Mohammad Sadegh Rasooli


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Multilingual Bidirectional Unsupervised Translation through Multilingual Finetuning and Back-Translation
Bryan Li | Mohammad Sadegh Rasooli | Ajay Patel | Chris Callison-burch
Proceedings of the The Sixth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2023)

We propose a two-stage approach for training a single NMT model to translate unseen languages both to and from English. For the first stage, we initialize an encoder-decoder model to pretrained XLM-R and RoBERTa weights, then perform multilingual fine-tuning on parallel data in 40 languages to English. We find this model can generalize to zero-shot translations on unseen languages. For the second stage, we leverage this generalization ability to generate synthetic parallel data from monolingual datasets, then bidirectionally train with successive rounds of back-translation. Our approach, which we EcXTra (uE/unglish-uc/uentric Crosslingual (uX/u) uTra/unsfer), is conceptually simple, only using a standard cross-entropy objective throughout. It is also data-driven, sequentially leveraging auxiliary parallel data and monolingual data. We evaluate unsupervised NMT results for 7 low-resource languages, and find that each round of back-translation training further refines bidirectional performance. Our final single EcXTra-trained model achieves competitive translation performance in all translation directions, notably establishing a new state-of-the-art for English-to-Kazakh (22.9 10.4 BLEU). Our code is available at [this URL](


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The Persian Dependency Treebank Made Universal
Pegah Safari | Mohammad Sadegh Rasooli | Amirsaeid Moloodi | Alireza Nourian
Proceedings of the Thirteenth Language Resources and Evaluation Conference

We describe an automatic method for converting the Persian Dependency Treebank (Rasooli et al., 2013) to Universal Dependencies. This treebank contains 29107 sentences. Our experiments along with manual linguistic analysis show that our data is more compatible with Universal Dependencies than the Uppsala Persian Universal Dependency Treebank (Seraji et al., 2016), larger in size and more diverse in vocabulary. Our data brings in labeled attachment F-score of 85.2 in supervised parsing. Also, our delexicalized Persian-to-English parser transfer experiments show that a parsing model trained on our data is ≈2% absolutely more accurate than that of Seraji et al. (2016) in terms of labeled attachment score.


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Cultural and Geographical Influences on Image Translatability of Words across Languages
Nikzad Khani | Isidora Tourni | Mohammad Sadegh Rasooli | Chris Callison-Burch | Derry Tanti Wijaya
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Neural Machine Translation (NMT) models have been observed to produce poor translations when there are few/no parallel sentences to train the models. In the absence of parallel data, several approaches have turned to the use of images to learn translations. Since images of words, e.g., horse may be unchanged across languages, translations can be identified via images associated with words in different languages that have a high degree of visual similarity. However, translating via images has been shown to improve upon text-only models only marginally. To better understand when images are useful for translation, we study image translatability of words, which we define as the translatability of words via images, by measuring intra- and inter-cluster similarities of image representations of words that are translations of each other. We find that images of words are not always invariant across languages, and that language pairs with shared culture, meaning having either a common language family, ethnicity or religion, have improved image translatability (i.e., have more similar images for similar words) compared to its converse, regardless of their geographic proximity. In addition, in line with previous works that show images help more in translating concrete words, we found that concrete words have improved image translatability compared to abstract ones.

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“Wikily” Supervised Neural Translation Tailored to Cross-Lingual Tasks
Mohammad Sadegh Rasooli | Chris Callison-Burch | Derry Tanti Wijaya
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

We present a simple but effective approach for leveraging Wikipedia for neural machine translation as well as cross-lingual tasks of image captioning and dependency parsing without using any direct supervision from external parallel data or supervised models in the target language. We show that first sentences and titles of linked Wikipedia pages, as well as cross-lingual image captions, are strong signals for a seed parallel data to extract bilingual dictionaries and cross-lingual word embeddings for mining parallel text from Wikipedia. Our final model achieves high BLEU scores that are close to or sometimes higher than strong supervised baselines in low-resource languages; e.g. supervised BLEU of 4.0 versus 12.1 from our model in English-to-Kazakh. Moreover, we tailor our wikily translation models to unsupervised image captioning, and cross-lingual dependency parser transfer. In image captioning, we train a multi-tasking machine translation and image captioning pipeline for Arabic and English from which the Arabic training data is a wikily translation of the English captioning data. Our captioning results on Arabic are slightly better than that of its supervised model. In dependency parsing, we translate a large amount of monolingual text, and use it as an artificial training data in an annotation projection framework. We show that our model outperforms recent work on cross-lingual transfer of dependency parsers.

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ParsiNLU: A Suite of Language Understanding Challenges for Persian
Daniel Khashabi | Arman Cohan | Siamak Shakeri | Pedram Hosseini | Pouya Pezeshkpour | Malihe Alikhani | Moin Aminnaseri | Marzieh Bitaab | Faeze Brahman | Sarik Ghazarian | Mozhdeh Gheini | Arman Kabiri | Rabeeh Karimi Mahabagdi | Omid Memarrast | Ahmadreza Mosallanezhad | Erfan Noury | Shahab Raji | Mohammad Sadegh Rasooli | Sepideh Sadeghi | Erfan Sadeqi Azer | Niloofar Safi Samghabadi | Mahsa Shafaei | Saber Sheybani | Ali Tazarv | Yadollah Yaghoobzadeh
Transactions of the Association for Computational Linguistics, Volume 9

Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich languages like English. This work focuses on Persian language, one of the widely spoken languages in the world, and yet there are few NLU datasets available for this language. The availability of high-quality evaluation datasets is a necessity for reliable assessment of the progress on different NLU tasks and domains. We introduce ParsiNLU, the first benchmark in Persian language that includes a range of language understanding tasks—reading comprehension, textual entailment, and so on. These datasets are collected in a multitude of ways, often involving manual annotations by native speakers. This results in over 14.5k new instances across 6 distinct NLU tasks. Additionally, we present the first results on state-of-the-art monolingual and multilingual pre-trained language models on this benchmark and compare them with human performance, which provides valuable insights into our ability to tackle natural language understanding challenges in Persian. We hope ParsiNLU fosters further research and advances in Persian language understanding.1


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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.


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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.


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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.


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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)


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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

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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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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


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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