Ablation Studies Analysis Sample Clauses

Ablation Studies Analysis. To find out which step is important for the question answering task we did the ablation studies by experimenting with approaches step by step, Table is the results of the ablation studies of the experiment. In this ablation studies we add the speaker name into the vocabulary to enable the speaker representation can be learned better. From the results From the results, we found that the main improvement is based on joint learning of utterance id prediction which pre-training tasks help. We found following conclusions: 1. None of the language modelings in Section 2.1 made much impact without getting coupled with the joint learning of the utterance ID prediction (UID) in Section 2.2.1. This is indeed encouraging since all the proposed language modelings are designed to help UID, not the span prediction. 2. With only the token-level LM (2.1.1), UID still didn’t make much impact. 3. With the utterance-level LM (2.1.2), UID gave over 1% boost on both SM and UM for XXXX models and about 0.5% boost on both SM and UM for XxXXXXx models. 4. With the utterance-order prediction (2.1.3), UID gave the rest of the boost, which was slightly higher than the ones achieved by the utterance-level LM. Method EM SM UM BERTpre with uid loss 45.7(±0.8) 61.1(±0.8) 71.5(±0.5) BERTpre without uid loss 45.6(±0.9) 61.2(±0.7) 71.3(±0.6) BERTpre+ulm with uid loss 46.2(±1.1) 62.4(±1.2) 72.5(±0.8) BERTpre+ulm without uid loss 45.7(±0.9) 61.8(±0.9) 71.8(±0.5) BERTpre+ulm+uop with uid loss 46.8(±1.3) 63.1(±1.1) 73.3(±0.7) BERTpre+ulm+uop without uid loss 45.6(±0.9) 61.7(±0.7) 71.7(±0.6) RoBERTapre with uid loss 52.8(±0.9) 68.7(±0.8) 81.9(±0.5) RoBERTapre without uid loss 52.6(±0.7) 68.6(±0.6) 81.7(±0.7) RoBERTapre+ulm with uid loss 53.2(±0.6) 69.2(±0.7) 82.4(±0.5) RoBERTapre+ulm without uid loss 52.9(±0.8) 68.7(±1.1) 81.7(±0.6) RoBERTapre+ulm+uop with uid loss 53.5(±0.7) 69.6(±0.8) 82.7(±0.5) RoBERTapre+ulm+uop without uid loss 52.5(±0.8) 68.8(±0.5) 81.9(±0.7) Table 5.1: Results for the ablation studies:where BERTpre or RoBERTapre is the first task of the pretraining, ulm is utterance level language model task and uop is the utterance order prediction task. with uid loss or without uid loss represent if we use joint learning of two tasks when doing fine-tuning.
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