Two-Level Transformer and Auxiliary Coherence Modeling for Improved Text Segmentation
Table 2: Performance of text segmentation models in zero-shot language transfer setting on the Wiki-50-X (X ∈ {CS, FI, TR}) datasets.
['[ITALIC] Model', 'CS', 'FI', 'TR']
In this setting, we use our Transformer-based models, trained on the English Wiki-727K dataset, to segment texts from the Wiki-50-X (X ∈ {CS, FI, TR}) datasets in other languages. As a baseline, we additionally evaluate GraphSeg [glavavs2016unsupervised], as a language-agnostic model requiring only pretrained word embeddings of the test language as input. Both our Transformer-based models, TLT-TS and CATS, outperform the unsupervised GraphSeg model (which seems to be only marginally better than the random baseline) by a wide margin. The coherence-aware CATS model is again significantly better (p<0.01 for FI and p<0.05 for CS and TR) than the TLT-TS model which was trained to optimize only the segmentation objective. This is quite encouraging as it suggests that it is possible to, via the zero-shot language transfer, rather reliably segment texts from under-resourced languages lacking sufficiently large gold-segmented data needed to directly train language-specific segmentation models (that is, robust neural segmentation models in particular).