop on Computational Approaches to Historical Language Change

Mahsa Amini’s death shocked Iranian society. The effects of this event and the subsequent tragedies in Iran not only in realspace but also in cyberspace, including Twitter, were tremendous and unimaginable. We explore how Twitter has changed after Mahsa Amini’s death by analyzing the sentiments of Iranian users in the 90 days after this event. Additionally, we track the change in word meaning and each word’s neighboring words. Finally, we use word clustering methods for topic modeling.

2022

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Using Two Losses and Two Datasets Simultaneously to Improve TempoWiC Accuracy
Mohammad Javad Pirhadi | Motahhare Mirzaei | Sauleh Eetemadi
Proceedings of the The First Workshop on Ever Evolving NLP (EvoNLP)

WSD (Word Sense Disambiguation) is the task of identifying which sense of a word is meant in a sentence or other segment of text. Researchers have worked on this task (e.g. Pustejovsky, 2002) for years but it’s still a challenging one even for SOTA (state-of-the-art) LMs (language models). The new dataset, TempoWiC introduced by Loureiro et al. (2022b) focuses on the fact that words change over time. Their best baseline achieves 70.33% macro-F1. In this work, we use two different losses simultaneously. We also improve our model by using another similar dataset to generalize better. Our best configuration beats their best baseline by 4.23%.

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Pars-ABSA: a Manually Annotated Aspect-based Sentiment Analysis Benchmark on Farsi Product Reviews
Taha Shangipour ataei | Kamyar Darvishi | Soroush Javdan | Behrouz Minaei-Bidgoli | Sauleh Eetemadi
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Due to the increased availability of online reviews, sentiment analysis witnessed a thriving interest from researchers. Sentiment analysis is a computational treatment of sentiment used to extract and understand the opinions of authors. While many systems were built to predict the sentiment of a document or a sentence, many others provide the necessary detail on various aspects of the entity (i.e., aspect-based sentiment analysis). Most of the available data resources were tailored to English and the other popular European languages. Although Farsi is a language with more than 110 million speakers, to the best of our knowledge, there is a lack of proper public datasets on aspect-based sentiment analysis for Farsi. This paper provides a manually annotated Farsi dataset, Pars-ABSA, annotated and verified by three native Farsi speakers. The dataset consists of 5,114 positive, 3,061 negative and 1,827 neutral data samples from 5,602 unique reviews. Moreover, as a baseline, this paper reports the performance of some aspect-based sentiment analysis methods focusing on transfer learning on Pars-ABSA.

2021

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ParsFEVER: a Dataset for Farsi Fact Extraction and Verification
Majid Zarharan | Mahsa Ghaderan | Amin Pourdabiri | Zahra Sayedi | Behrouz Minaei-Bidgoli | Sauleh Eetemadi | Mohammad Taher Pilehvar
Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics

Training and evaluation of automatic fact extraction and verification techniques require large amounts of annotated data which might not be available for low-resource languages. This paper presents ParsFEVER: the first publicly available Farsi dataset for fact extraction and verification. We adopt the construction procedure of the standard English dataset for the task, i.e., FEVER, and improve it for the case of low-resource languages. Specifically, claims are extracted from sentences that are carefully selected to be more informative. The dataset comprises nearly 23K manually-annotated claims. Over 65% of the claims in ParsFEVER are many-hop (require evidence from multiple sources), making the dataset a challenging benchmark (only 13% of the claims in FEVER are many-hop). Also, despite having a smaller training set (around one-ninth of that in Fever), a model trained on ParsFEVER attains similar downstream performance, indicating the quality of the dataset. We release the dataset and the annotation guidelines at https://github.com/Zarharan/ParsFEVER.

2015

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Detecting Translation Direction: A Cross-Domain Study
Sauleh Eetemadi | Kristina Toutanova
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop

2014

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Asymmetric Features Of Human Generated Translation
Sauleh Eetemadi | Kristina Toutanova
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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Dramatically Reducing Training Data Size Through Vocabulary Saturation
William Lewis | Sauleh Eetemadi
Proceedings of the Eighth Workshop on Statistical Machine Translation