Papers by Nima Pourdamghani
Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events (2020.emnlp-main)
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Miguel Ballesteros, Rishita Anubhai, Shuai Wang, Nima Pourdamghani, Yogarshi Vyas, Jie Ma, Parminder Bhatia, Kathleen McKeown, Yaser Al-Onaizan
| Challenge: | Existing models for temporal ordering of events rely on pretrained representations, transfer and multitask learning, and self-training techniques. |
| Approach: | They propose a neural architecture and a set of training methods for ordering events by predicting temporal relations by pre-training models. |
| Outcome: | The proposed models can predict temporal relations between two pairs of events within a span of text and identify temporal relationships between them. |
Translating Translationese: A Two-Step Approach to Unsupervised Machine Translation (P19-1)
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| Challenge: | Using a dictionary, given a rough, target language natives can uncover the latent, fully-fluent rendering of the translation. |
| Approach: | They propose a method that breaks translation into two steps by generating a dictionary and then ‘translating’ the resulting pseudo-translation into a fully fluent translation. |
| Outcome: | The proposed method 'gets better translation results on high-resource languages than previously published unsupervised MT studies' |
Using Word Vectors to Improve Word Alignments for Low Resource Machine Translation (N18-2)
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| Challenge: | Using word similarities, we improve word alignments in low resource settings . word alignment is essential for statistical machine translation (MT) |
| Approach: | They propose a method for improving word alignments using word similarities using word vectors trained on monolingual data. |
| Outcome: | The proposed method improves word alignments in low-resource settings by improving alignments of infrequent tokens. |