# Hierarchically-Refined Label Attention Network for Sequence Labeling

arXiv: https://arxiv.org/abs/1908.08676 · HTML: https://mlsanity.com/paper/1908.08676/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2019.

## Built on by (most cited first, 25 of 25)

- [Template-Based Named Entity Recognition Using BART](https://mlsanity.com/paper/2106.01760/) (2021)
- [LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting](https://mlsanity.com/paper/2109.00720/) (2021)
- [MNER-QG: An End-to-End MRC framework for Multimodal Named Entity Recognition with Query Grounding](https://mlsanity.com/paper/2211.14739/) (2022)
- [Towards Joint Intent Detection and Slot Filling via Higher-order Attention](https://mlsanity.com/paper/2109.08890/) (2021)
- [An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling](https://mlsanity.com/paper/2110.13691/) (2021)
- [A Dynamic Graph Interactive Framework with Label-Semantic Injection for Spoken Language Understanding](https://mlsanity.com/paper/2211.04023/) (2022)
- [FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition](https://mlsanity.com/paper/2208.11464/) (2022)
- [TSAM: A Two-Stream Attention Model for Causal Emotion Entailment](https://mlsanity.com/paper/2203.00819/) (2022)
- [kNN-NER: Named Entity Recognition with Nearest Neighbor Search](https://mlsanity.com/paper/2203.17103/) (2022)
- [Investigating Non-local Features for Neural Constituency Parsing](https://mlsanity.com/paper/2109.12814/) (2021)
- [The Temporal Structure of Language Processing in the Human Brain Corresponds to The Layered Hierarchy of Deep Language Models](https://mlsanity.com/paper/2310.07106/) (2023)
- [A Fast Attention Network for Joint Intent Detection and Slot Filling on Edge Devices](https://mlsanity.com/paper/2205.07646/) (2022)
- [GNN-SL: Sequence Labeling Based on Nearest Examples via GNN](https://mlsanity.com/paper/2212.02017/) (2022)
- [Distantly supervised end-to-end medical entity extraction from electronic health records with human-level quality](https://mlsanity.com/paper/2201.10463/) (2022)
- [A Persian Benchmark for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/2303.00408/) (2023)
- [TC-GAT: Graph Attention Network for Temporal Causality Discovery](https://mlsanity.com/paper/2304.10706/) (2023)
- [TKDP: Threefold Knowledge-enriched Deep Prompt Tuning for Few-shot Named Entity Recognition](https://mlsanity.com/paper/2306.03974/) (2023)
- [Boosting Span-based Joint Entity and Relation Extraction via Squence Tagging Mechanism](https://mlsanity.com/paper/2105.10080/) (2021)
- [Unifying Token and Span Level Supervisions for Few-Shot Sequence Labeling](https://mlsanity.com/paper/2307.07946/) (2023)
- [HAN: Higher-order Attention Network for Spoken Language Understanding](https://mlsanity.com/paper/2108.11916/) (2021)
- [Responsive parallelized architecture for deploying deep learning models in production environments](https://mlsanity.com/paper/2112.08933/) (2021)
- [Yunshan Cup 2020: Overview of the Part-of-Speech Tagging Task for Low-resourced Languages](https://mlsanity.com/paper/2204.02658/) (2022)
- [Multi-grained Label Refinement Network with Dependency Structures for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/2209.04156/) (2022)
- [A Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism for Emotion-Cause Pair Extraction](https://mlsanity.com/paper/2209.07972/) (2022)
- [GWPT: A Green Word-Embedding-based POS Tagger](https://mlsanity.com/paper/2401.07475/) (2024)

## Abstract (arXiv)

CRF has been used as a powerful model for statistical sequence labeling. For neural sequence labeling, however, BiLSTM-CRF does not always lead to better results compared with BiLSTM-softmax local classification. This can be because the simple Markov label transition model of CRF does not give much information gain over strong neural encoding. For better representing label sequences, we investigate a hierarchically-refined label attention network, which explicitly leverages label embeddings and captures potential long-term label dependency by giving each word incrementally refined label distributions with hierarchical attention. Results on POS tagging, NER and CCG supertagging show that the proposed model not only improves the overall tagging accuracy with similar number of parameters, but also significantly speeds up the training and testing compared to BiLSTM-CRF.