---
title: "Synthetic Examples Improve Cross-Target — Conforti 2021"
description: "Stance detection (SD) models often fail on targets they never saw in training."
authors: ["Costanza Conforti", "Jakob Berndt", "Mohammad Taher Pilehvar", "Chryssi Giannitsarou", "Flavio Toxvaerd", "Nigel Collier"]
year: 2021
venue: "Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis"
source: "https://aclanthology.org/2021.wassa-1.19/"
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/conforti-2021-synthetic-examples-improve-cross-target/"
---
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# Synthetic Examples Improve Cross-Target Generalization: A Study on Stance Detection on a Twitter corpus.

**Authors:** [Costanza Conforti](https://md.research.elamur.ai/free/authors/costanza-conforti/), [Jakob Berndt](https://md.research.elamur.ai/free/authors/jakob-berndt/), [Mohammad Taher Pilehvar](https://md.research.elamur.ai/free/authors/mohammad-taher-pilehvar/), [Chryssi Giannitsarou](https://md.research.elamur.ai/free/authors/chryssi-giannitsarou/), [Flavio Toxvaerd](https://md.research.elamur.ai/free/authors/flavio-toxvaerd/), [Nigel Collier](https://md.research.elamur.ai/free/authors/nigel-collier/) · **Year:** 2021 · **Venue:** Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.wassa-1.19/) · [PDF](https://aclanthology.org/2021.wassa-1.19.pdf) · [OpenAlex](https://openalex.org/W3156268002)

**Citations:** 7 citations (openalex-snapshot · 2026-10-14) · [5](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary

Stance detection (SD) models often fail on targets they never saw in training. Conforti et al. found this with the WT–WT corpus of tweets about US mergers and acquisitions (M&A): a model trained on healthcare mergers performs much worse on mergers in other industries. Because manual annotation is costly, the authors use a simple weakly supervised pipeline. First, they train a classifier on gold out-of-domain data. Then they use it to label a large collection of unannotated tweets about other mergers. Finally, they retrain on the gold data together with these "silver" examples, giving the synthetic examples lower weight. The classifier is an MLP over TF-IDF features. The authors also add a newly annotated defense-industry merger to WT–WT and release it. With synthetic data, macro-F1 on the two unseen-industry test sets rises by up to +3.4 and +5.1 points. The in-domain test set does not improve. The approach is cheap and model-agnostic, and it shows that unlabeled tweets can partly replace new annotation when an SD system moves to a new target.

## Key points

- Training data comes from three healthcare mergers in WT–WT (AET_HUM, ANTM_CI, CVS_AET; 30,367 tweets). The test sets are CI_ESRX (healthcare, in-domain), DIS_FOXA (entertainment) and the new UTX_COL (defense). Labels are support, refute, comment and unrelated.
- The authors crawled 134,922 unlabeled tweets about 16 additional mergers and labeled them with an MLP. Its inputs are the TF-IDF vectors of the tweet and target plus their cosine similarity. This labeler scored 78.33 F1 on held-out data.
- Synthetic samples are added at 50% of the training set size. They are weighted 0.6 for support, refute and comment and 0.2 for unrelated, which the authors judged to be noisier.
- Adding silver data only from industry-related mergers worked best. The authors conclude that the gain comes mainly from domain adaptation rather than regularization. The unrelated class benefited most, with up to +39.7 accuracy on DIS_FOXA.
- Recall gains level off at about 30% synthetic data. The choice of which stance classes to add made little difference.

*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

5 papers of our library cite it (of the 140673 whose bibliographies we read); 1 of them have a page here, 4 not yet.

- [A Challenge Dataset and Effective Models for Conversational Stance Detection](https://md.research.elamur.ai/free/papers/niu-2024-challenge-dataset-effective-models-conversational/)

## Cite

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

```bibtex
@inproceedings{conforti2021synthetic,
  title = {{Synthetic Examples Improve Cross-Target Generalization: A Study on Stance Detection on a Twitter corpus.}},
  author = {Conforti, Costanza and Berndt, Jakob and Pilehvar, Mohammad Taher and Giannitsarou, Chryssi and Toxvaerd, Flavio and Collier, Nigel},
  booktitle = {Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis},
  year = {2021},
  url = {https://aclanthology.org/2021.wassa-1.19/}
}
```

- [← previous Syntax-augmented Multilingual BERT for Cross-lingual Transfer](https://md.research.elamur.ai/free/papers/ahmad-2021-syntax-augmented-multilingual-bert-cross/)
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- [↑ back to papers of 2021](https://md.research.elamur.ai/free/papers/2021/page/4/)

