Evaluating Pretrained Transformer Models for Entity Linking in Task-Oriented Dialog
This work addresses the problem of entity linking in task-oriented dialog for NLP researchers, but it is incremental as it focuses on evaluating existing models rather than proposing new ones.
The paper evaluates pretrained transformer models (PTMs) for unsupervised entity linking in task-oriented dialog, finding that many PTMs perform sub-par compared to traditional methods but competitive with neural baselines, with fine-tuning for text-similarity tasks improving results in semantic, syntactic, and other characteristics.
The wide applicability of pretrained transformer models (PTMs) for natural language tasks is well demonstrated, but their ability to comprehend short phrases of text is less explored. To this end, we evaluate different PTMs from the lens of unsupervised Entity Linking in task-oriented dialog across 5 characteristics -- syntactic, semantic, short-forms, numeric and phonetic. Our results demonstrate that several of the PTMs produce sub-par results when compared to traditional techniques, albeit competitive to other neural baselines. We find that some of their shortcomings can be addressed by using PTMs fine-tuned for text-similarity tasks, which illustrate an improved ability in comprehending semantic and syntactic correspondences, as well as some improvements for short-forms, numeric and phonetic variations in entity mentions. We perform qualitative analysis to understand nuances in their predictions and discuss scope for further improvements. Code can be found at https://github.com/murali1996/el_tod