Subhajit Naskar — accepted AI-conference papers

2024

Quality-Aware Translation Models: Efficient Generation and Quality Estimation in a Single Model

Christian Tomani, David Vilar, Markus Freitag, Colin Cherry, Subhajit Naskar, Mara Finkelstein +2

ACL 2024long

Maximum-a-posteriori (MAP) decoding is the most widely used decoding strategy for neural machine translation (NMT) models. The underlying assumption is that model probability correlates well with human judgment, with better translations getting assigned a higher score by the model. However, research…

Cited by 3 Source ↗PDF ↗code ↗Scholar ↗

2021

Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models

Sumanta Bhattacharyya, Amirmohammad Rooshenas, Subhajit Naskar, Simeng Sun, Mohit Iyyer, Andrew McCallum

ACL 2021long

The discrepancy between maximum likelihood estimation (MLE) and task measures such as BLEU score has been studied before for autoregressive neural machine translation (NMT) and resulted in alternative training algorithms (Ranzato et al., 2016; Norouzi et al., 2016; Shen et al., 2016; Wu et al., 2018…

Cited by 48 Source ↗PDF ↗code ↗Scholar ↗