This repository contains the code for the paper "Sequential Path Signature Networks for Personalised Longitudinal Language Modeling", accepted at ACL Findings 2023.
Due to the sensitive nature of the data, the datasets we use in the paper (TalkLife and Reddit) are not publically available. File paths.py contains user defined paths to the datasets and directories (that we exclude), but which you can use to source datasets for your own longitudinal task.
The following columns are necessary for the input dataset: timeline_id, label, datetime, as well as a column that determines the train/dev/test splits (or a function that randomnly splits the dataset by timeline_id). Having a postid is optional, but recommended for clarity.
The user also needs to provide a seperate file that contains the embeddings (here Sentence-BERT) of your choice. Here we produced Sentence-BERT embeddings using 'all-MiniLM-L6-v2'. In order to reproduce such embeddings we recommend this example.
Clone the git repo:
$ git clone [email protected]:Maria-Liakata-NLP-Group/seq-sig-net.git
Create a conda environment:
$ conda env create --file=environment.yml
Actviate the conda environment
$ conda activate py38-MoC
Model notebooks are in the notebooks folder.
Model classes are in the models folder.
Details of the best hyperparameters of each model are in the Appendix section of the paper.