Papers with decoding policy

3 papers
Opportunistic Decoding with Timely Correction for Simultaneous Translation (2020.acl-main)

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Challenge: Existing approaches to balancing translation quality and latency are either too aggressive or too conservative.
Approach: They propose an opportunistic decoding technique that always (over-)generates a certain mount of extra words at each step to keep the audience on track with the latest information.
Outcome: The proposed technique reduces latency and increases BLEU with no over-generating . it also corrects mistakes in the overgenerated words when observing more context .
Direct Simultaneous Speech-to-Text Translation Assisted by Synchronized Streaming ASR (2021.findings-acl)

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Challenge: Existing approaches to simultaneous speech-to-text translation suffer from error propagation and extra latency.
Approach: They propose a new paradigm for simultaneous speech-to-text translation using two separate decoders . they use multitask learning to jointly learn these two tasks with a shared encoder .
Outcome: The proposed method achieves substantially better translation quality at similar levels of latency.
Alignment-Based Decoding Policy for Low-Latency and Anticipation-Free Neural Japanese Input Method Editors (2024.findings-acl)

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Challenge: Japanese input method editors (IMEs) allow users to input Japanese text using a limited set of characters such as the kana syllabary.
Approach: They propose a simple decoding policy to enable simultaneous kana-kanji conversion in Japanese IMEs inspired by simultaneous machine translation.
Outcome: The proposed approach achieves a better quality-latency trade-off than baselines while being more practical due to its ability to directly handle streaming input.

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