Wiki to Automotive: Understanding the Distribution Shift and its impact on Named Entity Recognition

TL;DR

This paper investigates the distribution shift in automotive domain text and its impact on Named Entity Recognition, revealing challenges like OOV words and context sparsity that hinder transfer learning performance.

Contribution

It provides an analysis of distribution shift characteristics in automotive text and evaluates the performance of BERT and SciBERT models on NER tasks in this domain.

Findings

SciBERT outperforms BERT in automotive NER

Fine-tuning with domain data yields limited improvements

Distribution shift features include OOV words and context sparsity

Abstract

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.