# Design Challenges and Misconceptions in Neural Sequence Labeling

arXiv: https://arxiv.org/abs/1806.04470 · HTML: https://mlsanity.com/paper/1806.04470/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2018.

## Built on by (most cited first, 28 of 28)

- [Template-Based Named Entity Recognition Using BART](https://mlsanity.com/paper/2106.01760/) (2021)
- [Named Entity Recognition and Classification on Historical Documents: A Survey](https://mlsanity.com/paper/2109.11406/) (2021)
- [A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition](https://mlsanity.com/paper/2106.14373/) (2021)
- [LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting](https://mlsanity.com/paper/2109.00720/) (2021)
- [Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning](https://mlsanity.com/paper/2208.14565/) (2022)
- [MedJEx: A Medical Jargon Extraction Model with Wiki's Hyperlink Span and Contextualized Masked Language Model Score](https://mlsanity.com/paper/2210.05875/) (2022)
- [METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets](https://mlsanity.com/paper/2209.13773/) (2022)
- [One Model for All Domains: Collaborative Domain-Prefix Tuning for Cross-Domain NER](https://mlsanity.com/paper/2301.10410/) (2023)
- [Experimental Standards for Deep Learning in Natural Language Processing Research](https://mlsanity.com/paper/2204.06251/) (2022)
- [SeqScore: Addressing Barriers to Reproducible Named Entity Recognition Evaluation](https://mlsanity.com/paper/2107.14154/) (2021)
- [TOE: A Grid-Tagging Discontinuous NER Model Enhanced by Embedding Tag/Word Relations and More Fine-Grained Tags](https://mlsanity.com/paper/2211.00684/) (2022)
- [Know What I don't Know: Handling Ambiguous and Unanswerable Questions for Text-to-SQL](https://mlsanity.com/paper/2212.08902/) (2022)
- [Improving Few-Shot Cross-Domain Named Entity Recognition by Instruction Tuning a Word-Embedding based Retrieval Augmented Large Language Model](https://mlsanity.com/paper/2411.00451/) (2024)
- [TMR: Evaluating NER Recall on Tough Mentions](https://mlsanity.com/paper/2103.12312/) (2021)
- [Larger-Context Tagging: When and Why Does It Work?](https://mlsanity.com/paper/2104.04434/) (2021)
- [MISE: Meta-knowledge Inheritance for Social Media-Based Stressor Estimation](https://mlsanity.com/paper/2505.03827/) (2025)
- [Neural Supervised Domain Adaptation by Augmenting Pre-trained Models with Random Units](https://mlsanity.com/paper/2106.04935/) (2021)
- [YATO: Yet Another deep learning based Text analysis Open toolkit](https://mlsanity.com/paper/2209.13877/) (2022)
- [LMN at SemEval-2022 Task 11: A Transformer-based System for English Named Entity Recognition](https://mlsanity.com/paper/2203.03546/) (2022)
- [Prompt-based Text Entailment for Low-Resource Named Entity Recognition](https://mlsanity.com/paper/2211.03039/) (2022)
- [A Span-based Model for Extracting Overlapping PICO Entities from RCT Publications](https://mlsanity.com/paper/2401.06791/) (2024)
- [Assessment of Generative Named Entity Recognition in the Era of Large Language Models](https://mlsanity.com/paper/2601.17898/) (2026)
- [Learning the Ordering of Coordinate Compounds and Elaborate Expressions in Hmong, Lahu, and Chinese](https://mlsanity.com/paper/2204.04080/) (2022)
- [ADEQA: A Question Answer based approach for joint ADE-Suspect Extraction using Sequence-To-Sequence Transformers](https://mlsanity.com/paper/2412.15510/) (2024)
- [myNER: Contextualized Burmese Named Entity Recognition with Bidirectional LSTM and fastText Embeddings via Joint Training with POS Tagging](https://mlsanity.com/paper/2504.04038/) (2025)
- [Towards Efficient Quantity Retrieval from Text:An Approach via Description Parsing and Weak Supervision](https://mlsanity.com/paper/2507.08322/) (2025)
- [Weakly Supervised Medical Entity Extraction and Linking for Chief Complaints](https://mlsanity.com/paper/2509.01899/) (2025)
- [Named Entity Recognition in COVID-19 tweets with Entity Knowledge Augmentation](https://mlsanity.com/paper/2510.04001/) (2025)

## Abstract (arXiv)

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.