Amir Ahmad Habibi


2021

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Homonymy and Polysemy Detection with Multilingual Information
Amir Ahmad Habibi | Bradley Hauer | Grzegorz Kondrak
Proceedings of the 11th Global Wordnet Conference

Deciding whether a semantically ambiguous word is homonymous or polysemous is equivalent to establishing whether it has any pair of senses that are semantically unrelated. We present novel methods for this task that leverage information from multilingual lexical resources. We formally prove the theoretical properties that provide the foundation for our methods. In particular, we show how the One Homonym Per Translation hypothesis of Hauer and Kondrak (2020a) follows from the synset properties formulated by Hauer and Kondrak (2020b). Experimental evaluation shows that our approach sets a new state of the art for homonymy detection.

2020

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UAlberta at SemEval-2020 Task 2: Using Translations to Predict Cross-Lingual Entailment
Bradley Hauer | Amir Ahmad Habibi | Yixing Luan | Arnob Mallik | Grzegorz Kondrak
Proceedings of the Fourteenth Workshop on Semantic Evaluation

We investigate the hypothesis that translations can be used to identify cross-lingual lexical entailment. We propose novel methods that leverage parallel corpora, word embeddings, and multilingual lexical resources. Our results demonstrate that the implementation of these ideas leads to improvements in predicting entailment.

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Low-Resource G2P and P2G Conversion with Synthetic Training Data
Bradley Hauer | Amir Ahmad Habibi | Yixing Luan | Arnob Mallik | Grzegorz Kondrak
Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

This paper presents the University of Alberta systems and results in the SIGMORPHON 2020 Task 1: Multilingual Grapheme-to-Phoneme Conversion. Following previous SIGMORPHON shared tasks, we define a low-resource setting with 100 training instances. We experiment with three transduction approaches in both standard and low-resource settings, as well as on the related task of phoneme-to-grapheme conversion. We propose a method for synthesizing training data using a combination of diverse models.

2019

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Cognate Projection for Low-Resource Inflection Generation
Bradley Hauer | Amir Ahmad Habibi | Yixing Luan | Rashed Rubby Riyadh | Grzegorz Kondrak
Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology

We propose cognate projection as a method of crosslingual transfer for inflection generation in the context of the SIGMORPHON 2019 Shared Task. The results on four language pairs show the method is effective when no low-resource training data is available.