Mohammad Sadegh Rasooli


2020

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Multitask Learning for Cross-Lingual Transfer of Broad-coverage Semantic Dependencies
Maryam Aminian | Mohammad Sadegh Rasooli | Mona Diab
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We describe a method for developing broad-coverage semantic dependency parsers for languages for which no semantically annotated resource is available. We leverage a multitask learning framework coupled with annotation projection. We use syntactic parsing as the auxiliary task in our multitask setup. Our annotation projection experiments from English to Czech show that our multitask setup yields 3.1% (4.2%) improvement in labeled F1-score on in-domain (out-of-domain) test set compared to a single-task baseline.

2019

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Cross-Lingual Transfer of Semantic Roles: From Raw Text to Semantic Roles
Maryam Aminian | Mohammad Sadegh Rasooli | Mona Diab
Proceedings of the 13th International Conference on Computational Semantics - Long Papers

We describe a transfer method based on annotation projection to develop a dependency-based semantic role labeling system for languages for which no supervised linguistic information other than parallel data is available. Unlike previous work that presumes the availability of supervised features such as lemmas, part-of-speech tags, and dependency parse trees, we only make use of word and character features. Our deep model considers using character-based representations as well as unsupervised stem embeddings to alleviate the need for supervised features. Our experiments outperform a state-of-the-art method that uses supervised lexico-syntactic features on 6 out of 7 languages in the Universal Proposition Bank.

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Low-Resource Syntactic Transfer with Unsupervised Source Reordering
Mohammad Sadegh Rasooli | Michael Collins
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

We describe a cross-lingual transfer method for dependency parsing that takes into account the problem of word order differences between source and target languages. Our model only relies on the Bible, a considerably smaller parallel data than the commonly used parallel data in transfer methods. We use the concatenation of projected trees from the Bible corpus, and the gold-standard treebanks in multiple source languages along with cross-lingual word representations. We demonstrate that reordering the source treebanks before training on them for a target language improves the accuracy of languages outside the European language family. Our experiments on 68 treebanks (38 languages) in the Universal Dependencies corpus achieve a high accuracy for all languages. Among them, our experiments on 16 treebanks of 12 non-European languages achieve an average UAS absolute improvement of 3.3% over a state-of-the-art method.

2017

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Transferring Semantic Roles Using Translation and Syntactic Information
Maryam Aminian | Mohammad Sadegh Rasooli | Mona Diab
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Our paper addresses the problem of annotation projection for semantic role labeling for resource-poor languages using supervised annotations from a resource-rich language through parallel data. We propose a transfer method that employs information from source and target syntactic dependencies as well as word alignment density to improve the quality of an iterative bootstrapping method. Our experiments yield a 3.5 absolute labeled F-score improvement over a standard annotation projection method.

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Cross-Lingual Syntactic Transfer with Limited Resources
Mohammad Sadegh Rasooli | Michael Collins
Transactions of the Association for Computational Linguistics, Volume 5

We describe a simple but effective method for cross-lingual syntactic transfer of dependency parsers, in the scenario where a large amount of translation data is not available. This method makes use of three steps: 1) a method for deriving cross-lingual word clusters, which can then be used in a multilingual parser; 2) a method for transferring lexical information from a target language to source language treebanks; 3) a method for integrating these steps with the density-driven annotation projection method of Rasooli and Collins (2015). Experiments show improvements over the state-of-the-art in several languages used in previous work, in a setting where the only source of translation data is the Bible, a considerably smaller corpus than the Europarl corpus used in previous work. Results using the Europarl corpus as a source of translation data show additional improvements over the results of Rasooli and Collins (2015). We conclude with results on 38 datasets from the Universal Dependencies corpora.

2015

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Density-Driven Cross-Lingual Transfer of Dependency Parsers
Mohammad Sadegh Rasooli | Michael Collins
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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On the Importance of Ezafe Construction in Persian Parsing
Alireza Nourian | Mohammad Sadegh Rasooli | Mohsen Imany | Heshaam Faili
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2014

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Unsupervised Morphology-Based Vocabulary Expansion
Mohammad Sadegh Rasooli | Thomas Lippincott | Nizar Habash | Owen Rambow
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Non-Monotonic Parsing of Fluent Umm I mean Disfluent Sentences
Mohammad Sadegh Rasooli | Joel Tetreault
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers

2013

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Joint Parsing and Disfluency Detection in Linear Time
Mohammad Sadegh Rasooli | Joel Tetreault
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Orthographic and Morphological Processing for Persian-to-English Statistical Machine Translation
Mohammad Sadegh Rasooli | Ahmed El Kholy | Nizar Habash
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Development of a Persian Syntactic Dependency Treebank
Mohammad Sadegh Rasooli | Manouchehr Kouhestani | Amirsaeid Moloodi
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2012

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Fast Unsupervised Dependency Parsing with Arc-Standard Transitions
Mohammad Sadegh Rasooli | Heshaam Faili
Proceedings of the Joint Workshop on Unsupervised and Semi-Supervised Learning in NLP