  Named entity recognition (NER) is an important task in NLP, which is all the
more challenging in conversational domain with their noisy facets. Moreover,
conversational texts are often available in limited amount, making supervised
tasks infeasible. To learn from small data, strong inductive biases are
required. Previous work relied on hand-crafted features to encode these biases
until transfer learning emerges. Here, we explore a transfer learning method,
namely language model pretraining, on NER task in Indonesian conversational
texts. We utilize large unlabeled data (generic domain) to be transferred to
conversational texts, enabling supervised training on limited in-domain data.
We report two transfer learning variants, namely supervised model fine-tuning
and unsupervised pretrained LM fine-tuning. Our experiments show that both
variants outperform baseline neural models when trained on small data (100
sentences), yielding an absolute improvement of 32 points of test F1 score.
Furthermore, we find that the pretrained LM encodes part-of-speech information
which is a strong predictor for NER.
