Enhancing Semantic Plausibility Modeling using Entity and Event Knowledge [Paper]

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Usage

Virtual Environment

Create and activate a conda environment.

conda create -n MSPPlausibleParrots python=3.11
conda activate MSPPlausibleParrots
git clone https://github.com/st143575/SemPlaus-plausibleparrots.git
cd SemPlaus-plausibleparrots

Install pip in the conda env (if it's not installed by default).

conda install pip

Installation

Make sure you have the following dependencies installed.

• cuda 12.3

Install packages in requirements.txt by running

pip install -r requirements.txt --no-cache-dir

Path Configuration

PATH is the root path of this repository. CACHE_DIR = PATH + "cache/"

Data Analysis

Please follow the instructions in ./0-preliminary_study/ for data analysis.

Data Preprocessing and Data Augmentation

Please follow the instructions in ./1-data_preprocessing/ for data preprocessing and augmentation.

Baselines

We evaluate two baseline systems on our task: (1) zero-shot inference; and (2) fine-tuning on the sentences preprocessed and augmented from the original (s,v,o)-event triples. To run the baselines, please follow the instructions in ./2-baselines/.

Event Detection (ED)

Please follow the instructions in ./3-ed/.

Note: To avoid potential package version conflict, please create and activate a new environment, install packages in requirements_ed.txt and run ED there. See the instructions in ./3-ed/ for details.

Ultra Fine-grained Entity Typing (UFET)

Please follow the instructions in ./4-ufet/.

Note: To avoid potential package version conflict, please create and activate a new environment, install packages in requirements_et.txt and run UFET there. See the instructions in ./4-ufet/ for details.

Dataset Construction

We construct the datasets for our system using the templates specifically designed for (1) injecting both event and entity type knowledge (evt+ent), (2) injecting only event type knowledge (evt), and (3) injecting only entity types knowledge (ent), respectively. Please follow the instructions in ./5-dataset_construction/.

System Fine-tuning and Evaluation

We fine-tune and evaluate our system with the knowledge-enhanced datasets. Please follow the instructions in ./6-finetune_and_eval/.

Evaluation Results