Papers › Alleviating Sequence Information Loss with Data Overlapping and Prime Batch Sizes

Alleviating Sequence Information Loss with Data Overlapping and Prime Batch Sizes

18 Sep 2019CONLL 2019 11arXiv:1909.08700archive 2025-07-28

Noémien Kocher, Christian Scuito, Lorenzo Tarantino, Alexandros Lazaridis, Andreas Fischer, Claudiu Musat

In sequence modeling tasks the token order matters, but this information can be partially lost due to the discretization of the sequence into data points. In this paper, we study the imbalance between the way certain token pairs are included in data points and others are not. We denote this a token order imbalance (TOI) and we link the partial sequence information loss to a diminished performance of the system as a whole, both in text and speech processing tasks. We then provide a mechanism to leverage the full token order information -Alleviated TOI- by iteratively overlapping the token composition of data points. For recurrent networks, we use prime numbers for the batch size to avoid redundancies when building batches from overlapped data points. The proposed method achieved state of the art performance in both text and speech related tasks.

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Code

nkcr/overlap-ml officialmentioned in papermentioned on GitHubpytorchBSD-3-Clause report

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Tasks

Language Modelling

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Language Modelling WikiText-103 AWD-LSTM-MoS + ATOI Test perplexity 32.85 #74 of 89 Archive leaderboard report
Language Modelling WikiText-103 AWD-LSTM-MoS + ATOI Validation perplexity 31.92 #74 of 89 Archive leaderboard report
Language Modelling WikiText-2 AWD-LSTM + ATOI Number of params 33M #30 of 38 Archive leaderboard report
Language Modelling WikiText-2 AWD-LSTM + ATOI Test perplexity 64.73 #30 of 38 Archive leaderboard report
Language Modelling WikiText-2 AWD-LSTM + ATOI Validation perplexity 67.47 #30 of 38 Archive leaderboard report

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