Ali Dabirmoghaddam — accepted AI-conference papers

2024

Finding Replicable Human Evaluations via Stable Ranking Probability

Parker Riley, Daniel Deutsch, George Foster, Viresh Ratnakar, Ali Dabirmoghaddam, Markus Freitag

NAACL 2024long

Reliable human evaluation is critical to the development of successful natural language generation models, but achieving it is notoriously difficult. Stability is a crucial requirement when ranking systems by quality: consistent ranking of systems across repeated evaluations is not just desirable, b…

Cited by 3 Source ↗PDF ↗code ↗Scholar ↗

2022

Multilingual Document-Level Translation Enables Zero-Shot Transfer From Sentences to Documents

Biao Zhang, Ankur Bapna, Melvin Johnson, Ali Dabirmoghaddam, Naveen Arivazhagan, Orhan Firat

ACL 2022long

Document-level neural machine translation (DocNMT) achieves coherent translations by incorporating cross-sentence context. However, for most language pairs there’s a shortage of parallel documents, although parallel sentences are readily available. In this paper, we study whether and how contextual…

Cited by 15 Source ↗PDF ↗Scholar ↗