Seyed Abolghasem Mirroshandel

Also published as: Seyed Abolghasem Mirroshandel


2023

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RobustQA: A Framework for Adversarial Text Generation Analysis on Question Answering Systems
Yasaman Boreshban | Seyed Morteza Mirbostani | Seyedeh Fatemeh Ahmadi | Gita Shojaee | Fatemeh Kamani | Gholamreza Ghassem-Sani | Seyed Abolghasem Mirroshandel
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Question answering (QA) systems have reached human-level accuracy; however, these systems are not robust enough and are vulnerable to adversarial examples. Recently, adversarial attacks have been widely investigated in text classification. However, there have been few research efforts on this topic in QA. In this article, we have modified the attack algorithms widely used in text classification to fit those algorithms for QA systems. We have evaluated the impact of various attack methods on QA systems at character, word, and sentence levels. Furthermore, we have developed a new framework, named RobustQA, as the first open-source toolkit for investigating textual adversarial attacks in QA systems. RobustQA consists of seven modules: Tokenizer, Victim Model, Goals, Metrics, Attacker, Attack Selector, and Evaluator. It currently supports six different attack algorithms. Furthermore, the framework simplifies the development of new attack algorithms in QA. The source code and documentation of RobustQA are available at https://github.com/mirbostani/RobustQA.

2016

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Integrating Selectional Constraints and Subcategorization Frames in a Dependency Parser
Seyed Abolghasem Mirroshandel | Alexis Nasr
Computational Linguistics, Volume 42, Issue 1 - March 2016

2013

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Enforcing Subcategorization Constraints in a Parser Using Sub-parses Recombining
Seyed Abolghasem Mirroshandel | Alexis Nasr | Benoît Sagot
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Temporal Relation Classification in Persian and English contexts
Mahbaneh Eshaghzadeh Torbati | Gholamreza Ghassem-sani | Seyed Abolghasem Mirroshandel | Yadollah Yaghoobzadeh | Negin Karimi Hosseini
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

2012

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Generative Constituent Parsing and Discriminative Dependency Reranking: Experiments on English and French
Joseph Le Roux | Benoît Favre | Alexis Nasr | Seyed Abolghasem Mirroshandel
Proceedings of the ACL 2012 Joint Workshop on Statistical Parsing and Semantic Processing of Morphologically Rich Languages

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Semi-supervised Dependency Parsing using Lexical Affinities
Seyed Abolghasem Mirroshandel | Alexis Nasr | Joseph Le Roux
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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ISO-TimeML Event Extraction in Persian Text
Yadollah Yaghoobzadeh | Gholamreza Ghassem-sani | Seyed Abolghasem Mirroshandel | Mahbaneh Eshaghzadeh
Proceedings of COLING 2012

2011

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Active Learning Strategies for Support Vector Machines, Application to Temporal Relation Classification
Seyed Abolghasem Mirroshandel | Gholamreza Ghassem-Sani | Alexis Nasr
Proceedings of 5th International Joint Conference on Natural Language Processing

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Active Learning for Dependency Parsing Using Partially Annotated Sentences
Seyed Abolghasem Mirroshandel | Alexis Nasr
Proceedings of the 12th International Conference on Parsing Technologies

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Modèles génératif et discriminant en analyse syntaxique : expériences sur le corpus arboré de Paris 7 (Generative and discriminative models in parsing: experiments on the Paris 7 Treebank)
Joseph Le Roux | Benoît Favre | Seyed Abolghasem Mirroshandel | Alexis Nasr
Actes de la 18e conférence sur le Traitement Automatique des Langues Naturelles. Articles longs

Nous présentons une architecture pour l’analyse syntaxique en deux étapes. Dans un premier temps un analyseur syntagmatique construit, pour chaque phrase, une liste d’analyses qui sont converties en arbres de dépendances. Ces arbres sont ensuite réévalués par un réordonnanceur discriminant. Cette méthode permet de prendre en compte des informations auxquelles l’analyseur n’a pas accès, en particulier des annotations fonctionnelles. Nous validons notre approche par une évaluation sur le corpus arboré de Paris 7. La seconde étape permet d’améliorer significativement la qualité des analyses retournées, quelle que soit la métrique utilisée.

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Temporal Relation Extraction Using Expectation Maximization
Seyed Abolghasem Mirroshandel | Gholamreza Ghassem-Sani
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

2009

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Syntactic Tree-based Relation Extraction Using a Generalization of Collins and Duffy Convolution Tree Kernel
Mahdy Khayyamian | Seyed Abolghasem Mirroshandel | Hassan Abolhassani
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium

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Using Tree Kernels for Classifying Temporal Relations between Events
Seyed Abolghasem Mirroshandel | Gholamreza Ghassem-Sani | Mahdy Khayyamian
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 1