title: Efficient Monotonic Multihead Attention
summary: We introduce the Efficient Monotonic Multihead Attention (EMMA), a
  state-of-the-art simultaneous translation model with numerically-stable and
  unbiased monotonic alignment estimation. In addition, we present improved
  training and inference strategies, including simultaneous fine-tuning from an
  offline translation model and reduction of monotonic alignment variance. The
  experimental results demonstrate that the proposed model attains
  state-of-the-art performance in simultaneous speech-to-text translation on the
  Spanish and English translation task.
paper_authors._id: 65727f19b49f7a8efbc49a34
paper_authors.name: Xutai Ma
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-08T10:41:10.678Z
paper_authors._id: 65034bd70fc2f04d8569ab83
paper_authors.avatarUrl: /avatars/7473d2513f5aabe00e4751dc55441bee.svg
paper_authors.fullname: Ma
paper_authors.type: user
paper_authors.user: Xutai
paper_authors._id: 65727f19b49f7a8efbc49a35
paper_authors.name: Anna Sun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-08T10:41:18.717Z
paper_authors._id: 63eace53a332618465d5a527
paper_authors.avatarUrl: /avatars/64b887ebcbf3f74abdcdca2e8383441d.svg
paper_authors.fullname: Anna Sun
paper_authors.type: user
paper_authors.user: annasun28
paper_authors._id: 65727f19b49f7a8efbc49a36
paper_authors.name: Siqi Ouyang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-08T10:41:25.897Z
paper_authors._id: 63fbe11a0aab060792fec566
paper_authors.avatarUrl: /avatars/4d2a64f951e2acc83c2947da0e9cc277.svg
paper_authors.fullname: Siqi Ouyang
paper_authors.type: user
paper_authors.user: owaski
paper_authors._id: 65727f19b49f7a8efbc49a37
paper_authors.name: Hirofumi Inaguma
paper_authors._id: 65727f19b49f7a8efbc49a38
paper_authors.name: Paden Tomasello
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-08T10:41:45.342Z
paper_authors._id: 656e520c57ab9e2219c55dd3
paper_authors.avatarUrl: /avatars/21599ad9b3807939f90622f5d694a58c.svg
paper_authors.fullname: Paden Tomasello
paper_authors.type: user
paper_authors.user: padentomasello
paper_title: Efficient Monotonic Multihead Attention
paper_summary: We introduce the Efficient Monotonic Multihead Attention (EMMA), a
  state-of-the-art simultaneous translation model with numerically-stable and
  unbiased monotonic alignment estimation. In addition, we present improved
  training and inference strategies, including simultaneous fine-tuning from an
  offline translation model and reduction of monotonic alignment variance. The
  experimental results demonstrate that the proposed model attains
  state-of-the-art performance in simultaneous speech-to-text translation on the
  Spanish and English translation task.
paper_ai_summary: Efficient Monotonic Multihead Attention (EMMA) achieves top performance in simultaneous speech-to-text translation through improved training and reduced alignment variance.
paper_ai_keywords: Efficient Monotonic Multihead Attention EMMA monotonic alignment simultaneous fine-tuning monotonic alignment variance