---
title: "IIIDYT at SemEval-2018 Task 3: Irony detection in English tweets"
description: "The paper describes the IIIDYT entry to SemEval-2018 Task 3, subtask A. The task is a binary decision on whether an English… 25 references."
authors: ["Edison Marrese-Taylor", "Suzana Ilic", "Jorge Balazs", "Helmut Prendinger", "Yutaka Matsuo"]
year: 2018
venue: "Proceedings of the 12th International Workshop on Semantic Evaluation"
source: "https://aclanthology.org/S18-1087/"
doi: "10.18653/v1/s18-1087"
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/taylor-2018-iiidyt-semeval-2018-task-3/"
---
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# IIIDYT at SemEval-2018 Task 3: Irony detection in English tweets

**Authors:** [Edison Marrese-Taylor](https://md.research.elamur.ai/free/en/authors/edison-marrese-taylor/), [Suzana Ilic](https://md.research.elamur.ai/free/en/authors/suzana-ilic/), [Jorge Balazs](https://md.research.elamur.ai/free/en/authors/jorge-balazs/), [Helmut Prendinger](https://md.research.elamur.ai/free/en/authors/helmut-prendinger/) and [Yutaka Matsuo](https://md.research.elamur.ai/free/en/authors/yutaka-matsuo/) · **Year:** 2018 · **Venue:** [Proceedings of the 12th International Workshop on Semantic Evaluation](https://md.research.elamur.ai/free/en/venues/semeval/) ([SemEval 2018](https://md.research.elamur.ai/free/en/venues/semeval/2018/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/S18-1087/) · [PDF](https://aclanthology.org/S18-1087.pdf) · [DOI 10.18653/v1/s18-1087](https://doi.org/10.18653/v1/s18-1087) · [OpenAlex](https://openalex.org/W2798547941)

**Citations:** 3 citations (OpenAlex, 2026-10-14)

## Summary
The paper describes the IIIDYT entry to SemEval-2018 Task 3, subtask A. The task is a binary decision on whether an English tweet is ironic. The authors avoided heavy hand-crafted linguistic or contextual features and used representation learning instead. Their model is a multi-layer bidirectional LSTM whose last hidden state encodes the tweet, and it is initialised with GloVe embeddings trained on Twitter. They added a few simple binary cues about casing, digits and repetition, and they gave frequent unknown words vectors sampled near the region of rare GloVe words. The data was a balanced set of about 4.8k tweets originally collected through irony-related hashtags. On their own validation split, an ensemble of four such networks beat the official TF-IDF SVM baseline. On the official test set, however, performance collapsed, and the system finished near the bottom of the ranking. The authors read this as a sign that a generic architecture trained on so little data does not generalise. They argue that irony detection needs task-specific design, large-scale pre-training or transfer learning, and possibly larger noisy self-labelled corpora.

## Key points
- The dataset has 2,396 ironic and 2,396 non-ironic tweets. The ironic tweets break down into 1,728 verbal irony by polarity contrast, 267 other verbal irony and 401 situational irony.
- Pre-processing used the NLTK TweetTokenizer. Usernames, URLs and trigger words such as *not*, *sarcasm* and *irony* were removed, while case was kept and no stemming was applied.
- The best configuration used 100-dimensional GloVe vectors, 150 hidden units and dropout 0.1. It was trained with Adam at learning rate 0.0001 with early stopping on an 80/20 split, and the final system was an ensemble of four random seeds.
- On validation the model reached F1 0.7262 against the baseline's 0.6263. On the test set its F1 dropped to 0.2905, with accuracy 0.3520, among 43 teams.
- In the ablation, sentence-level binary features helped slightly (F1 0.6848 with them versus 0.6820 without). Token-level features hurt (0.6843 with them versus 0.7008 without).

## Interesting ideas
- Frequent words missing from the pre-trained vocabulary get a vector drawn at random from a sphere centred on the centroid of the rarest 10% of known words, so they land in a plausible rare-word region instead of sharing one UNK vector.
- The hashtags and words that were used to collect the ironic tweets are deleted before training, so the model cannot simply learn the collection label.
- Casing is deliberately preserved because all-caps words or spans can signal an ironic or exaggerated tone.
- Features matching the granularity of the decision (whole-sentence cues) helped, while finer per-token cues added noise.
- Situational irony is separated from verbal irony because it rests on a scenario contradicting expectations rather than on surface wording.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (tokens): The tagger could initialise representations of unseen but recurring word forms near the profile of known rare words, rather than collapsing them all into one unknown class.
- **I2** (learn): Mova's silver-data pipeline could strip whatever cue produced a label before training, and treat the stripped version as a negative control against shortcut learning.
- **I3** (frames): The prag irony module could take emphatic casing from the token layer as one explainable cue, alongside polarity contrast between the frame and its context.
- **I4** (frames): Irony and speech-act decisions could be driven by utterance-level properties, such as a contrast between evaluation and situation, rather than by accumulating word-level hints.
- **I5** (world): Such cases could be flagged when the world model's expected outcome of an event chain conflicts with the narrated outcome, for example winning the lottery and then dying the next day.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [The University of Tokyo](https://md.research.elamur.ai/free/en/universities/the-university-of-tokyo/) — University, Tokyo, Japan · 45 works · 8,692 citations
- [National Institute of Informatics](https://md.research.elamur.ai/free/en/labs/national-institute-of-informatics/) — Research institute, Tokyo, Japan · 21 works · 3,045 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 9,086 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 8,889 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 9,296 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 9,206 works

🧭 Topics (2): [English](https://md.research.elamur.ai/free/en/topics/english/) · 1,373 works · [😊 Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · 608 works

## Cite

Cite as: Edison Marrese-Taylor, Suzana Ilic, Jorge Balazs, Helmut Prendinger and Yutaka Matsuo. 2018. IIIDYT at SemEval-2018 Task 3: Irony detection in English tweets. Proceedings of the 12th International Workshop on Semantic Evaluation.

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

```bibtex
@inproceedings{marresetaylor2018iiidyt,
  title = {{IIIDYT at SemEval-2018 Task 3: Irony detection in English tweets}},
  author = {Edison Marrese-Taylor, Suzana Ilic, Jorge Balazs, Helmut Prendinger and Yutaka Matsuo},
  booktitle = {Proceedings of the 12th International Workshop on Semantic Evaluation},
  year = {2018},
  doi = {10.18653/v1/s18-1087},
  url = {https://doi.org/10.18653/v1/s18-1087}
}
```

- [← previous HashCount at SemEval-2018 Task 3: Concatenative Featurization of Tweet and Hashtags for Irony Detection](https://md.research.elamur.ai/free/en/papers/cho-2018-hashcount-semeval-2018-task-3/)
- [next → INAOE-UPV at SemEval-2018 Task 3: An Ensemble Approach for Irony Detection in Twitter](https://md.research.elamur.ai/free/en/papers/farias-2018-inaoe-upv-semeval-2018-task/)
- [↑ back to papers of 2018](https://md.research.elamur.ai/free/en/papers/2018/page/6/)

## References

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*Updated 24 October 2026*