---
title: "Who cites Alto: Rapid Prototyping for Parsing and…"
description: "The papers of the Elamur Research library that cite «Alto: Rapid Prototyping for Parsing and Translation», found in their bibliographies."
canonical: "https://research.elamur.ai/free/en/papers/gontrum-2017-alto-rapid-prototyping-parsing-translation/cited-by/"
---
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# Who cites «Alto: Rapid Prototyping for Parsing and Translation»

6 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.research.elamur.ai/free/en/papers/gontrum-2017-alto-rapid-prototyping-parsing-translation/)

1. [AMR Parsing is Far from Solved: GrAPES, the Granular AMR Parsing Evaluation Suite](https://aclanthology.org/2023.emnlp-main.662/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
2. [SLOG: A Structural Generalization Benchmark for Semantic Parsing](https://aclanthology.org/2023.emnlp-main.194/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
3. [Applying Occam”s Razor to Transformer-Based Dependency Parsing: What Works, What Doesn”t, and What is Really Necessary](https://md.research.elamur.ai/free/en/papers/grunewald-2021-applying-occam-s-razor-transformer/) *— our card · 2021 · Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)*
4. [BME-TUW at SR”20: Lexical grammar induction for surface realization](https://aclanthology.org/2020.msr-1.2/) *— 2020 · Proceedings of the Third Workshop on Multilingual Surface Realisation*
5. [Generalized chart constraints for efficient PCFG and TAG parsing](https://aclanthology.org/P18-2099/) *— 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
6. [Integrated sentence generation using charts](https://aclanthology.org/W17-3520/) *— 2017 · Proceedings of the 10th International Conference on Natural Language Generation*
