Papers with Farsi
Should Cross-Lingual AMR Parsing go Meta? An Empirical Assessment of Meta-Learning and Joint Learning AMR Parsing (2024.findings-emnlp)
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| Challenge: | Cross-lingual AMR parsing is a task of predicting AMR graphs in a target language when training data is available only in . et al. (2018) evaluated meta-learning for cross-lingual parse in Croatian, Farsi, Korean, Chinese, and French. |
| Approach: | They propose to use meta-learning to tackle cross-lingual AMR parsing in a target language . they evaluate their models in k-shot scenarios and compare them to classical joint learning . |
| Outcome: | The proposed model performs better in 0-shot evaluation for Croatian, Farsi, Korean, Chinese, and French. |
MultiMUC: Multilingual Template Filling on MUC-4 (2024.eacl-long)
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William Gantt, Shabnam Behzad, Hannah An, Yunmo Chen, Aaron White, Benjamin Van Durme, Mahsa Yarmohammadi
| Challenge: | We present multilingual parallel template filling datasets for MUCs . systems were required to extract one template per incident, containing details about perpetrators, victims, weapons used . |
| Approach: | They introduce MultiMUC, the first multilingual parallel corpus for template filling . they obtain automatic translations from a strong multilingual machine translation system . |
| Outcome: | The proposed dataset includes translations of the classic MUC-4 template filling benchmark into Arabic, Chinese, Farsi, Korean, and Russian. |
What about “em”? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns (2023.acl-long)
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| Challenge: | Wrong pronoun translations can discriminate against marginalized groups, e.g., non-binary individuals. |
| Approach: | They compare 3rd-person pronoun translations to five other languages . they propose to address gender exclusivity in future research . |
| Outcome: | The proposed method compares translations of gendered vs. gender-neutral pronouns from english to five other languages and vice versa. |
Constructing Multimodal Language Learner Texts Using LARA: Experiences with Nine Languages (2020.lrec-1)
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Elham Akhlaghi, Branislav Bédi, Fatih Bektaş, Harald Berthelsen, Matthias Butterweck, Cathy Chua, Catia Cucchiarin, Gülşen Eryiğit, Johanna Gerlach, Hanieh Habibi, Neasa Ní Chiaráin, Manny Rayner, Steinþór Steingrímsson, Helmer Strik
| Challenge: | LARA is an open source project that aims to support easy conversion of plain texts into online versions suitable for use by language learners. |
| Approach: | They propose to support easy conversion of plain texts into online versions suitable for use by language learners. |
| Outcome: | The proposed platform is suitable for creating texts in multiple languages via crowdsourcing techniques that can be used for teaching a language via reading and listening. |
Multilingual Coreference Resolution in Multiparty Dialogue (2023.tacl-1)
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| Challenge: | Existing datasets for entity coreference resolution are limited to English and other languages are rare. |
| Approach: | They propose to use TV transcripts to create multilingual multiparty coreference datasets that leverage existing subtitles in Chinese and Farsi. |
| Outcome: | The proposed dataset re-annotates for coreference on TV transcripts and then leverages existing subtitle translations to create a multilingual corpus. |
ParsTranslit: Truly Versatile Tajik-Farsi Transliteration (2026.findings-eacl)
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| Challenge: | Despite significant similarities between the two written standards, script differences hinder simple one-to-one mapping, hindering written communication and interaction between Tajikistan and its Persian-speaking “siblings”. |
| Approach: | They propose to use a sequence-to-sequence model to convert between two scripts in a Persian-speaking country using two datasets. |
| Outcome: | The proposed model achieves chrF++ and Normalized CER scores of 87.91 and 0.05 from Farsi to Tajik and 92.28 and 0.04 from Tajikistan to Farsis. |
The Multilingual Microblog Translation Corpus: Improving and Evaluating Translation of User-Generated Text (2022.lrec-1)
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| Challenge: | a corpus of over 200,000 microblog translations supports translation of thirteen languages into English . large collections of parallel text, or bitext, are increasingly available in many languages . |
