Papers › Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings
Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings
Bernd Bohnet, Ryan Mcdonald, Goncalo Simoes, Daniel Andor, Emily Pitler, Joshua Maynez
The rise of neural networks, and particularly recurrent neural networks, has produced significant advances in part-of-speech tagging accuracy. One characteristic common among these models is the presence of rich initial word encodings. These encodings typically are composed of a recurrent character-based representation with learned and pre-trained word embeddings. However, these encodings do not consider a context wider than a single word and it is only through subsequent recurrent layers that word or sub-word information interacts. In this paper, we investigate models that use recurrent neural networks with sentence-level context for initial character and word-based representations. In particular we show that optimal results are obtained by integrating these context sensitive representations through synchronized training with a meta-model that learns to combine their states. We present results on part-of-speech and morphological tagging with state-of-the-art performance on a number of languages.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Part-Of-Speech Tagging | Penn Treebank | Meta BiLSTM | Accuracy | 97.96 | #2 of 20 | Archive leaderboard | report |
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