--- title: "Who cites Constrained Decoding for Neural — 62 citing papers" description: "62 papers of the Elamur Research library cite «Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented…" canonical: "https://research.elamur.ai/free/en/papers/balakrishnan-2019-constrained-decoding-neural-nlg-compositional/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/balakrishnan-2019-constrained-decoding-neural-nlg-compositional/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Constrained Decoding for Neural NLG from…](https://md.research.elamur.ai/free/en/papers/balakrishnan-2019-constrained-decoding-neural-nlg-compositional/) / cited by # Who cites «Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue» 62 papers of our library cite it (of the 141,840 whose bibliographies we read) · 7 with a page here, the most cited first · [← back to «Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue»](https://md.research.elamur.ai/free/en/papers/balakrishnan-2019-constrained-decoding-neural-nlg-compositional/) 1. [**Dependency Dialogue Acts — Annotation Scheme and Case Study**Jon Z. Cai, Brendan King, Margaret Perkoff, et al.*2023 · 7 citations*](https://md.research.elamur.ai/free/en/papers/cai-2023-dependency-dialogue-acts-annotation-scheme/) **[Survey on Hallucinations in Large Language Models (大语言模型的幻觉问题研究综述)](https://md.research.elamur.ai/free/en/papers/liu-2024-hallucinations-large-language/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Zeyuan Liu (刘泽垣), Pengjiang Wang (王鹏江), Xiaobin Song (宋晓斌), et al.*2024* 55 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/balakrishnan-2019-constrained-decoding-neural-nlg-compositional/) — Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue