supar - SuPar v1.1.4

Release Notes

This release is meant to fix the following issues: * Fix incorrect masking of ConstituencyCRF * Handle single-root case in MatrixTree * Retain the computational graph for marginals

We also add implementations of sparsemax and SparsemaxSemiring (see details in Martins et al. 2016 and Rush et al. 2020).

- Python
Published by yzhangcs about 4 years ago

supar - SuPar v1.1.3

Release Notes

Highlights

We add implementations of many structured prediction algorithms presented in the form of torch.disrtibutions and semiring notations[^1], including * Tree * MatrixTree (Koo et al., 2007; Ma and Hovy, 2017) * DependencyCRF (Eisner et al., 2000; Zhang et al., 2020) * Dependency2oCRF (McDonald et al., 2006; Zhang et al., 2020) * ConstituencyCRF (Stern et al. 2017; Zhang et al., 2020b) * Linear Chain: * LinearChainCRF (Lafferty et al., 2001)

Take LinearChainCRF as an example: ```py

from supar import LinearChainCRF batchsize, seqlen, ntags = 2, 5, 4 lens = torch.tensor([3, 4]) value = torch.randint(ntags, (batchsize, seqlen)) s1 = LinearChainCRF(torch.randn(batchsize, seqlen, ntags), torch.randn(ntags+1, ntags+1), lens) s2 = LinearChainCRF(torch.randn(batchsize, seqlen, ntags), torch.randn(ntags+1, ntags+1), lens) s1.max tensor([4.4120, 8.9672], gradfn=) s1.argmax tensor([[2, 0, 3, 0, 0], [3, 3, 3, 2, 0]]) s1.logpartition tensor([ 6.3486, 10.9106], gradfn=) s1.logprob(value) tensor([ -8.1515, -10.5572], gradfn=) s1.entropy tensor([3.4150, 3.6549], gradfn=) s1.kl(s2) tensor([4.0333, 4.3807], grad_fn=) ```

Bug fixes

• Fix bug of model saving (#82)
• Fix issue of stride setting for small BERT (#86)
• Fix preprocessing crashes for some UD treebanks (#85)

[^1]: The implementations of structured distributions and semirings are heavily borrowed from torchstruct with some tailoring. For more details, see their tutorial paper and Goodman's paper.

- Python
Published by yzhangcs over 4 years ago

supar - SuPar v1.1.2

Release Notes

• ELMo support
• Checkpoint support
• Dataloader now yields Batch objects
• Fix con name conflict in Windows system (#74)

- Python
Published by yzhangcs over 4 years ago

supar - SuPar v1.1.1

- Python
Published by yzhangcs almost 5 years ago

supar - SuPar v1.1.0

Release Notes

New Features

• Built-in tokenizers (#47)
• Variational Inference methods
  • Mean Field Variational Inference
  • Loopy Belief Propagation
• Semantic Dependency Parsers
  • Biaffine
  • MFVI/LBP
• Model finetuning

Bug Fixes

• Fix issue of building nltk.Tree from string with parentheses (#59, #65)

Available Parsers

• Dependency Parser
  • Biaffine (Dozat and Manning, 2017)
  • CRF/MatrixTree (Koo et al., 2007; Ma and Hovy, 2017)
  • CRF2o (Zhang et al., 2020a)
• Constituency Parser
  • CRF (Zhang et al., 2020b)
• Semantic Dependency Parser
  • Biaffine (Dozat and Manning, 2018)
  • MFVI/LBP (Wang et al, 2019)

- Python
Published by yzhangcs almost 5 years ago

supar - SuPar v1.0.0

Release Notes

Released Models

The following parsers are released in SuPar package and the corresponding English/Chinese pretrained models can be found in the attachments. * Dependency Parser * Biaffine Dependency Parser (Dozat and Manning, 2017) * Non-Projective CRF Dependency Parser with Matrix Tree (Koo et al., 2007; Ma and Hovy, 2017) * Projective First-Order CRF Dependency Parser (Zhang et al., 2020a) * Projective Second-Order Dependency Parser (Zhang et al, 2020a) * Constituency Parser * CRF Constituency Parser (Zhang et al, 2020b)

References

• Timothy Dozat and Christopher D. Manning. 2017. Deep Biaffine Attention for Neural Dependency Parsing.
• Terry Koo, Amir Globerson, Xavier Carreras and Michael Collins. 2007. Structured Prediction Models via the Matrix-Tree Theorem.
• Xuezhe Ma and Eduard Hovy. 2017. Neural Probabilistic Model for Non-projective MST Parsing.
• Yu Zhang, Houquan Zhou and Zhenghua Li. 2020. Fast and Accurate Neural CRF Constituency Parsing.
• Yu Zhang, Zhenghua Li and Min Zhang. 2020. Efficient Second-Order TreeCRF for Neural Dependency Parsing.

- Python
Published by yzhangcs over 5 years ago