sangheek16/dialogue-response-dynamics

Codes and material used for evaluating PLMs on dialogue response dynamics

About

This repo contains datasets and code for COLING 2022 paper “No, they did not”: Dialogue response dynamics in pre-trained language models, by Sanghee J. Kim, Lang Yu and Allyson Ettinger.

Repo Structures

• src/ contains source code to generate dataset and duplicate experiment results in the paper
• datasets/ contains raw data and datasets used in the paper

Dataset

• names.csv, np.csv and vp.csv are raw datasets that can be used as inputs for input_generator.py to generate an input .csv file -- this will have the same format as used_items.csv
• used_items.csv contains the items that were used in this paper

Code

• evaluate.py is built on Huggingface's transformer and requires minicons
• input_generator.py randomly samples from names.csv, np.csv and vp.csv and generates input items that can be used for model evaluation
• for replication of the results in this paper, use used_items.csv under src/ instead of generating new items
• evaluate.py produces model output probabilities / conditional log-probabilities / pseudo-log-likelihoods of causal language model and masked language models. It produces output for header selection (section 5.1), rejection (section 5.2), conjunction (section 5.3) and ellipsis (section 6) tasks.
• probe.py contains code for generating probing results (section 5.4)

Usage

To run evaluate.py, update config at the beginning of the code (e.g., input path, output path, etc.). Also update the name of model (model_name variable) and type of task (task variable) you wish to explore, at the end of the code. Then run python3 evaluate.py.