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Monotonic Multihead Attention
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Simultaneous machine translation models start generating a target sequence before they have encoded or read the source sequence. Recent approaches for this task either apply a fixed policy on a state-of-the art Transformer model, or a learnable monotonic attention on a weaker recurrent neural network-based structure. In this paper, we propose a new attention mechanism, Monotonic Multihead Attention (MMA), which extends the monotonic attention mechanism to multihead attention. We also introduce two novel and interpretable approaches for latency control that are specifically designed for multiple attentions heads. We apply MMA to the simultaneous machine translation task and demonstrate better latency-quality tradeoffs compared to MILk, the previous state-of-the-art approach. We also analyze how the latency controls affect the attention span and we motivate the introduction of our model by analyzing the effect of the number of decoder layers and heads on quality and latency.
Forward citations
Cited by 4 Pith papers
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SimulS2ST-Omni: Data-Efficient Streaming Speech-to-Speech Translation via Explicit Trajectory Supervision
A joint text-code trajectory supervision recipe lets a two-stream speech LM achieve competitive long-form streaming S2ST with ~2k hours of paired speech.
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Do LLMs Need Architectural Changes for Simultaneous Speech Translation? A Prefix-to-Prefix Data Driven Approach
Teacher-built bounded-waiting prefix targets let a chunked streaming speech LLM improve simultaneous translation quality by +1.54 COMETKiwi at +0.15 s latency.
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MFLA: Monotonic Finite Look-ahead Attention for Streaming Speech Recognition
MFLA adds finite look-ahead attention plus a CIF-based token counter to Whisper, enabling streaming recognition with a wait-k latency-quality trade-off.
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X-Translator: A Real-Time Multilingual Speaker-Aware Speech-to-Speech Translation System
An open, modular cascaded system (streaming ASR + MT + prompt-conditioned TTS) preserves speaker identity in long-form multi-speaker translation, at higher latency and slightly lower translation quality than proprietary APIs.
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