Conversational Question Reformulation via Sequence-to-Sequence Architectures and Pretrained Language Models
Table 3: BLEU score comparison. For simplicity, we compare neural S2S models using greedy search.
['[EMPTY]', 'CANARD Dev', 'CANARD Test', 'CAsT']
In general, all neural S2S models perform better than the original questions (Raw), except for LSTM on CAsT. This indicates that the PLMs (GPT2, BERT, UniLM, and T5) have obtained at least some generalization capability on the CQR task.