Skip to content

Latest commit

 

History

History
42 lines (31 loc) · 2.87 KB

README.md

File metadata and controls

42 lines (31 loc) · 2.87 KB
Raw

NLG-RL

This repository provides PyTorch implementations of our method [1] for reinforcement learning for sentence generation with action-space reduction.

Requirements

  • Python 3.X
  • PyTorch 0.2X or 0.4X
  • Numba

Training NMT models

Please cd to ./code_0.2 or ./code_0.4 to run experiments. The input data format is one sentence per line for each language.

Small softmax

  • Vocabulary predictor (minimal usage with the default settings used in our paper)
    python train_vocgen.py --train_source XX --train_target YY --dev_source ZZ --dev_target WW

  • NMT with cross-entropy (minimal usage after training the vocabulary predictor)
    python train_nmt.py --train_source XX --train_target YY --dev_source ZZ --dev_target WW

  • NMT with REINFORCE and cross-entropy
    python train_nmt_rl.py --train_source XX --train_target YY --dev_source ZZ --dev_target WW

Full softmax (standard baseline)

  • NMT with cross-entropy (minimal usage without using the vocabulary predictor)
    python train_nmt.py --K -1 --train_source XX --train_target YY --dev_source ZZ --dev_target WW

  • NMT with REINFORCE and cross-entropy
    python train_nmt_rl.py --K -1 --train_source XX --train_target YY --dev_source ZZ --dev_target WW

Notes

A new feature in ./code_0.4

I added an additional option --batch_split_size to train_nmt.py and train_nmt_rl.py to avoid using multiple GPUs, mainly for the Full-softmax setting. The idea is very simple; at each mini-batch iteration, the mini-batch is further split into N smaller chunks by setting --batch_split_size N, after sorting the mini-batch examples according to source token lengths. By this, GPU memory consumption can be reduced, and sometimes we can even expect speedup because of less padding computations. Note that partial derivatives of the N chunks are accumurated, and thus this is different from reducing the mini-batch size. Some experimental results are shown in our NAACL2019 camera-ready paper.

BLEU scores

By default, the BLEU scores computed by this codebase do not assume any additional (de-)tokenization. Thus, if you need more specific ways to compute BLEU scores, you need to modify bleu.sh to incorporate your (de-)tokenization tools.

Reference

[1] Kazuma Hashimoto and Yoshimasa Tsuruoka. 2019. Accelerated Reinforcement Learning for Sentence Generation by Vocabulary Prediction. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2019), arXiv cs.CL 1809.01694. (bibtex)

Questions?

Any issues and PRs are welcome.

E-mail: hassy [at] logos.t.u-tokyo.ac.jp or k.hashimoto [at] salesforce.com