FFCD: A Fast-and-Frugal Coherence Detection Method

Abstract

In the era of heavy emphasis on deep neural architectures with well-proven and impressive competencies albeit with massive carbon footprints, we present a simple and inexpensive solution founded on Bidirectional Encoder Representations from Transformers Next Sentence Prediction (BERT NSP) task to localize sequential discourse coherence in a text. We propose Fast and Frugal Coherence Detection (FFCD) method, which is an effective tool for the author to incarcerate regions of weak coherence at the sentence level and reveal the extent of overall coherence of the document in near real-time. We leverage the pre-trained BERT NSP model for sequential coherence detection at sentence-to-sentence transition and evaluate the performance of the proposed FFCD approach on coherence detection tasks using publicly available datasets. The mixed performance of our solution compared to state-of-the-art methods for coherence detection invigorates efforts to design explainable and inexpensive solutions downstream of the existing upscale language models.