---
title: "Two are Better than One — Wang 2020"
description: "Wang and Lu instead build two cooperating encoders: a sequence encoder for NER and a table encoder for RE. In each… 62 references, cited by 70 papers here."
authors: ["Jue Wang", "Wei Lu"]
year: 2020
venue: "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)"
source: "https://aclanthology.org/2020.emnlp-main.133/"
doi: "10.18653/v1/2020.emnlp-main.133"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wang-2020-two-are-better-than-one/"
---
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# Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders

**Authors:** [Jue Wang](https://md.research.elamur.ai/free/en/authors/jue-wang/) and [Wei Lu](https://md.research.elamur.ai/free/en/authors/wei-lu/) · **Year:** 2020 · **Venue:** Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.emnlp-main.133/) · [PDF](https://aclanthology.org/2020.emnlp-main.133.pdf) · [DOI 10.18653/v1/2020.emnlp-main.133](https://doi.org/10.18653/v1/2020.emnlp-main.133) · [OpenAlex](https://openalex.org/W3105063288)

**Citations:** 216 citations (OpenAlex, 2026-10-14) · [70](https://md.research.elamur.ai/free/en/papers/wang-2020-two-are-better-than-one/cited-by/) in our library, of the 140,884 papers whose bibliographies we read

## Summary
Joint extraction of named entities and relations is often framed as table filling. An N×N grid is built over word pairs: diagonal cells hold BIO entity tags, and off-diagonal cells hold directed relation labels. Earlier joint systems usually relied on one shared representation for both subtasks, which can blur the features each task needs. Many also flattened the table into a sequence and lost its 2D structure.

Wang and Lu instead build two cooperating encoders: a sequence encoder for NER and a table encoder for RE. In each stacked layer, the table is formed from pairs of sequence vectors. A multi-dimensional GRU-based RNN then contextualizes it across layer, row and column. The sequence encoder updates itself through table-guided attention. The authors also inject BERT-family self-attention weights into the table as signals of word-word interaction, which they describe as a first.

The model achieves state-of-the-art F1 for both tasks on ACE04, ACE05, CoNLL04 and ADE. This shows that separate but interacting task-specific representations are beneficial.

## Key points
- **Architecture:** Multiple layers alternate between building the table from the sequence representation and refining the sequence via table-guided attention. Input embeddings combine word vectors, character-level LSTM vectors, and contextualized vectors from models such as BERT or ALBERT.
- **MD-RNN ablation on ACE05:** Results are reported as two scores per setting, presumably NER and RE F1.
  - The default bidirectional MD-RNN scores 89.5 / 67.6.
  - The four-direction variant gives 89.7 / 67.6.
  - The unidirectional variant drops RE to 66.9.
  - Keeping only the layer dimension yields 63.9 on RE.
  - CNN-based table encoders performed worse than RNN-based ones.
- **Attention visualization:** Using bertviz, the authors compare table-guided attention heads with ALBERT heads. The table-guided heads capture entity and relation patterns that ALBERT misses, and do so with higher confidence.
- **Probing intermediate layers:** The trained prediction layer is applied to intermediate layers on ACE05 examples. Early-layer errors are progressively corrected in deeper layers.
- **Code:** Released at github.com/LorrinWWW/two-are-better-than-one.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

Topics: [🔎 Information extraction](https://md.research.elamur.ai/free/en/topics/information-extraction/)

## Institutions

- [Zhejiang University](https://md.research.elamur.ai/free/en/universities/zhejiang-university/) — University, Hangzhou, China · 33 works · 10,088 citations
- [Singapore University of Technology and Design](https://md.research.elamur.ai/free/en/universities/singapore-university-of-technology-and-design/) — University, Singapore, Singapore · 20 works · 8,193 citations

## Cite

Cite as: Jue Wang and Wei Lu. 2020. Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wang2020two,
  title = {{Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders}},
  author = {Jue Wang and Wei Lu},
  booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2020},
  doi = {10.18653/v1/2020.emnlp-main.133},
  url = {https://doi.org/10.18653/v1/2020.emnlp-main.133}
}
```

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