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34. (health): TMUS S , VIAB CBS , BMY CELG , MSFT LNKD , MDT COV , IBM RHT • DIS FOXA (entertainment): TMUS S , CMCSA TWC , MSFT LNKD , IBM RHT , CHTR TWC , MDT COV , BMY CELG , VIAB CBS , Andreas Vlachos and Sebastian Riedel. 2014. Fact checking: Task definition and dataset construction. In Proceedings… [find](https://scholar.google.com/scholar?q=%28health%29%3A+TMUS+S+%2C+VIAB+CBS+%2C+BMY+CELG+%2C+MSFT+LNKD+%2C+MDT+COV+%2C+IBM+RHT+%E2%80%A2+DIS+FOXA+%28entertainment%29%3A+TMUS+S+%2C+CMCSA+TWC+%2C+MSFT+LNKD+%2C+IBM+RHT+%2C+CHTR+TWC+%2C+MDT+COV+%2C+BMY+CELG+%2C+VIAB+CBS+%2C+Andreas+Vlachos) [our card](https://md.research.elamur.ai/free/papers/vlachos-2014-fact-checking-task-definition-dataset/)
35. DELL EMC • UTX COL (defense): TMUS S , MDT COV , CHTR TWC , MSFT LNKD , VIAB CBS , BMY CELG , CMCSA TWC , CTL LVLT Arkaitz Zubiaga, Ahmet Aker, Kalina Bontcheva, Maria Liakata, and Rob Procter. 2018a. Detection and resolution of rumours in social media: A survey. ACM Comput. Surv., 51(2):32:1–32:36. [find](https://scholar.google.com/scholar?q=DELL+EMC+%E2%80%A2+UTX+COL+%28defense%29%3A+TMUS+S+%2C+MDT+COV+%2C+CHTR+TWC+%2C+MSFT+LNKD+%2C+VIAB+CBS+%2C+BMY+CELG+%2C+CMCSA+TWC+%2C+CTL+LVLT+Arkaitz+Zubiaga%2C+Ahmet+Aker%2C+Kalina+Bontcheva%2C+Maria+Liakata%2C+and+Rob+Procter%2E+2018a%2E)
36. Preprocessing. We perform the following steps on all tweets: lowercasing, tokenization; digits/URL normalization; stripping of the \# sign from hashtags; normalization of low-frequency users. [find](https://scholar.google.com/scholar?q=Preprocessing%2E+We+perform+the+following+steps+on+all+tweets%3A+lowercasing%2C+tokenization%3B+digits%2FURL+normalization%3B+stripping+of+the+%5C%23+sign+from+hashtags%3B+normalization+of+low%2Dfrequency+users%2E)
37. Arkaitz Zubiaga, Geraldine Wong Sak Hoi, Maria Liakata, Rob Procter, and Peter Tolmie. 2015. Analysing how people orient to and spread rumours in social media by looking at conversational threads. CoRR, abs/1511.07487. — [arXiv:1511.07487](https://arxiv.org/abs/1511.07487) [find](https://scholar.google.com/scholar?q=Arkaitz+Zubiaga%2C+Geraldine+Wong+Sak+Hoi%2C+Maria+Liakata%2C+Rob+Procter%2C+and+Peter+Tolmie%2E+2015%2E+Analysing+how+people+orient+to+and+spread+rumours+in+social+media+by+looking+at+conversational+threads%2E+CoR)
38. Hyperparameters Specifications Hyperparameters are reported in Table 6. When possible, we follow Riedel et al. (2017) for parameter selection. Note that we perform minimal parameter tuning: the goal of this paper is to investigate the efficacy of synthetically annotated data for SD, independently fr… [find](https://scholar.google.com/scholar?q=Hyperparameters+Specifications+Hyperparameters+are+reported+in+Table+6%2E+When+possible%2C+we+follow+Riedel+et+al%2E+%282017%29+for+parameter+selection%2E+Note+that+we+perform+minimal+parameter+tuning%3A+the+goal+o)
39. Arkaitz Zubiaga, Elena Kochkina, Maria Liakata, Rob Procter, Michal Lukasik, Kalina Bontcheva, Trevor Cohn, and Isabelle Augenstein. 2018b. Discourse-aware rumour stance classification in social media using sequential classifiers. Inf. Process. Manage., 54(2):273–290. batch size epochs optimizer BoW… [find](https://scholar.google.com/scholar?q=Arkaitz+Zubiaga%2C+Elena+Kochkina%2C+Maria+Liakata%2C+Rob+Procter%2C+Michal+Lukasik%2C+Kalina+Bontcheva%2C+Trevor+Cohn%2C+and+Isabelle+Augenstein%2E+2018b%2E+Discourse%2Daware+rumour+stance+classification+in+social+media)