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Hurdles to Progress in Long-form Question Answering

This repository contains the official scripts and datasets accompanying our NAACL 2021 paper, "Hurdles to Progress in Long-form Question Answering". This repository supports inference from the pretrained retriever / generator & includes evaluation scripts.

Specifically, this codebase contains the model checkpoints, inference scripts for the retriever / generator model, generated outputs from model using c-REALM retrievals and random retrievals, scripts to compute ROUGE-L / R-Prec scores using the generations, scripts for question paraphrase classification, scripts for computing ROUGE-L bounds. You can also find the original Routing Transformer model's codebase here.

Setup

python -m virtualenv lfqa-venv
source lfqa-venv/bin/activate
pip install transformers
pip install tensor2tensor

For GPU support, you might need to change the version of your TensorFlow depending on the CUDA / CuDNN installation (details). GPU support is strongly recommended for faster inference.

Model Checkpoints & Generations

Routing Transformer finetuned on ELI5: link
c-REALM TF Hub model + encoded retrieval corpora: link
c-REALM tokenized KILT Wikipedia data: link
c-REALM tokenized ELI5 training data: link
Pre-computed generations & QQP classifier: link

The original Routing Transformer model (pretrained on PG-19) and a local attention version of it can be found in the main repository (link).

Generating from the Routing Transformer

(We have provided the pre-computed retrievals from c-REALM on ELI5, so no need to run the c-REALM retriever)

  1. Download the "Routing Transformer finetuned on ELI5" model listed above and place it inside models.
wget https://storage.googleapis.com/rt-checkpoint/eli5_checkpoint.zip
unzip eli5_checkpoint.zip -d models
rm eli5_checkpoint.zip

  1. Download the generations folder from the Google Drive link listed as "Pre-computed generations & QQP classifier" above.

  2. Run eval_generate_eli5.py to generate from the model. We have provided c-REALM retrieval outputs in the script for the ELI5 validation / test split. For custom inputs, you will need to load the retriever and wikipedia corpus (see next section). Generation is on the slower side (~4 minutes per ELI5 QA pair on a 1080ti GPU), we hope to switch to the faster decoding mode in the Routing Transformer model in the near future.

Retrievals from c-REALM

(This script only tests the retriever, it doesn't depend on the Routing Transformer generator model)

  1. Download the "c-REALM TF Hub model + encoded retrieval corpora" model listed above. Place it inside the models folder.
wget https://storage.googleapis.com/rt-checkpoint/retriever.zip
unzip retriever.zip -d models
rm retriever.zip
  1. Download "c-REALM tokenized KILT Wikipedia data" if you are interested in retrieving from the KILT Wikipedia corpus and/or "c-REALM tokenized ELI5 training data" if you are interested in retrieving question paraphrases from the ELI5 training set. Place them inside the models folder.
wget https://storage.googleapis.com/rt-checkpoint/eli5_retrieval_train.zip
unzip eli5_retrieval_train.zip -d models
rm eli5_retrieval_train.zip
  1. Run eval_retriever_eli5.py to retrieve using c-REALM. Modify the --retrieval_corpus flag to choose the retrieval corpus.

Evaluation of Outputs

Setup

  1. Download the generations folder from the Google Drive link into this root folder.

  2. Clone the KILT repository in this folder and run the installation in a virtual environment.

git clone https://github.com/facebookresearch/KILT
cd KILT
virtualenv -p python3.7 kilt-venv
pip install -r requirements.txt
pip install --editable .

  1. If you are interested in using the Quora Question Paraphrase classifier (used in Section 3.2 of the paper), download the roberta-large-finetuned-qqp folder from "Pre-computed generations & QQP classifier" listed above. This model was built by Tu Vu.

