--- title: "Shallow Convolutional Neural Network for — Zhang 2015" description: "Implicit discourse relations are hard to classify because no explicit connective such as \"because\" or \"but\" signals the link between two text spans." authors: ["Biao Zhang", "Jinsong Su", "Deyi Xiong", "Yaojie Lu", "Hong Duan", "Junfeng Yao"] year: 2015 venue: "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing" source: "https://aclanthology.org/D15-1266/" doi: "10.18653/v1/d15-1266" licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)" licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/zhang-2015-shallow-convolutional-neural-network-implicit/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zhang-2015-shallow-convolutional-neural-network-implicit/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2015](https://md.research.elamur.ai/free/en/papers/2015/page/2/) / Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition # Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition **Authors:** [Biao Zhang](https://md.research.elamur.ai/free/en/authors/biao-zhang/), [Jinsong Su](https://md.research.elamur.ai/free/en/authors/jinsong-su/), [Deyi Xiong](https://md.research.elamur.ai/free/en/authors/deyi-xiong/), [Yaojie Lu](https://md.research.elamur.ai/free/en/authors/yaojie-lu/), [Hong Duan](https://md.research.elamur.ai/free/en/authors/hong-duan/), Junfeng Yao · **Year:** 2015 · **Venue:** Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/) **Links:** [Publisher's page](https://aclanthology.org/D15-1266/) · [PDF](https://aclanthology.org/D15-1266.pdf) · [DOI 10.18653/v1/d15-1266](https://doi.org/10.18653/v1/d15-1266) · [OpenAlex](https://openalex.org/W2251826083) **Citations:** 112 citations (openalex-snapshot · 2026-10-14) · [49](#cited-by) in our library, of the 140884 papers whose bibliographies we read ## Summary Implicit discourse relations are hard to classify because no explicit connective such as "because" or "but" signals the link between two text spans. Zhang et al. argue that deep networks are a poor fit for this task because PDTB training data is small and the relation classes are skewed, so deep models tend to overfit. They propose a Shallow Convolutional Neural Network (SCNN) with a single hidden layer. For each argument, SCNN pools Word2Vec-initialised word vectors with average, min and max operations. It then joins the features from both arguments, applies tanh and length normalisation, and feeds the result to a softmax classifier. The model is tested on PDTB 2.0 as four separate one-vs-rest binary tasks over the top-level senses. SCNN beats SVM, TSVM, Brown-cluster-enhanced SVMs and a recursive autoencoder on three of the four relations, mostly because its recall is higher. The work shows that a very simple neural model can match or surpass heavily feature-engineered systems when training data is scarce. ## Key points - **Data split:** The authors use PDTB 2.0, which covers 2,312 Wall Street Journal articles. Sections 2–20 are for training, 0–1 for development and 21–22 for testing. Training sets were balanced by sampling equal numbers of positive and negative instances. - **Setup:** Embeddings have d=128 and were pre-trained with Word2Vec on 1.02M unlabeled sentences (33.5M words). The parameters were tuned with L-BFGS using λ=1e-4. - **F1 scores:** SCNN reached 52.04 for CONTINGENCY, 69.59 for EXPANSION and 30.54 for TEMPORAL, which were the best results reported. For COMPARISON it scored 33.22, slightly behind No-Cro at 34.22. - **Effect of normalisation:** Length normalisation clearly helped. Measured against the SVM baseline, SCNN gained 0.71, 2.17, 6.52 and 4.93 F1 points on the four relations, and on most of them it also beat SCNN-No-Norm. - **Other baselines:** The RAE and unlabeled-data TSVM baselines mostly underperformed. Brown cluster pair features helped only for COMPARISON and CONTINGENCY. *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 49 papers of our library cite it (of the 140884 whose bibliographies we read); 31 of them have a page here, 18 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/) - [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/) - [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/) - [Zero-shot transfer for implicit discourse relation classification](https://md.research.elamur.ai/free/en/papers/kurfal-2019-zero-shot-transfer-implicit-discourse/) - [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/) - [Recognizing Implicit Discourse Relations via Repeated Reading: Neural Networks with Multi-Level Attention](https://md.research.elamur.ai/free/en/papers/liu-2016-recognizing-implicit-discourse-relations-repeated/) - [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/) - [Discourse Relation Sense Classification Using Cross-argument Semantic Similarity Based on Word Embeddings](https://md.research.elamur.ai/free/en/papers/mihaylov-2016-discourse-relation-sense-classification-cross/) - [Natural Language Inference by Tree-Based Convolution and Heuristic Matching](https://md.research.elamur.ai/free/en/papers/mou-2016-natural-language-inference-tree-based/) - [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/) - [Implicit Discourse Relation Recognition with Context-aware Character-enhanced Embeddings](https://md.research.elamur.ai/free/en/papers/qin-2016-implicit-discourse-relation-recognition-context/) - [A Stacking Gated Neural Architecture for Implicit Discourse Relation Classification](https://md.research.elamur.ai/free/en/papers/qin-2016-stacking-gated-neural-architecture-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/) - [On the Need of Cross Validation for Discourse Relation Classification](https://md.research.elamur.ai/free/en/papers/shi-2017-need-cross-validation-discourse-relation/) - [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/) - [Next Sentence Prediction helps Implicit Discourse Relation Classification within and across Domains](https://md.research.elamur.ai/free/en/papers/shi-2019-next-sentence-prediction-helps-implicit/) - [Two End-to-end Shallow Discourse Parsers for English and Chinese in CoNLL-2016 Shared Task](https://md.research.elamur.ai/free/en/papers/wang-2016-two-end-end-shallow-discourse/) - [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/) - [Bilingually-constrained Synthetic Data for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/wu-2016-bilingually-constrained-synthetic-data-implicit/) - [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/) - [Variational Neural Discourse Relation Recognizer](https://md.research.elamur.ai/free/en/papers/zhang-2016-variational-neural-discourse-relation-recognizer/) - [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/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{zhang2015shallow, title = {{Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition}}, author = {Zhang, Biao and Su, Jinsong and Xiong, Deyi and Lu, Yaojie and Duan, Hong and Yao, Junfeng}, booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing}, year = {2015}, doi = {10.18653/v1/d15-1266}, url = {https://doi.org/10.18653/v1/d15-1266} } ``` - [← previous Sequences of Verbal Forms and Taxis in Biblical Hebrew](https://md.research.elamur.ai/free/en/papers/hbo-2015-lenzi-sequences-verbal-forms-taxis-biblical/) - [next → Shallow Discourse Parsing Using Constituent Parsing Tree](https://md.research.elamur.ai/free/en/papers/chen-2015-shallow-discourse-parsing-constituent-parsing/) - [↑ back to papers of 2015](https://md.research.elamur.ai/free/en/papers/2015/page/2/) ## References 27 references, 14 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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