\taskname: Answering Ambiguous Open-domain Questions
Table 5: Zero-shot performance on multiple answer prediction of the models trained on NQ-open. We report Exact Match (EM) on NQ-open and F1ans on AmbigNQ.
['Model', 'NQ-open EM', 'F1ans\xa0( [ITALIC] all)', 'F1ans\xa0( [ITALIC] multi)']
Since AmbigNQ provides an evaluation set with explicit sets of multiple answers, we can also test if models trained on partial supervision only (NQ-open) are capable of producing full answer sets. This may be important for modeling in domains where single-answer datasets are available but full annotations like in AmbigNQ are not. To this end, we present a zero-shot setting where a system predicts multiple distinct answers without using AmbigNQ training data. We include four NQ-open models including ours, consisting of diverse approaches and model architectures, as baselines. Although Min et al. In addition, although SpanSeqGen outperforms Karpukhin et al.