Summary of Learning Monotonic Attention in Transducer For Streaming Generation, by Zhengrui Ma et al.
Learning Monotonic Attention in Transducer for Streaming Generation
by Zhengrui Ma, Yang Feng, Min Zhang
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research addresses challenges in using the popular Transducer architecture for simultaneous translation and other tasks requiring non-monotonic alignments. The existing input-synchronous decoding mechanism is inefficient, leading to suboptimal performance. To overcome this limitation, a learnable monotonic attention mechanism is proposed, integrating with the history of input stream. This allows Transducer models to adjust attention scope based on predictions, avoiding the need for exhaustive alignment search. The approach leverages the forward-backward algorithm and extensive experiments demonstrate improved handling of non-monotonic alignments in streaming generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a significant improvement in using the popular Transducer architecture for simultaneous translation and other tasks that require non-monotonic alignments. Right now, the existing method is not very good at this task because it can’t handle complex relationships between words. The researchers came up with a new way to look at the input stream, so the model can adjust its attention as it generates text. This means it’s better at understanding when words are related in different ways. The results show that their new approach is much more effective. |
Keywords
» Artificial intelligence » Alignment » Attention » Translation
