Category: NLP

Announcing UDTube

In collaboration with CUNY master’s program graduate Daniel Yakubov, we have recently open-sourced UDTube, our neural morphological analyzer. UDTube performs what is sometimes called morphological analysis in context: it provides morphological analyses—coarse POS tagging, more-detailed morphosyntactic tagging, and lemmatization—to whole sentences using nearby words as context.

The UDTube model, developed in Yakubov 2024, is quite simple: it uses a pre-trained Hugging Face encoders to compute subword embeddings. We then take the last few layers of these embeddings and mean-pool them, then mean-pool subword embeddings for those words which correspond to multiple subwords. The resulting encoding of the input is then fed to separate classifier heads for the different tasks (POS tagging, etc.). During training we fine-tune the pre-trained encoder in addition to fitting the classifier heads, and we make it possible to set separate optimizers, learning rates, and schedulers for the encoder and classifier modules.

UDTube is built atop PyTorch and Lightning, and its command-line interface is made much simpler by the use of LightningCLI, a module which handles most of the interface work. One can configure the entire thing using YAML configuration files. CUDA GPUs and MPS-era Macs (M1 etc.) can be used to accelerate training and inference (and should work out of the box). We also provide scripts to perform hyperparameter tuning using Weights & Biases. We believe that this model, with appropriate tuning, is probably state-of-the-art for morphological analysis in context.

UDTube is available under an Apache 2.0 license on GitHub and on PyPI.

References

Yakubov, D. 2024. How do we learn what we cannot say? Master’s thesis, CUNY Graduate Center.

Learned tokenization

Conventional (i.e., non-neural, pre-BERT) NLP stacks tend to use rule-based systems for tokenizing sentences into words. One good example is Spacy, which provides rule-based tokenizers for the languages it supports. I am sort of baffled this is considered a good idea for languages other than English, since it seems to me that most languages need machine learning for even this task to properly handle phenomena like clitics. If you like the Spacy interface—I admit it’s very convenient—and work in Python, you may want to try thespacy-udpipe library, which exposes the UDPipe 1.5 models for Universal Dependencies 2.5; these in turn use learned tokenizers (and taggers, morphological analyzers, and dependency parsers, if you care) trained on high-quality Universal Dependencies data.