Papers › Finding Function in Form: Compositional Character Models for Open Vocabulary Word...
Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation
Wang Ling, Tiago Luís, Luís Marujo, Ramón Fernandez Astudillo, Silvio Amir, Chris Dyer, Alan W. black, Isabel Trancoso
We introduce a model for constructing vector representations of words by composing characters using bidirectional LSTMs. Relative to traditional word representation models that have independent vectors for each word type, our model requires only a single vector per character type and a fixed set of parameters for the compositional model. Despite the compactness of this model and, more importantly, the arbitrary nature of the form-function relationship in language, our "composed" word representations yield state-of-the-art results in language modeling and part-of-speech tagging. Benefits over traditional baselines are particularly pronounced in morphologically rich languages (e.g., Turkish).
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Part-Of-Speech Tagging | Penn Treebank | Char Bi-LSTM | Accuracy | 97.78 | #4 of 20 | Archive leaderboard | report |
| Part-Of-Speech Tagging | Penn Treebank | Bi-LSTM | Accuracy | 97.36 | #17 of 20 | Archive leaderboard | report |
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