{
  "id": "lattice2021",
  "title": "Sequence-to-Lattice Models for Fast Translation",
  "authors": [
    {
      "given": "Yuntian",
      "family": "Deng"
    },
    {
      "given": "Alexander M.",
      "family": "Rush"
    }
  ],
  "year": 2021,
  "venue": "Findings of EMNLP 2021",
  "paper_url": "https://aclanthology.org/2021.findings-emnlp.318/",
  "pdf_url": "https://aclanthology.org/2021.findings-emnlp.318.pdf",
  "code_url": "https://github.com/harvardnlp/cascaded-generation",
  "doi": "10.18653/v1/2021.findings-emnlp.318",
  "summary": "Candidate lookup, parallel conditional edge scoring, and exact Viterbi decoding replace a deep translation decoder.",
  "connection": "Its candidate–edge–path architecture connects to coherent parallel drafting; its objective and inference differ from speculative prefix selection.",
  "bibtex_type": "inproceedings",
  "citation_key": "deng-rush-2021-sequence",
  "source_urls": [
    "https://aclanthology.org/2021.findings-emnlp.318/",
    "https://aclanthology.org/2021.findings-emnlp.318.pdf"
  ],
  "abstract": "Non-autoregressive machine translation (NAT) approaches enable fast generation by utilizing parallelizable generative processes. The remaining bottleneck in these models is their decoder layers; unfortunately unlike in autoregressive models (Kasai et al., 2020), removing decoder layers from NAT models significantly degrades accuracy. This work proposes a sequence-to-lattice model that replaces the decoder with a search lattice. Our approach first constructs a candidate lattice using efficient lookup operations, generates lattice scores from a deep encoder, and finally finds the best path using dynamic programming. Experiments on three machine translation datasets show that our method is faster than past non-autoregressive generation approaches, and more accurate than naively reducing the number of decoder layers.",
  "abstract_source": "https://aclanthology.org/2021.findings-emnlp.318/",
  "updated_at": "2026-11-10",
  "record_url": "https://yuntiandeng.com/research/papers/lattice2021/"
}