  In the last few years, the release of BERT, a multilingual transformer based
model, has taken the NLP community by storm. BERT-based models have achieved
state-of-the-art results on various NLP tasks, including dialog tasks. One of
the limitation of BERT is the lack of ability to handle long text sequence. By
default, BERT has a maximum wordpiece token sequence length of 512. Recently,
there has been renewed interest to tackle the BERT limitation to handle long
text sequences with the addition of new self-attention based architectures.
However, there has been little to no research on the impact of this limitation
with respect to dialog tasks. Dialog tasks are inherently different from other
NLP tasks due to: a) the presence of multiple utterances from multiple
speakers, which may be interlinked to each other across different turns and b)
longer length of dialogs. In this work, we empirically evaluate the impact of
dialog length on the performance of BERT model for the Next Response Selection
dialog task on four publicly available and one internal multi-turn dialog
datasets. We observe that there is little impact on performance with long
dialogs and even the simplest approach of truncating input works really well.
