---
title: "Multi-task Attention-based Neural Networks for — Lan 2017"
description: "The paper addresses implicit discourse relation recognition: deciding how two adjacent text spans (Arg-1 and Arg-2) are logically related, such as cause…"
authors: ["Man Lan", "Jianxiang Wang", "Yuanbin Wu", "Zheng-Yu Niu", "Haifeng Wang"]
year: 2017
venue: "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D17-1134/"
doi: "10.18653/v1/d17-1134"
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/lan-2017-multi-task-attention-based-neural/"
---
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# Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification

**Authors:** [Man Lan](https://md.research.elamur.ai/free/en/authors/man-lan/), [Jianxiang Wang](https://md.research.elamur.ai/free/en/authors/jianxiang-wang/), [Yuanbin Wu](https://md.research.elamur.ai/free/en/authors/yuanbin-wu/), [Zheng-Yu Niu](https://md.research.elamur.ai/free/en/authors/zheng-yu-niu/), [Haifeng Wang](https://md.research.elamur.ai/free/en/authors/haifeng-wang/) · **Year:** 2017 · **Venue:** Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/D17-1134/) · [PDF](https://aclanthology.org/D17-1134.pdf) · [DOI 10.18653/v1/d17-1134](https://doi.org/10.18653/v1/d17-1134) · [OpenAlex](https://openalex.org/W2760057941)

**Citations:** 103 citations (openalex-snapshot · 2026-10-14) · [39](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper addresses implicit discourse relation recognition: deciding how two adjacent text spans (Arg-1 and Arg-2) are logically related, such as cause or contrast, when no explicit connective links them. The authors argue that simply concatenating independently encoded arguments misses cross-argument cues. They also note that labeled training data is scarce. Their model combines two ideas. First, Bi-LSTM encoders feed an attention mechanism in which each argument's summary vector weights the words of the other argument, so each pair representation depends on both spans. Second, this pair encoder is shared in a multi-task setup that pairs the main task with an auxiliary task drawing on unannotated text. Knowledge passes between tasks through equal loss sharing, weighted loss sharing, or a sigmoid-gated interaction layer. The model is evaluated on PDTB 2.0 and the CoNLL-2016 shared task data. It reaches state-of-the-art or competitive scores on most settings, which suggests that combining argument-level and task-level interaction helps a low-resource discourse task.

## Key points
- The architecture uses word embeddings and a Bi-LSTM, whose summed hidden states are re-weighted by cross-argument attention. A softmax classifier is trained with cross-entropy loss.
- The multi-task variants are Equal Share, Weighted Share (auxiliary loss scaled by w ∈ (0, 1]) and Sigmoid (Gated) Interaction. Small values of w worked best, because they limit noise from the auxiliary task.
- On PDTB binary classification, the reported F1 scores are 40.73 for Comparison, 58.96 for Contingency, 72.47 for Expansion, 81.36 for Expansion+EntRel and 38.50 for Temporal.
- On PDTB 4-way classification, the model reaches F1 47.80 with accuracy 57.39. Accuracy is slightly below the best compared system (57.57).
- On CoNLL-2016, the model scores 39.40 and 40.12, most likely on the test and blind test sets. It beats Wang and Lan (2016) on the second set but not the first. The model also outperforms Liu et al. (2016b) and the authors' earlier traditional multi-task system, Lan et al. (2013).

*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: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Discourse analysis](https://md.research.elamur.ai/free/en/topics/discourse-analysis/)

## Cited by

Cited by

39 papers of our library cite it (of the 140673 whose bibliographies we read); 26 of them have a page here, 13 not yet.

