MeetingQA: Extractive Question-Answering on Meeting Transcripts (ACL 2023)

• Authors: Archiki Prasad, Trung Bui, Seunghyun Yoon, Hanieh Deilamsalehy, Franck Dernoncourt, Mohit Bansal
• Paper // Project Page
• Note: This repository contains code and data for our ACL 2023 paper "MeetingQA: Extractive Question-Answering on Meeting Transcripts".

Dependencies

This code is written using PyTorch and HuggingFace's Transformer repo. Running MeetingQA baselines requires access to GPUs. Most evaluations are relatively light-weight, so a total of 2-3 GPUs should suffice.

Installation and Setup

Repo Structure

The consituent folders are described below (also refer to corresponding README.md files in the folders):

• requirements: Contains various *_requirements.txt files used to setup multiple conda environments used throughout the project.
• DataCollection: Contains code used for gathering and processing raw meeting transcripts/datasets.
• PostAnnotationProcessing: Contains code use in annotation and post-processing of meeting transcripts for QA.
• qaCode: Contains code for training models and running experiments.
• ProcessedTranscripts: Contains processed meeting transcripts (data).
• SyntheticDataset: Contains dataset files used for model training and evaluation (data).

Setting up from scratch

In order to setup the project from scratch, follow the steps described in the folders following the order requirements -> DataCollection -> PostAnnotationProcessing -> qaCode. The appropriate data files and folders will be created in the process starting with ProcessedTranscripts followed by AllData.

Reference

Please cite our paper if you use our repository and/or dataset in your works:

@article{prasad2023meeting,
  author    = {Archiki Prasad, Trung Bui, Seunghyun Yoon,  Hanieh Deilamsalehy, Franck Dernoncourt, and Mohit Bansal},
  title     = {MeetingQA: Extractive Question-Answering on Meeting Transcripts},
  journal   = {61st Annual Meeting of the Association for Computational Linguistics (ACL)},
  year      = {2023}