Amir Hossein Kargaran

Hello! I’m Amir 👋, a Ph.D. student at CIS, LMU Munich advised by Prof. Hinrich Schütze. My primary focus is on scaling NLP horizontally to include more languages in the map of NLP.

Before starting my Ph.D., I completed my M.Sc. in Computer Engineering at Sharif University of Technology, 2022. I did my B.Sc. at Isfahan University of Technology double majored in Electrical Engineering and Computer Engineering, 2020.

news

Feb 20, 2024	📌 GlotScript: A Resource and Tool for Low Resource Writing System Identification is accepted at the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC’24). Check out our repo here.
Jan 13, 2024	📌 GIRT-Model: Automated Generation of Issue Report Templates is accepted at 21st IEEE/ACM Conference on Mining Software Repositories (MSR’24).
Oct 24, 2023	📌 GlotLID: Language Identification for Low-Resource Languages is accepted at the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP’23). Check out our demo here.

see all the news here.

selected publications

EMNLP 2023

GlotLID: Language Identification for Low-Resource Languages

Amir Hossein Kargaran, Ayyoob Imani, François Yvon, and Hinrich Schütze

In The 2023 Conference on Empirical Methods in Natural Language Processing 2023

Abs arXiv ACL PDF Code Slides Website

Several recent papers have published good solutions for language identification (LID) for about 300 high-resource and medium-resource languages. However, there is no LID available that (i) covers a wide range of low-resource languages, (ii) is rigorously evaluated and reliable and (iii) efficient and easy to use. Here, we publish GlotLID-M, an LID model that satisfies the desiderata of wide coverage, reliability and efficiency. It identifies 1665 languages, a large increase in coverage compared to prior work. In our experiments, GlotLID-M outperforms four baselines (CLD3, FT176, OpenLID and NLLB) when balancing F1 and false positive rate (FPR). We analyze the unique challenges that low-resource LID poses: incorrect corpus metadata, leakage from high-resource languages, difficulty separating closely related languages, handling of macrolanguage vs varieties and in general noisy data. We hope that integrating GlotLID-M into dataset creation pipelines will improve quality and enhance accessibility of NLP technology for low-resource languages and cultures. GlotLID-M model, code, and list of data sources are available: https://github.com/cisnlp/GlotLID.
LREC 2024

GlotScript: A Resource and Tool for Low Resource Writing System Identification

Amir Hossein Kargaran, François Yvon, and Hinrich Schütze

The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation 2024

Abs arXiv Code

We present GlotScript, an open resource and tool for low resource writing system identification. GlotScript-R is a resource that provides the attested writing systems for more than 7,000 languages. It is compiled by aggregating information from existing writing system resources. GlotScript-T is a writing system identification tool that covers all 161 Unicode 15.0 scripts. For an input text, it returns its script distribution where scripts are identified by ISO 15924 codes. We also present two use cases for GlotScript. First, we demonstrate that GlotScript supports cleaning multilingual corpora such as mC4 and OSCAR. Second, we analyze the tokenization of a number of language models such as GPT-4 using GlotScript and provide insights on the coverage of low resource scripts and languages by each language model. We hope that GlotScript will become a useful resource for work on low resource languages in the NLP community. GlotScript-R and GlotScript-T are available at https://github.com/cisnlp/GlotScript.
ACL 2023

Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages

Ayyoob ImaniGooghari, Peiqin Lin, Amir Hossein Kargaran, Silvia Severini, Masoud Jalili Sabet, Nora Kassner, Chunlan Ma, Helmut Schmid, André Martins, François Yvon, and Hinrich Schütze

In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) 2023

Abs Area Chair Award arXiv ACL PDF Code

The NLP community has mainly focused on scaling Large Language Models (LLMs) vertically, i.e., making them better for about 100 languages. We instead scale LLMs horizontally: we create, through continued pretraining, Glot500-m, an LLM that covers 511 predominantly low-resource languages. An important part of this effort is to collect and clean Glot500-c, a corpus that covers these 511 languages and allows us to train Glot500-m. We evaluate Glot500-m on five diverse tasks across these languages. We observe large improvements for both high-resource and low-resource languages compared to an XLM-R baseline. Our analysis shows that no single factor explains the quality of multilingual LLM representations. Rather, a combination of factors determines quality including corpus size, script, "help" from related languages and the total capacity of the model. Our work addresses an important goal of NLP research: we should not limit NLP to a small fraction of the world’s languages and instead strive to support as many languages as possible to bring the benefits of NLP technology to all languages and cultures. Code, data and models are available at https://github.com/cisnlp/Glot500.
WEBSCI 2021

Wide-AdGraph: Detecting Ad Trackers with a Wide Dependency Chain Graph

Amir Hossein Kargaran, Mohammad Sadegh Akhondzadeh, Mohammad Reza Heidarpour, Mohammad Hossein Manshaei, Kave Salamatian, and Masoud Nejad Sattary

In 13th ACM Web Science Conference 2021

Abs Best Student Paper arXiv ACM PDF_V2 PDF_V1 Video Code

Websites use third-party ads and tracking services to deliver targeted ads and collect information about users that visit them. These services put users’ privacy at risk, and that is why users’ demand for blocking these services is growing. Most of the blocking solutions rely on crowd-sourced filter lists manually maintained by a large community of users. In this work, we seek to simplify the update of these filter lists by combining different websites through a large-scale graph connecting all resource requests made over a large set of sites. The features of this graph are extracted and used to train a machine learning algorithm with the aim of detecting ads and tracking resources. As our approach combines different information sources, it is more robust toward evasion techniques that use obfuscation or changing the usage patterns. We evaluate our work over the Alexa top-10K websites and find its accuracy to be 96.1% biased and 90.9% unbiased with high precision and recall. It can also block new ads and tracking services, which would necessitate being blocked by further crowd-sourced existing filter lists. Moreover, the approach followed in this paper sheds light on the ecosystem of third-party tracking and advertising.