---
title: "Who cites An Information Bottleneck — 51 citing papers"
description: "51 papers of the Elamur Research library cite «An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction»…"
canonical: "https://research.elamur.ai/free/en/papers/paranjape-2020-information-bottleneck-approach-controlling-conciseness/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [An Information Bottleneck Approach for…](https://md.research.elamur.ai/free/en/papers/paranjape-2020-information-bottleneck-approach-controlling-conciseness/) / cited by

# Who cites «An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction»

51 papers of our library cite it (of the 141,840 whose bibliographies we read) · 2 with a page here, the most cited first · [← back to «An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction»](https://md.research.elamur.ai/free/en/papers/paranjape-2020-information-bottleneck-approach-controlling-conciseness/)

1. **[Challenges and Strategies in Cross-Cultural NLP](https://md.research.elamur.ai/free/en/papers/hershcovich-2022-challenges-strategies-cross-cultural-nlp/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Daniel Hershcovich, Stella Frank, Heather Lent, et al.*2022 · 290 citations*
2. **[Measuring Association Between Labels and Free-Text Rationales](https://md.research.elamur.ai/free/en/papers/wiegreffe-2021-measuring-association-between-labels-free/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sarah Wiegreffe, Ana Marasović, Noah A. Smith*2021 · 215 citations*

49 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/paranjape-2020-information-bottleneck-approach-controlling-conciseness/) — An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction
