QADialogSystem
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We design models that generate conversational responses for factual questions using expert answer phrases from Question Answering systems.
QADialogSystem
We design models that generate conversational responses for factual questions using expert answer phrases from Question Answering systems. Paper: "Fluent Response Generation for Conversational Question Answering"
Setup for CoQA baseline
- Install
SMRCToolkit
for model code - Run
python run_bert_coqa.py
to train the model - Run
python evaluate_on_qa_generation_test_using_coqa.py
to get CoQA predictions on SQuAD Dev Test set
Setup for QuAC baseline
- Download and extract QuAC trained model inside the
"quac_baseline"
folder - Run
quac_baseline.py
to extract quac model responses onsquad_dev_test
Setup for SQuAD baseline
- Clone
bert
within"squad_baseline"
folder. - Checkout to specific commit by running
git checkout 88a817c37f788702a363ff935fd173b6dc6ac0d6
- Refer to
model_training_commands.txt
inside"bert"
folder for running instructions
STs+BERT baseline
- The outputs of STs+BERT baseline predictions on SQuAD Dev Test set can be found in
mturk_evaluations/data2/bert_softmax_predictions_on_squad_dev_test_0_to_822.txt
Setup for Pointer generator models
- run
git clone https://github.com/OpenNMT/OpenNMT-py
to get"OpenNMT-py"
folder within"QADialogSystem"
. - checkout to specific commit by running
git checkout 7f1fc81da864c465862f23e048802107ada714a8
from within the"OpenNMT-py"
folder To get the pretrained models -
cd OpenNMT-py
- Download zip file containing saved PGN and PGN-pre model checkpoints
-
unzip pgn_models.zip
To re-train the models - Extract
opensub_qa_en
data in"Data/Opensubtitles_qa"
- run
preprocess_opensubtitles_qa.py
in"Data/Opensubtitles_qa"
folder to moses tokenize theopensub_qa_en
data. - Follow the training commands in
all_final_model_training_and_testing_commands.txt
DialoGPT, GPT-2
Download and extract saved GPT-2, GPT-2-Pre and DGPT models in "DialoGPT"
folder as follows:
-
cd DialoGPT
- Download zip file containing saved GPT-2, GPT-2-Pre and DGPT model checkpoints fine-tuned on SS and SS+ data
-
unzip gpt_and_dgpt_models.zip
For instructions on how to run the models refer to all_final_model_training_and_testing_commands.txt
Citation
@article{baheti2020fluent,
title={Fluent Response Generation for Conversational Question Answering},
author={Baheti, Ashutosh and Ritter, Alan and Small, Kevin},
journal={arXiv preprint arXiv:2005.10464},
year={2020}
}
TODO
- Add the instructions on how to generate STs + BERT baseline predictions on SQuAD Dev Test set