Masud Moshtaghi


Supervised and Nonlinear Alignment of Two Embedding Spaces for Dictionary Induction in Low Resourced Languages
Masud Moshtaghi
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Enabling cross-lingual NLP tasks by leveraging multilingual word embedding has recently attracted much attention. An important motivation is to support lower resourced languages, however, most efforts focus on demonstrating the effectiveness of the techniques using embeddings derived from similar languages to English with large parallel content. In this study, we first describe the general requirements for the success of these techniques and then present a noise tolerant piecewise linear technique to learn a non-linear mapping between two monolingual word embedding vector spaces. We evaluate our approach on inferring bilingual dictionaries. We show that our technique outperforms the state-of-the-art in lower resourced settings with an average of 3.7% improvement of precision @10 across 14 mostly low resourced languages.


A Comparative Study of Weighting Schemes for the Interpretation of Spoken Referring Expressions
Su Nam Kim | Ingrid Zukerman | Thomas Kleinbauer | Masud Moshtaghi
Proceedings of the Australasian Language Technology Association Workshop 2014


A Support Platform for Event Detection using Social Intelligence
Timothy Baldwin | Paul Cook | Bo Han | Aaron Harwood | Shanika Karunasekera | Masud Moshtaghi
Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics