Papers › Jointly Learning to Label Sentences and Tokens

Jointly Learning to Label Sentences and Tokens

14 Nov 2018arXiv:1811.05949archive 2025-07-28

Marek Rei, Anders Søgaard

Learning to construct text representations in end-to-end systems can be difficult, as natural languages are highly compositional and task-specific annotated datasets are often limited in size. Methods for directly supervising language composition can allow us to guide the models based on existing knowledge, regularizing them towards more robust and interpretable representations. In this paper, we investigate how objectives at different granularities can be used to learn better language representations and we propose an architecture for jointly learning to label sentences and tokens. The predictions at each level are combined together using an attention mechanism, with token-level labels also acting as explicit supervision for composing sentence-level representations. Our experiments show that by learning to perform these tasks jointly on multiple levels, the model achieves substantial improvements for both sentence classification and sequence labeling.

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Grammatical Error DetectionSentenceSentence Classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Grammatical Error Detection CoNLL-2014 A1 BiLSTM-JOINT (trained on FCE) F0.5 22.14 #4 of 8 Archive leaderboard report
Grammatical Error Detection CoNLL-2014 A2 BiLSTM-JOINT (trained on FCE) F0.5 29.65 #5 of 8 Archive leaderboard report
Grammatical Error Detection FCE BiLSTM-JOINT F0.5 52.07 #2 of 8 Archive leaderboard report
Grammatical Error Detection JFLEG BiLSTM-JOINT (trained on FCE) F0.5 52.52 #1 of 1 Archive leaderboard report

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