SDLM

This is author implementation of the paper:
Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach
Wenyu Du*, Zhouhan Lin*, Yikang Shen, Timothy J. O'Donnell, Yoshua Bengio, Yue Zhang (* = equal contribution)
ACL 2020

The repository is originally forked from Ordered Neuron (Shen et al. 2018). Please cite our work as follows if you use it in your work:

@inproceedings{sdlm,
    title = "Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach",
    author = "Du, Wenyu  and Lin, Zhouhan  and  Shen, Yikang  and O{'}Donnell, Timothy J.  and  Bengio, Yoshua  and Zhang, Yue",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    pages = "6611--6628",
   }

Usage

Step 1: Prerequisite Software and Data Preparation

We simply replicate the environment in Ordered Neuron (Shen et al. 2018) with an update to PyTorch 1.2.

data consists of two parallel versions of PTB penn and syntactic_penn. penn is the vanilla Penn Treebank for language modelling, while syntactic_penn contains additional syntactic information. You may still need to download the original PTB from LDC for testing.

Step 2: Run the Experiments

Train language model with syntactic distance

python main.py --l4d=0/1/2

Train language model without syntactic distance (Vanilla ON-LSTM LM)

python main.py --un

Step 3: Test

Decode using biased algorithm:

python testf1.py  --b --l=0/1/2

Decode using unbiased algorithm:

python testf1.py  --o --l=0/1/2