---
title: "C-Net: Contextual Network for Sarcasm Detection — Jena 2020"
description: "The paper tackles sarcasm detection in online conversations, where a reply often reads as sarcastic only in light of the preceding dialogue."
authors: ["Amit Kumar Jena", "Aman Sinha", "Rohit Agarwal"]
year: 2020
venue: "Proceedings of the Second Workshop on Figurative Language Processing"
source: "https://aclanthology.org/2020.figlang-1.8/"
doi: "10.18653/v1/2020.figlang-1.8"
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/papers/jena-2020-c-net-contextual-network-sarcasm/"
---
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# C-Net: Contextual Network for Sarcasm Detection

**Authors:** Amit Kumar Jena, Aman Sinha, Rohit Agarwal · **Year:** 2020 · **Venue:** Proceedings of the Second Workshop on Figurative Language Processing · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.figlang-1.8/) · [PDF](https://aclanthology.org/2020.figlang-1.8.pdf) · [DOI 10.18653/v1/2020.figlang-1.8](https://doi.org/10.18653/v1/2020.figlang-1.8) · [OpenAlex](https://openalex.org/W3045790525)

**Citations:** 32 citations (openalex-snapshot · 2026-10-14) · [6](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper tackles sarcasm detection in online conversations, where a reply often reads as sarcastic only in light of the preceding dialogue. It was the authors' entry to the Sarcasm Detection shared task at the Second Workshop on Figurative Language Processing (ACL 2020). The task used balanced Twitter and Reddit datasets in which every response comes with an ordered history of earlier turns.

The authors first built response-only baselines. These included classical classifiers, RNN/Bi-LSTM, DeepMoji and ELMo features, and fine-tuned BERT, XLNet and RoBERTa. They then proposed C-Net. In C-Net, each context sentence inherits the response's label as a pseudo-label, and a separate BERT (base-uncased) model is trained per position. The per-model sarcasm probabilities are then merged in a fusion layer, using either logistic regression or Simple Exponential Smoothing (SES). SES assigns progressively smaller weights to turns further back in the conversation.

C-Net with SES reached the best Twitter score (F1 0.750). On Reddit, it scored 0.663, slightly behind response-only transformers. The results suggest that context can help, though not uniformly across platforms.

## Key points
- **Data:** Twitter has 5000 training and 1800 test examples, and Reddit has 4400 training and 1800 test examples. Context length per response varies from 2 to 20 turns.
- **Best configuration:** C-Net+SES uses three BERT models (response, latest context turn, second-latest turn). It scored F1 0.750 on Twitter and 0.663 on Reddit; C-Net+LR scored 0.747 and 0.650.
- **Response-only baselines:** BERT scored 0.733 on Twitter and 0.671 on Reddit, while RoBERTa was best on Reddit with 0.678.
- **Full-context attempt:** a Time-stamping variant used position markers on all context sentences. It performed worse, scoring 0.710 on Twitter and 0.500 on Reddit.
- **SES smoothing factor:** α was tuned by grid search, giving 0.395 for Twitter and 0.2 for Reddit. Training used fastai with gradual unfreezing and slanted triangular learning rates.

*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/shelves/linguistics/)

## Cited by

Cited by

6 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 4 not yet.

- [A Report on the 2020 Sarcasm Detection Shared Task](https://md.research.elamur.ai/free/papers/ghosh-2020-report-2020-sarcasm-detection-shared/)
- [Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network](https://md.research.elamur.ai/free/papers/liang-2022-multi-modal-sarcasm-detection-cross/)

## Cite

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

```bibtex
@inproceedings{jena2020c,
  title = {{C-Net: Contextual Network for Sarcasm Detection}},
  author = {Jena, Amit Kumar and Sinha, Aman and Agarwal, Rohit},
  booktitle = {Proceedings of the Second Workshop on Figurative Language Processing},
  year = {2020},
  doi = {10.18653/v1/2020.figlang-1.8},
  url = {https://doi.org/10.18653/v1/2020.figlang-1.8}
}
```

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## References

21 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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