| Approach: | They propose a corpus of over 200,000 microblog posts that supports translation of thirteen languages into English. |
| Outcome: | The proposed corpus contains over 200,000 translations of microblog posts in 13 languages . fine-tuning showed significant improvements in translation quality . |
Adaptive Knowledge Sharing in Multi-Task Learning: Improving Low-Resource Neural Machine Translation (P18-2)
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| Challenge: | Neural Machine Translation (NMT) requires large amounts of bilingual data to learn a translation model with reasonable quality. |
| Approach: | They propose to extend recurrent units with multiple "blocks" along with a trainable "routing network" this allows for adaptive collaboration by dynamic sharing of blocks conditioned on the task at hand, input, and model state. |
| Outcome: | Empirical evaluations of two low-resource translation tasks show +1 BLEU score improvements compared to strong baselines. |
Metaphors in Pre-Trained Language Models: Probing and Generalization Across Datasets and Languages (2022.acl-long)
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| Challenge: | Existing studies on pre-trained language models assume they encode metaphorical knowledge useful for NLP systems. |
| Approach: | They propose to probing metaphoricity information in PLMs and measure their generalization . they find that contextual representations in PMLs encode metaphorical knowledge . |
| Outcome: | The proposed model can encode metaphorical knowledge across languages and datasets . the model can be used to train and test NLP systems . |
Multi-class Multilingual Classification of Wikipedia Articles Using Extended Named Entity Tag Set (2020.lrec-1)
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| Challenge: | Existing classification models struggle with large datasets using fine-grained tag sets. |
| Approach: | They propose to structure Wikipedia into a large multi-lingual dataset using an Extended Named Entity tag set. |
| Outcome: | The proposed model fails to describe why Wikipedia articles are used to summarize, translate or answer questions. |
TruthTrap: A Bilingual Benchmark for Evaluating Factually Correct Yet Misleading Information in Question Answering (2026.findings-eacl)
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| Challenge: | Large Language Models (LLMs) are increasingly used to answer factual, information-seeking questions (ISQs). |
| Approach: | They propose to use a dataset to evaluate large language models to generate human-like text on ISQs in two languages, English and Farsi, and then use it to evaluate nine LLMs. |
| Outcome: | The proposed dataset shows that accuracy drops by 25% when models encounter misleading yet factual hints. |
Multilingual Amnesia: On the Transferability of Unlearning in Multilingual LLMs (2026.eacl-long)
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Alireza Dehghanpour Farashah, Aditi Khandelwal, Marylou Fauchard, Zhuan Shi, Negar Rostamzadeh, Golnoosh Farnadi
| Challenge: | Existing studies on unlearning in multilingual large language models focus on monolingual settings, typically English. |
| Approach: | They propose to use a multilingual data and concept unlearning model to investigate the problem . they extend benchmarks for factual knowledge and stereotypes into ten languages . |
| Outcome: | The proposed model is able to unlearning in 10 languages across five languages and resource levels. |
Can I Introduce My Boyfriend to My Grandmother? Evaluating Large Language Models Capabilities on Iranian Social Norm Classification (2025.findings-naacl)
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| Challenge: | Introducing the Iranian Social Norms dataset, a collection of 1,699 social norms, with Farsi adding linguistic complexity. |
| Approach: | They propose a collection of Iranian social norms with English translations and a novel Iranian dataset. |
| Outcome: | The Iranian Social Norms dataset is the first to be used in the Farsi language . it includes 1,699 social norms including environments, demographic features, and scope annotation, alongside English translations. |
Bayesian Learning for Neural Dependency Parsing (N19-1)
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| Challenge: | Several approaches for dependency parsing in the small data regime have been proposed. |
