Alistair Moffat


2022

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Accelerating Learned Sparse Indexes Via Term Impact Decomposition
Joel Mackenzie | Antonio Mallia | Alistair Moffat | Matthias Petri
Findings of the Association for Computational Linguistics: EMNLP 2022

Novel inverted index-based learned sparse ranking models provide more effective, but less efficient, retrieval performance compared to traditional ranking models like BM25. In this paper, we introduce a technique we call postings clipping to improve the query efficiency of learned representations. Our technique amplifies the benefit of dynamic pruning query processing techniques by accounting for changes in term importance distributions of learned ranking models. The new clipping mechanism accelerates top-k retrieval by up to 9.6X without any loss in effectiveness.

2014

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Is Machine Translation Getting Better over Time?
Yvette Graham | Timothy Baldwin | Alistair Moffat | Justin Zobel
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

2013

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Continuous Measurement Scales in Human Evaluation of Machine Translation
Yvette Graham | Timothy Baldwin | Alistair Moffat | Justin Zobel
Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse

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Crowd-Sourcing of Human Judgments of Machine Translation Fluency
Yvette Graham | Timothy Baldwin | Alistair Moffat | Justin Zobel
Proceedings of the Australasian Language Technology Association Workshop 2013 (ALTA 2013)

2012

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Measurement of Progress in Machine Translation
Yvette Graham | Timothy Baldwin | Aaron Harwood | Alistair Moffat | Justin Zobel
Proceedings of the Australasian Language Technology Association Workshop 2012