# The Extraordinary Failure of Complement Coercion Crowdsourcing

arXiv: https://arxiv.org/abs/2010.05971 · HTML: https://mlsanity.com/paper/2010.05971/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing](https://mlsanity.com/paper/2102.12060/) (2021)
- [Learnings from Technological Interventions in a Low Resource Language: Enhancing Information Access in Gondi](https://mlsanity.com/paper/2211.16172/) (2022)
- [Measure More, Question More: Experimental Studies on Transformer-based Language Models and Complement Coercion](https://mlsanity.com/paper/2212.10536/) (2022)

## Abstract (arXiv)

Crowdsourcing has eased and scaled up the collection of linguistic annotation in recent years. In this work, we follow known methodologies of collecting labeled data for the complement coercion phenomenon. These are constructions with an implied action -- e.g., "I started a new book I bought last week", where the implied action is reading. We aim to collect annotated data for this phenomenon by reducing it to either of two known tasks: Explicit Completion and Natural Language Inference. However, in both cases, crowdsourcing resulted in low agreement scores, even though we followed the same methodologies as in previous work. Why does the same process fail to yield high agreement scores? We specify our modeling schemes, highlight the differences with previous work and provide some insights about the task and possible explanations for the failure. We conclude that specific phenomena require tailored solutions, not only in specialized algorithms, but also in data collection methods.