| Approach: | They propose to use stochastic gradient Langevin dynamics to generate samples from the approximated posterior to overcome the computational and statistical costs of the approximate inference step. |
| Outcome: | The proposed model outperforms the biaffine model on 6 languages with less than 5k training instances and improves across five languages. |
XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsing (2022.findings-emnlp)
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| Challenge: | Existing work focuses on English datasets, and it is unclear whether large language models can serve as competitive semantic parsers for other languages. |
| Approach: | They propose a framework that learns to retrieve relevant English exemplars for a given query to construct prompts. |
| Outcome: | The proposed framework learns to retrieve relevant English exemplars for a given query to construct prompts. |
Semi-Automated Construction of Sense-Annotated Datasets for Practically Any Language (2025.coling-main)
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Jai Riley, Bradley M. Hauer, Nafisa Sadaf Hriti, Guoqing Luo, Amir Reza Mirzaei, Ali Rafiei, Hadi Sheikhi, Mahvash Siavashpour, Mohammad Tavakoli, Ning Shi, Grzegorz Kondrak
| Challenge: | Word sense disambiguation is a widely studied NLP task of identifying the meaning of a word in context. |
| Approach: | They propose a method to create parallel sense-annotated datasets in English . they use machine translation, word alignment, sense projection, and sense filtering to produce silver annotations . |
| Outcome: | The proposed method produces parallel sense-annotated datasets on Farsi, Chinese, and Bengali . the results are higher than those obtained with recent multilingual systems, the authors say . |
MirasVoice: A bilingual (English-Persian) speech corpus (L18-1)
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| Challenge: | Existing research and development areas in speech recognition are focused on the language of speakers. |
| Approach: | They propose to use a bilingual (English-Farsi) speech corpus to validate and explore speaker verification systems. |
| Outcome: | The proposed corpus can be used in a variety of language dependent and independent applications. |
FaMTEB: Massive Text Embedding Benchmark in Persian Language (2025.findings-emnlp)
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Erfan Zinvandi, Morteza Alikhani, Mehran Sarmadi, Zahra Pourbahman, Sepehr Arvin, Reza Kazemi, Arash Amini
| Challenge: | a comprehensive benchmark for Persian text embeddings is built upon the Massive Text Embedding Benchmark (MTEB) 63 datasets are included in the benchmark, including a novel task of summary retrieval. |
| Approach: | They propose a benchmark for Persian (Farsi) text embeddings built upon the Massive Text Embedding Benchmark. |
| Outcome: | The proposed framework includes 63 datasets spanning seven different tasks . the evaluation datasets were rigorously evaluated by humans and automated systems . |
Pars-ABSA: a Manually Annotated Aspect-based Sentiment Analysis Benchmark on Farsi Product Reviews (2022.lrec-1)
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| Challenge: | Existing systems for sentiment analysis are focused on document and sentence levels, but there are no public datasets on aspect-based sentiment analysis for Farsi. |
| Approach: | They propose to use a manually annotated Farsi dataset to analyze the opinion polarity of reviews . they also use transfer learning to analyze aspects of the review to improve their results . |
| Outcome: | The proposed method performs better than other aspects of the existing system. |
Multilingual Simplification of Medical Texts (2023.emnlp-main)
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Sebastian Joseph, Kathryn Kazanas, Keziah Reina, Vishnesh Ramanathan, Wei Xu, Byron Wallace, Junyi Jessy Li
| Challenge: | Existing work on medical text simplification has focused on monolingual settings . important findings in medicine are typically presented in technical, jargon-laden language . text simulating models can generate viable simplified texts, but there are outstanding challenges . |
| Approach: | They propose a dataset for medical text simplification in four languages . they evaluate fine-tuned and zero-shot models across these languages based on human assessments and analyses . |
| Outcome: | The proposed dataset evaluates models in English, Spanish, French, and Farsi . it shows that the models can generate viable simplified texts, but there are challenges . |