71 citations · 117 across the 14 of their papers we have counts for
13 papers · 1 filter
External Language Model Integration for Factorized Neural Transducers
Michael Levit, Sarangarajan Parthasarathy, Cem Aksoylar +2
We propose an adaptation method for factorized neural transducers (FNT) with external language models. We demonstrate that both neural and n-gram external LMs add significantly mor…
Multilingual Bidirectional Unsupervised Translation Through Multilingual Finetuning and Back-Translation
Bryan Li, Mohammad Sadegh Rasooli, Ajay Patel +1
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…
"Wikily" Supervised Neural Translation Tailored to Cross-Lingual Tasks
Mohammad Sadegh Rasooli, Chris Callison-Burch, Derry Tanti Wijaya
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…
Automatic Standardization of Colloquial Persian
Mohammad Sadegh Rasooli, Farzane Bakhtyari, Fatemeh Shafiei +2
The Iranian Persian language has two varieties: standard and colloquial. Most natural language processing tools for Persian assume that the text is in standard form: this assumptio…
ParsiNLU: A Suite of Language Understanding Challenges for Persian
Daniel Khashabi, Arman Cohan, Siamak Shakeri +22
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 l…
The Persian Dependency Treebank Made Universal
Mohammad Sadegh Rasooli, Pegah Safari, Amirsaeid Moloodi +1
We describe an automatic method for converting the Persian Dependency Treebank (Rasooli et al, 2013) to Universal Dependencies. This treebank contains 29107 sentences. Our experime…
Mutlitask Learning for Cross-Lingual Transfer of Semantic Dependencies
Maryam Aminian, Mohammad Sadegh Rasooli, Mona Diab
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 lear…
Cross-Lingual Transfer of Semantic Roles: From Raw Text to Semantic Roles
Maryam Aminian, Mohammad Sadegh Rasooli, Mona Diab
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 informatio…
Low-Resource Syntactic Transfer with Unsupervised Source Reordering
Mohammad Sadegh Rasooli, Michael Collins
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…
Entity-Aware Language Model as an Unsupervised Reranker
Mohammad Sadegh Rasooli, Sarangarajan Parthasarathy
In language modeling, it is difficult to incorporate entity relationships from a knowledge-base. One solution is to use a reranker trained with global features, in which global fea…
Transferring Semantic Roles Using Translation and Syntactic Information
Maryam Aminian, Mohammad Sadegh Rasooli, Mona Diab
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 p…
Cross-Lingual Syntactic Transfer with Limited Resources
Mohammad Sadegh Rasooli, Michael Collins
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. The…
Yara Parser: A Fast and Accurate Dependency Parser
Mohammad Sadegh Rasooli, Joel Tetreault
Dependency parsers are among the most crucial tools in natural language processing as they have many important applications in downstream tasks such as information retrieval, machi…