Wiki to Automotive: Understanding the Distribution Shift and its impact on Named Entity Recognition
This addresses performance gaps in NLP for niche domains like automotive, but it is incremental as it analyzes existing methods without introducing new techniques.
The paper investigates why pre-trained models like BERT underperform on Named Entity Recognition (NER) in the automotive domain, finding that distribution shifts due to sparse entities and out-of-vocabulary words cause challenges, with SciBERT outperforming BERT but fine-tuning offering minimal gains.
While transfer learning has become a ubiquitous technique used across Natural Language Processing (NLP) tasks, it is often unable to replicate the performance of pre-trained models on text of niche domains like Automotive. In this paper we aim to understand the main characteristics of the distribution shift with automotive domain text (describing technical functionalities such as Cruise Control) and attempt to explain the potential reasons for the gap in performance. We focus on performing the Named Entity Recognition (NER) task as it requires strong lexical, syntactic and semantic understanding by the model. Our experiments with 2 different encoders, namely BERT-Base-Uncased and SciBERT-Base-Scivocab-Uncased have lead to interesting findings that showed: 1) The performance of SciBERT is better than BERT when used for automotive domain, 2) Fine-tuning the language models with automotive domain text did not make significant improvements to the NER performance, 3) The distribution shift is challenging as it is characterized by lack of repeating contexts, sparseness of entities, large number of Out-Of-Vocabulary (OOV) words and class overlap due to domain specific nuances.