Papers with token-by-token

4 papers
Attending to Future Tokens for Bidirectional Sequence Generation (D19-1)

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Challenge: Neural sequence generation is typically performed token-by-token and left-to-right.
Approach: They propose to use placeholder tokens to make the sequence generation process bidirectional by taking past and future tokens into consideration when generating the actual output token.
Outcome: The proposed approach outperforms baselines on two conversational tasks by a large margin.
Multilingual Autoregressive Entity Linking (2022.tacl-1)

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Challenge: mGENRE is a sequence-to-sequence system for multilingual entity linking . mGenRE is used to solve language-specific mentions to a multilingual Knowledge Base .
Approach: They propose a sequence-to-sequence system for multilingual entity linking . they match language-specific mentions against a multilingual Knowledge Base (KB) mGENRE is a sequential system that predicts the name of the target entity token-by-token .
Outcome: The proposed system improves on three popular MEL benchmarks and shows improvements in accuracy.
CopyNE: Better Contextual ASR by Copying Named Entities (2024.acl-long)

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Challenge: Existing approaches to transcribe contextual named entities (NEs) treat entities as tokens and generate them token-by-token, which may result in incomplete transcriptions of entities.
Approach: They propose a mechanism that can copy entities from the NE dictionary and reduce errors during entity transcription.
Outcome: The proposed mechanism can copy entities from the NE dictionary, reducing errors during entity transcription, ensuring the completeness of the entity.
Interpretable Math Word Problem Solution Generation via Step-by-step Planning (2023.acl-long)

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Challenge: Existing approaches to solving math word problems focus on obtaining the correct answer.
Approach: They propose a step-by-step planning approach for intermediate solution generation that strategically plans the generation of the next solution step based on the MWP and the previous solution steps.
Outcome: The proposed approach improves the accuracy and interpretability of the solution on automatic metrics and human evaluation.

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