- [Deep Enhanced Representation for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/bai-2018-deep-enhanced-representation-implicit-discourse/)
- [DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/chan-2023-discoprompt-path-prediction-prompt-tuning/)
- [Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph](https://md.research.elamur.ai/free/en/papers/dai-2018-improving-implicit-discourse-relation-classification/)
- [A Regularization Approach for Incorporating Event Knowledge and Coreference Relations into Neural Discourse Parsing](https://md.research.elamur.ai/free/en/papers/dai-2019-regularization-approach-incorporating-event-knowledge/)
- [CVAE-based Re-anchoring for Implicit Discourse Relation Classification](https://md.research.elamur.ai/free/en/papers/dou-2021-cvae-based-re-anchoring-implicit/)
- [Implicit Discourse Relation Recognition using Neural Tensor Network with Interactive Attention and Sparse Learning](https://md.research.elamur.ai/free/en/papers/guo-2018-implicit-discourse-relation-recognition-neural/)
- [Not Just Classification: Recognizing Implicit Discourse Relation on Joint Modeling of Classification and Generation](https://md.research.elamur.ai/free/en/papers/jiang-2021-not-just-classification-recognizing-implicit/)
- [Implicit Discourse Relation Classification: We Need to Talk about Evaluation](https://md.research.elamur.ai/free/en/papers/kim-2020-implicit-discourse-relation-classification-we/)
- [Contextualized Embeddings for Connective Disambiguation in Shallow Discourse Parsing](https://md.research.elamur.ai/free/en/papers/knaebel-2020-contextualized-embeddings-connective-disambiguation-shallow/)
- [Zero-shot transfer for implicit discourse relation classification](https://md.research.elamur.ai/free/en/papers/kurfal-2019-zero-shot-transfer-implicit-discourse/)
- [Facilitating Contrastive Learning of Discourse Relational Senses by Exploiting the Hierarchy of Sense Relations](https://md.research.elamur.ai/free/en/papers/long-2022-facilitating-contrastive-learning-discourse-relational/)
- [Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language](https://md.research.elamur.ai/free/en/papers/mahdaouy-2021-deep-multi-task-model-sarcasm/)
- [Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings](https://md.research.elamur.ai/free/en/papers/nguyen-2019-employing-correspondence-relations-connectives-identify/)
- [Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/ruan-2020-interactively-propagative-attention-learning-implicit/)
- [Comparison of methods for explicit discourse connective identification across various domains](https://md.research.elamur.ai/free/en/papers/scholman-2021-comparison-methods-explicit-discourse-connective/)
- [DiscoGeM: A Crowdsourced Corpus of Genre-Mixed Implicit Discourse Relations](https://md.research.elamur.ai/free/en/papers/scholman-2022-discogem-crowdsourced-corpus-genre-mixed/)
- [Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification](https://md.research.elamur.ai/free/en/papers/shi-2019-learning-explicitate-connectives-seq2seq-network/)
- [Prompt-based Logical Semantics Enhancement for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/wang-2023-prompt-based-logical-semantics-enhancement/)
- [Connective Prediction for Implicit Discourse Relation Recognition via Knowledge Distillation](https://md.research.elamur.ai/free/en/papers/wu-2023-connective-prediction-implicit-discourse-relation/)
- [ConnPrompt: Connective-cloze Prompt Learning for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/xiang-2022-connprompt-connective-cloze-prompt-learning/)
- [Encoding and Fusing Semantic Connection and Linguistic Evidence for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/xiang-2022-encoding-fusing-semantic-connection-linguistic/)
- [Using active learning to expand training data for implicit discourse relation recognition](https://md.research.elamur.ai/free/en/papers/xu-2018-active-learning-expand-training-data/)
- [Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese](https://md.research.elamur.ai/free/en/papers/xu-2019-topic-tensor-network-implicit-discourse/)
- [Context Tracking Network: Graph-based Context Modeling for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/zhang-2021-context-tracking-network-graph-based/)
- [Infusing Hierarchical Guidance into Prompt Tuning: A Parameter-Efficient Framework for Multi-level Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/zhao-2023-infusing-hierarchical-guidance-into-prompt/)
- [Prompt-based Connective Prediction Method for Fine-grained Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/zhou-2022-prompt-based-connective-prediction-method/)

## Cite

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

```bibtex
@inproceedings{lan2017multi,
  title = {{Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification}},
  author = {Lan, Man and Wang, Jianxiang and Wu, Yuanbin and Niu, Zheng-Yu and Wang, Haifeng},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  year = {2017},
  doi = {10.18653/v1/d17-1134},
  url = {https://doi.org/10.18653/v1/d17-1134}
}
```

- [← previous Multi-source annotation projection of coreference chains: assessing strategies and testing opportunities](https://md.research.elamur.ai/free/en/papers/grishina-2017-multi-source-annotation-projection-coreference/)
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- [↑ back to papers of 2017](https://md.research.elamur.ai/free/en/papers/2017/page/3/)

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