This repository implements word-synchronous beam search with fast-tracking (Stern et al. 2017; Hale et al., 2018) for Recurrent Neural Network Grammars (Dyer et al., 2016), as well as an ablated RNNG model referred as "ActionLSTM" in Wilcox et al. (2019). The ablated model is implemented in nt-parser/nt-parser-gen-action-only.cc, which only uses the encoder of the action sequence, similar to Choe and Charniak (2016). The code is built on the RNNG repository.
Please put the latest version of Dynet in the root folder of this repository, and refer to the original RNNG repository for instructions on building the repository, preparing oracle files, and training and evaluating models. The oracle files for the generative RNNG (nt-parser-gen) also work for nt-parser-gen-action-only. As the code here uses Dynet, the flag --cnn-mem in the commands will need to be changed to --dynet-mem.
The scripts here work with Python 2.7 version.
python get_dictionary.py train.02-21 > train_vocab.txt
Assuming a list of tokenized sentences as the text file eval_file.txt, with one sentence per line:
python get_unkified_input.py train_vocab.txt eval_file.txt > unkified_eval_file.txt
build/nt-parser/nt-parser-gen --dynet-mem 2000 -x -T path/to/train_gen.oracle -v path/to/unkified_eval_file -f path/to/surprisals_output --clusters path/to/clusters-train-berk.txt --input_dim 256 --lstm_input_dim 256 --hidden_dim 256 -m model_params_file
This will write the surprisal values estimated from a trained generative RNNG to path/to/surprisals_output. The hyperparameters of the word-synchronous beam search algorithm can be adjusted through the command-line flags, --beam_size (default 100), --fasttrack_beam_size (default 5), and --word_beam_size (default 10).
nt-parser-gen-action-only supports the usage of this algorithm with the action-only model.
We thank Miguel Ballesteros, Hui Wan, and Roger Levy for the help and advice with the implementation.
[1] Choe, D., & Charniak, E. (2016). Parsing as Language Modeling. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.
[2] Dyer, C., Kuncoro, A., Ballesteros, M., & Smith, N. A. (2016). Recurrent Neural Network Grammars. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
[3] Futrell, R., Wilcox, E., Morita, T., Qian, P., Ballesteros, M., & Levy, R. (2019). Neural language models as psycholinguistic subjects: Representations of syntactic state. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
[4] Hale, J., Dyer, C., Kuncoro, A., & Brennan, J. (2018). Finding Syntax in Human Encephalography with Beam Search. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).
[5] Stern, M., Fried, D., & Klein, D. (2017). Effective Inference for Generative Neural Parsing. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing.
[6] Wilcox, E., Qian, P., Futrell, R., Ballesteros, M., & Levy, R. (2019). Structural Supervision Improves Learning of Non-Local Grammatical Dependencies. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers).