This repository contains the source code to reproduce the experiments in NeurIPS 2022 paper Controllable Text Generation with Neurally-Decomposed Oracle by Tao Meng, Sidi Lu, Nanyun Peng and Kai-Wei Chang.
We are now working on the camera ready and the codebase is not a in a stable version. If you come up with some technical issue, please feel free to leave an issue or send an email to the first author.
We propose a general and efficient framework to control auto-regressive generation models with NeurAlly-Decomposed Oracle (NADO). Given a pre-trained base language model and a sequence-level boolean oracle function, we propose to decompose the oracle function into token-level guidance to steer the base model in text generation. Specifically, the token-level guidance is approximated by a neural model trained with examples sampled from the base model, demanding no additional auxiliary labeled data. We present the closed-form optimal solution to incorporate the token-level guidance into the base model for controllable generation. We further provide a theoretical analysis of how the approximation quality of NADO affects the controllable generation results. Experiments conducted on two applications: (1) text generation with lexical constraints and (2) machine translation with formality control demonstrate that our framework efficiently guides the base model towards the given oracle while maintaining high generation quality.
This repository will contain both experiments described in this paper. So far the LCG part is still under construction and expected to come out later October.
The machine translation formality change experiments leverage the CALLHOME Spanish-English Speech Translation Corpus as source data, and evaluate the BLUE score with the fluent references. Note that LDC access is required for the first dataset.
Requirements
pip install -r requirements.txtRunning
python train_MT.pyThe code will automatically download MarianMT model and sample translated texts from source texts from Fisher-and-Callhome Corpus. The sampled data will be dumped in ./dump/MT directory. The sampled data is labeled by an formality oracle trained in FUDGE paper. A NADO model will be trained by those labeled sampled data. The translated results will be evaluated based on oracle scores and the BLEU scores compared to fluent references.
Alternative arguments:
--sample_batch_size the batch size in sampling. Must be integer times of 8.
--batch_size the batch size in training. Must be a divider of sample_batch_size
--regularization the strength of regularization
--max_length the maximum length accepted in training or evaluation