Overview

This repo contains source code for "Formality Style Transfer with Hybrid Textual Annotations"

Install

The software is built upon fairseq, follow the instruction on this link for installation

Data

The preprocessed data could be downloaded from this link.

Usage

To replicate our experiment on the GYAFC dataset, see the pipeline.sh script for a working example.

The general usage is similar to fairseq with the same syntax for arguments. The only difference is to make sure the --task is style-transfer and arch is sty-transformer.

Training with supervised paired data: train.py

usage: train.py [-h] DIR
                --task style_transfer
                --arch sty_transformer [--criterion CRIT] [--max-epoch N]
                [--max-update N] [--clip-norm NORM] [--sentence-avg]
                [--mt-loss-weight] [--classify-loss-weight]
                [--self-recon-loss-weight] [--cycle-recon-loss-weight]
                [--max-source-positions] [--max-target-positions]
                [--encoder-embed-dim] [--encoder-ffn-embed-dim]
                [--decoder-embed-dim] [--decoder-ffn-embed-dim]
                [--encoder-attention-heads] [--decoder-attention-heads]
                [--encoder-layers] [--decoder-layers]
                [--optimizer OPT] [--lr LR_1,LR_2,...,LR_N]
                [--momentum M] [--weight-decay WD]
                [--lr-scheduler LR_SCHEDULER] [--lr-shrink LS] [--min-lr LR]
                [--save-dir DIR]
                [--restore-file RESTORE_FILE] [--save-interval N]
                [--save-interval-updates N] [--keep-interval-updates N]
                [--no-save] [--no-epoch-checkpoints] [--validate-interval N]

Generate with trained model: generate.py

usage: generate.py [-h] DIR
                   --task style_transfer  
                   [--no-progress-bar] [--log-interval N]
                   [--remove-bpe [REMOVE_BPE]] [--beam N]
                   [--nbest N]
                   [--output-path OUTPUT_PATH]