---
title: "Recognizing Implicit Discourse Relations via — Liu 2016"
description: "Implicit discourse relations, such as contrast or causality between two text spans (arguments) that lack an overt connective like \"because\", are hard to…"
authors: ["Yang Liu", "Sujian Li"]
year: 2016
venue: "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D16-1130/"
doi: "10.18653/v1/d16-1130"
arxiv: "1609.06380"
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/liu-2016-recognizing-implicit-discourse-relations-repeated/"
---
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# Recognizing Implicit Discourse Relations via Repeated Reading: Neural Networks with Multi-Level Attention

**Authors:** [Yang Liu](https://md.research.elamur.ai/free/en/authors/yang-liu/), [Sujian Li](https://md.research.elamur.ai/free/en/authors/sujian-li/) · **Year:** 2016 · **Venue:** Proceedings of the 2016 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/D16-1130/) · [PDF](https://aclanthology.org/D16-1130.pdf) · [DOI 10.18653/v1/d16-1130](https://doi.org/10.18653/v1/d16-1130) · [arXiv 1609.06380](https://arxiv.org/abs/1609.06380) · [OpenAlex](https://openalex.org/W2962998183)

**Citations:** 85 citations (openalex-snapshot · 2026-10-14) · [43](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Implicit discourse relations, such as contrast or causality between two text spans (arguments) that lack an overt connective like "because", are hard to classify automatically. Liu and Li argue that earlier feature-based and neural classifiers effectively scan the argument pair only once, while people facing difficult text reread it with sharper focus each time. They model this behaviour with NNMA (Neural Networks with Multi-Level Attention). A general level encodes each argument with a bi-LSTM over GloVe embeddings and mean-pools the outputs. Several stacked attention levels then reweight the words of both arguments, guided by an external short-term memory vector that is refreshed recurrently from the previous level. The top level's output feeds a softmax classifier. On the PDTB, using the four top-level relation classes, the authors report state-of-the-art results. Attention visualizations show the model homing in on relation-bearing words over successive passes. The work is an early demonstration that memory-guided, multi-pass attention benefits discourse parsing.

## Key points
- Task and data: implicit relation classification on the PDTB, using the top-level senses Temporal, Contingency, Comparison and Expansion.
- General level: two separate bi-LSTMs (one for Arg-1, one for Arg-2) initialized with pre-trained GloVe vectors, followed by mean pooling to obtain global argument vectors.
- First memory: built by a nonlinear transform of the two argument vectors and their difference. At each later level, the memory also takes the previous memory as input and drives softmax attention for attention-weighted pooling of the bi-LSTM states.
- Final prediction: a softmax over the top-level argument representations and their difference. The design extends to any number of attention levels.
- Analysis: per-level attention heatmaps for Comparison, Contingency and Expansion examples, plus KL-divergences between levels, show attention becoming progressively more focused. Exact scores fall in the portion of the paper missing from the extracted text and are not reproduced here.

*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

43 papers of our library cite it (of the 140673 whose bibliographies we read); 30 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/)
- [TransS-Driven Joint Learning Architecture for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/he-2020-transs-driven-joint-learning-architecture/)
- [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/)
- [Global and Local Hierarchy-aware Contrastive Framework for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/jiang-2023-global-local-hierarchy-aware-contrastive/)
- [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/)
- [Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification](https://md.research.elamur.ai/free/en/papers/lan-2017-multi-task-attention-based-neural/)
- [Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation](https://md.research.elamur.ai/free/en/papers/liu-2023-annotation-inspired-implicit-discourse-relation/)
- [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/)
- [Automatic Prediction of Discourse Connectives](https://md.research.elamur.ai/free/en/papers/malmi-2018-automatic-prediction-discourse-connectives/)
- [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/)
- [Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification](https://md.research.elamur.ai/free/en/papers/qin-2017-adversarial-connective-exploiting-networks-implicit/)
- [A Recurrent Neural Model with Attention for the Recognition of Chinese Implicit Discourse Relations](https://md.research.elamur.ai/free/en/papers/ronnqvist-2017-recurrent-neural-model-attention-recognition/)
- [Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/ruan-2020-interactively-propagative-attention-learning-implicit/)
- [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/)
- [Discourse Relation Prediction: Revisiting Word Pairs with Convolutional Networks](https://md.research.elamur.ai/free/en/papers/varia-2019-discourse-relation-prediction-revisiting-word/)
- [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/)
- [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/)
- [TEPrompt: Task Enlightenment Prompt Learning for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/xiang-2023-teprompt-task-enlightenment-prompt-learning/)
- [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/)
- [Revisiting Shallow Discourse Parsing in the PDTB-3: Handling Intra-sentential Implicits](https://md.research.elamur.ai/free/en/papers/zhao-2021-revisiting-shallow-discourse-parsing-pdtb/)
- [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{liu2016recognizing,
  title = {{Recognizing Implicit Discourse Relations via Repeated Reading: Neural Networks with Multi-Level Attention}},
  author = {Liu, Yang and Li, Sujian},
  booktitle = {Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing},
  year = {2016},
  doi = {10.18653/v1/d16-1130},
  eprint = {1609.06380},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/d16-1130}
}
```

- [← previous Reasoning about Pragmatics with Neural Listeners and Speakers](https://md.research.elamur.ai/free/en/papers/andreas-2016-reasoning-about-pragmatics-neural-listeners/)
- [next → Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/wang-2016-recursive-neural-conditional-random-fields/)
- [↑ back to papers of 2016](https://md.research.elamur.ai/free/en/papers/2016/page/3/)

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