We investigate the design challenges of constructing effective and efficient neural sequence labeling systems, by reproducing twelve neural sequence labeling models, which include most of the state-of-the-art structures, and conduct a systematic model comparison on three benchmarks (i.e. NER, Chunking, and POS tagging). Misconceptions and inconsistent conclusions in existing literature are examined and clarified under statistical experiments. In the comparison and analysis process, we reach several practical conclusions which can be useful to practitioners.
al REF build a neural sequence labeling framework 7 to reproduce the state-of-the-art models, while we build a portable framework and also conduct experiments in different languages and domains.
Design Challenges and Misconceptions in Neural Sequence Labeling
ArXiv
ArXiv
Computer Science