--- title: "Sarcasm Detection using Context Separators in — Dadu 2020" description: "This paper tackles automatic sarcasm detection in social media replies, where the intended meaning often depends on earlier turns of the conversation." authors: ["Tanvi Dadu", "Kartikey Pant"] year: 2020 venue: "Proceedings of the Second Workshop on Figurative Language Processing" source: "https://aclanthology.org/2020.figlang-1.6/" doi: "10.18653/v1/2020.figlang-1.6" 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/dadu-2020-sarcasm-detection-context-separators-online/" --- [Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/dadu-2020-sarcasm-detection-context-separators-online/) [home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2020](https://md.research.elamur.ai/free/papers/2020/page/4/) / Sarcasm Detection using Context Separators in Online Discourse # Sarcasm Detection using Context Separators in Online Discourse **Authors:** Tanvi Dadu, Kartikey Pant · **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-1.6/) · [PDF](https://aclanthology.org/2020.figlang-1.6.pdf) · [DOI 10.18653/v1/2020.figlang-1.6](https://doi.org/10.18653/v1/2020.figlang-1.6) · [OpenAlex](https://openalex.org/W3046075181) **Citations:** 27 citations (openalex-snapshot · 2026-10-14) · [10](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary This paper tackles automatic sarcasm detection in social media replies, where the intended meaning often depends on earlier turns of the conversation. Working with the two balanced datasets from the FigLang 2020 shared task (Twitter and Reddit threads), the authors fine-tune RoBERTa large and compare three input formats: the reply alone, the reply concatenated with preceding turns, and the same concatenation with a separation token placed between the context and the reply. The average number of context turns differs considerably between the training and test splits, so they keep only the two most recent dialogue turns as context. Adding context helps on both platforms. On Twitter, the separator makes almost no difference. On Reddit, it gives a clear gain, raising F1 by roughly 5.1% relative to plain concatenation. The study shows that a pretrained transformer can benefit from conversational context. It also shows that explicitly marking the boundary between that context and the reply can matter, depending on the platform. ## Key points - **Data:** The FigLang 2020 shared task provides 5,000 English tweets and 4,400 Reddit posts, each set with equal sarcastic and non-sarcastic counts. Each reply comes with an ordered list of earlier dialogue turns. - **Context mismatch between splits:** The average number of contexts per post varies by split. - Reddit: 2.491 (train) vs 4.254 (test). - Twitter: 3.867 (train) vs 3.164 (test). - Because of this mismatch, only the last two turns are used as context. - **Model and setup:** RoBERTa large (355M parameters, 50,265-token vocabulary), fine-tuned with these settings: - learning rate 1e-5; - 3 epochs; - 90/10 train-validation split; - maximum sequence length 50 for the reply alone and 256 for the context-based inputs. - **Twitter test F1:** - Response-only: 0.752 - Context-Response: 0.772 - Context-Response (Separated): 0.771 - **Reddit test F1:** - Response-only: 0.679 - Context-Response: 0.681 - Context-Response (Separated): 0.716, about a 5.13% improvement from the separator. *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/) Topics: [Discourse analysis](https://md.research.elamur.ai/free/topics/discourse-analysis/) ## Cited by Cited by 10 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 8 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/) - [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://md.research.elamur.ai/free/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{dadu2020sarcasm, title = {{Sarcasm Detection using Context Separators in Online Discourse}}, author = {Dadu, Tanvi and Pant, Kartikey}, booktitle = {Proceedings of the Second Workshop on Figurative Language Processing}, year = {2020}, doi = {10.18653/v1/2020.figlang-1.6}, url = {https://doi.org/10.18653/v1/2020.figlang-1.6} } ``` - [← previous RobertNLP at the IWPT 2020 Shared Task: Surprisingly Simple Enhanced UD Parsing for English](https://md.research.elamur.ai/free/papers/ud-2020-grunewald-robertnlp-iwpt-2020-shared-task/) - [next → Sarcasm Identification and Detection in Conversion Context using BERT](https://md.research.elamur.ai/free/papers/a-2020-sarcasm-identification-detection-conversion-context/) - [↑ back to papers of 2020](https://md.research.elamur.ai/free/papers/2020/page/4/) ## References 1 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. David Bamman and Noah A. Smith. 2015. Contextualized sarcasm detection on twitter. In ICWSM. [find](https://scholar.google.com/scholar?q=David+Bamman+and+Noah+A%2E+Smith%2E+2015%2E+Contextualized+sarcasm+detection+on+twitter%2E+In+ICWSM%2E)