  Recently, there has been considerable attention on detecting hallucinations
and omissions in Machine Translation (MT) systems. The two dominant approaches
to tackle this task involve analyzing the MT system's internal states or
relying on the output of external tools, such as sentence similarity or MT
quality estimators. In this work, we introduce OTTAWA, a novel Optimal
Transport (OT)-based word aligner specifically designed to enhance the
detection of hallucinations and omissions in MT systems. Our approach
explicitly models the missing alignments by introducing a "null" vector, for
which we propose a novel one-side constrained OT setting to allow an adaptive
null alignment. Our approach yields competitive results compared to
state-of-the-art methods across 18 language pairs on the HalOmi benchmark. In
addition, it shows promising features, such as the ability to distinguish
between both error types and perform word-level detection without accessing the
MT system's internal states.