  2. Download the ELI5 train, validation and test splits.

cd KILT
wget http://dl.fbaipublicfiles.com/KILT/eli5-train-kilt.jsonl -O train.jsonl
wget http://dl.fbaipublicfiles.com/KILT/eli5-dev-kilt.jsonl -O valid.jsonl
wget http://dl.fbaipublicfiles.com/KILT/eli5-test_without_answers-kilt.jsonl -O test.jsonl

Running evaluations

Enter the KILT folder and run the following command for evaluating p=0.6 with c-REALM retrievals on the validation set:

python kilt/eval_downstream.py ../generations/final_guess_eli5_0.6_predicted_retrieval.jsonl ../generations/final_gold_eli5_0.6_predicted_retrieval.jsonl

which should give you the output (partly reported in Table 6 of the paper),

{   'downstream': {   'accuracy': 0.0,
                      'em': 0.0,
                      'f1': 0.25566078582652935,
                      'rougel': 0.24417152125142375},
    'kilt': {   'KILT-accuracy': 0.0,
                'KILT-em': 0.0,
                'KILT-f1': 0.03414819887348917,
                'KILT-rougel': 0.03205580975169385},
    'retrieval': {'Rprec': 0.13258897418004187, 'recall@5': 0.2122586648057688}}

To evaluate other configurations, modify the paths in the command above. You can replace 0.6 with 0.9 for higher entropy generations, and replace predicted with random for randomly selected retrieval paragraphs (Hurdle #1 or Section 3.1 in the paper). Note that you should make this change for both the guess and gold files, to ensure correct alignment. We have only provided generations for the validation set since the test set answers / retrievals for ELI5 are hidden behind the KILT leaderboard.

Question paraphrase classification using QQP Classifier

In Section 3.2 of our paper, we used a Quora Question Paraphrase classifier to find question paraphrases amoung similar questions retrieved by c-REALM. To run this, make sure you have downloaded the QQP checkpoint (step 3 in Setup) and run,

python run_qqp.py --input_file generations/final_guess_eli5_0.6_similar_questions.jsonl

You should get a score of 43.6%. Note that this is a lower-bound --- qualitatively we found this classifier missed several paraphrase pairs with low lexical overlap, or cases where the retrieved training set question will have a super-set of the information needed to answer the validation set question.

Lower and Upper Bounds on ROUGE-L

Run the following to evaluate bounds on ROUGE-L. Make sure you have completed steps 1, 4 in the setup above. Scripts to evaluate other bounds involving training set retrieval coming soon!

cp generate_final_guess_bounds.py KILT/
cd KILT

# Copy input lowerbound, should get 20.0 ROUGE-L
python generate_final_guess_bounds.py --bound_type copy_input

# Random training set answer, should get 15.8-16.2 ROUGE-L depending on randomness
python generate_final_guess_bounds.py --bound_type random_train_ans

# "Performance" can be further boosted by randomly selecting from only longest answers
# for each training set question, up to ~16.7 ROUGE-L. This result is not reported in
# paper, but can be run using:
python generate_final_guess_bounds.py --bound_type random_train_ans_longest

# Longest gold answer upperbound, should get 21.2 ROUGE-L
python generate_final_guess_bounds.py --bound_type longest_gold

# Best gold answer upperbound, should get 26.2 ROUGE-L (takes a while to run, 45 min on single core)
python generate_final_guess_bounds.py --bound_type best_gold

Human Annotations

In the Drive link above, we have also added the human annotations used to generate the results in Table 3 and Table 15, under human_annotations/ab_testing. You can see the folder here. Each JSONL file corresponds to an experiment conducted in Table 15, which the folders contain the HTML files with answer pairs shown to annotators in a random order. The names of the files should be self-explanatory.

Each JSONL file contains the KILT-formatted ELI5 QA pair for validation instances for which we have annotations. Here are the description of various keys ---

  1. human_annotation_url - The URL of the HTML file shown to the annotator, a copy of this file is also present in the same folder.
  2. id - The KILT / ELI5 id of the instance.
  3. input - The question asked to the model.
  4. Which generation answers the question better? - (if present) the human annotation for this question, which will be a key in the same dictionary.
  5. Which answer is more coherent? - (if present) the human annotation for this question, which will be a key in the same dictionary.
  6. Which answer is more factually correct + sensical? - (if present) the human annotation for this question, which will be a key in the same dictionary.
  7. Two keys corresponding to the A/B for the experiment, with the answers. For the gold_vs_* experiments, the longest gold answer was shown to users.

Citation

If you found our paper or this repository useful, please cite:

@inproceedings{lfqa21,
author={Kalpesh Krishna and Aurko Roy and Mohit Iyyer},
Booktitle = {North American Association for Computational Linguistics},
Year = "2021",
Title={Hurdles to Progress in Long-form Question Answering},
}