---
title: "Show Me Your Evidence - an Automatic Method — Rinott 2015"
description: "The paper introduces Context Dependent Evidence Detection (CDED)."
authors: ["Ruty Rinott", "Lena Dankin", "Carlos Alzate Perez", "Mitesh M. Khapra", "Ehud Aharoni", "Noam Slonim"]
year: 2015
venue: "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D15-1050/"
doi: "10.18653/v1/d15-1050"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/rinott-2015-show-me-your-evidence-automatic/"
---
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# Show Me Your Evidence - an Automatic Method for Context Dependent Evidence Detection

**Authors:** [Ruty Rinott](https://md.research.elamur.ai/free/en/authors/ruty-rinott/), Lena Dankin, Carlos Alzate Perez, [Mitesh M. Khapra](https://md.research.elamur.ai/free/en/authors/mitesh-m-khapra/), [Ehud Aharoni](https://md.research.elamur.ai/free/en/authors/ehud-aharoni/), [Noam Slonim](https://md.research.elamur.ai/free/en/authors/noam-slonim/) · **Year:** 2015 · **Venue:** Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/D15-1050/) · [PDF](https://aclanthology.org/D15-1050.pdf) · [DOI 10.18653/v1/d15-1050](https://doi.org/10.18653/v1/d15-1050) · [OpenAlex](https://openalex.org/W2250762536)

**Citations:** 199 citations (OpenAlex, 2026-10-14) · [97](https://md.research.elamur.ai/free/en/papers/rinott-2015-show-me-your-evidence-automatic/cited-by/) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper introduces Context Dependent Evidence Detection (CDED). Given a debate topic, a claim, and related documents, the goal is to locate short, well-formed text segments that back the claim within that topic. The authors separate three evidence types: Study, Expert, and Anecdotal. They built a benchmark from 547 Wikipedia articles covering 58 Debatabase topics. Labeling had two stages: five annotators marked candidates, and a second group of five accepted or rejected them and assigned types. The system is a supervised pipeline:
- a context-free stage scores how coherent a candidate is and whether it looks like evidence;
- a context-dependent stage judges how the candidate relates to the claim and topic;
- a claim selection component sets aside claims that probably have no evidence.

Tests on topics unseen in training show that the models transfer across topics. For many claims, a correct evidence segment of the right type appears among the top 4 candidates. This work helps lay the groundwork for systems that build arguments automatically and support decisions.

## Key points
- **Dataset:**
  - 39 topics are used for train/test and 19 are held out for feature tuning and auxiliary classifiers.
  - The train/test topics contain 3,057 distinct CDE in 274 articles.
  - The data is heavily skewed: only 31% of claims have any Study-type CDE.
- **Pipeline vs. ablation baselines:**
  - Removing the context-free stage (the NCFS baseline) hurts both exact MRR and overlap-based MRR.
  - For Study evidence, the context-dependent component beats simple W2V and BM25 relatedness baselines.
- **Macro-averaged MRR with claim selection (Study / Expert / Anecdotal):**
  - Pipeline: 0.25 / 0.34 / 0.04.
  - Basic claim selection (BCS): 0.16 / 0.23 / 0.05.
  - All claims: 0.12 / 0.20 / 0.03.
- **Claim selection coverage:** the learned claim selector passes more claims than BCS (34% vs 31% for Study, 52% vs 46% for Expert) while still scoring higher. It performs poorly for Anecdotal, which had only 151 claims with CDE.
- **Remaining difficulties:** getting the exact segment boundaries right is still hard, as the gap between exact and overlap MRR shows.

## Interesting ideas
- **Two-stage annotation:** one group proposes candidates and a separate group votes on them. This raises recall cheaply and then filters by majority, which suits any span-annotation task with subjective labels.
- **Splitting context-free from context-dependent scoring:** first ask whether a span is well-formed and evidence-like at all, then check whether it fits this particular claim. The same split could serve citation finding, quote mining, or support retrieval for fact-checking.
- **Scoring relatedness against nearby text:** the system compares the claim not only with the candidate but also with its section header and the titles of the citations it references. This cheaply adds document structure to relevance estimation.
- **A claim-level "abstain" classifier:** the system first predicts whether a claim has any evidence at all. Tightening its threshold trades coverage for precision in a controlled way, which helps in user-facing tools where wrong suggestions are costly.
- **Reporting exact and overlap MRR together:** comparing the two separates "found the right region" from "cut the right span boundaries". This is a useful diagnostic for any span extraction system.

*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: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cite

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

```bibtex
@inproceedings{rinott2015show,
  title = {{Show Me Your Evidence - an Automatic Method for Context Dependent Evidence Detection}},
  author = {Rinott, Ruty and Dankin, Lena and Perez, Carlos Alzate and Khapra, Mitesh M. and Aharoni, Ehud and Slonim, Noam},
  booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing},
  year = {2015},
  doi = {10.18653/v1/d15-1050},
  url = {https://doi.org/10.18653/v1/d15-1050}
}
```

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