RNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering

Authors: Xi Ye, Semih Yavuz, Kazuma Hashimoto, Yingbo Zhou and Caiming Xiong

Abstract

main figure

Existing KBQA approaches, despite achieving strong performance on i.i.d. test data, often struggle in generalizing to questions involving unseen KB schema items. Prior rankingbased approaches have shown some success in generalization, but suffer from the coverage issue. We present RnG-KBQA, a Rank-andGenerate approach for KBQA, which remedies the coverage issue with a generation model while preserving a strong generalization capability. Our approach first uses a contrastive ranker to rank a set of candidate logical forms obtained by searching over the knowledge graph. It then introduces a tailored generation model conditioned on the question and the top-ranked candidates to compose the final logical form. We achieve new state-ofthe-art results on GRAILQA and WEBQSP datasets. In particular, our method surpasses the prior state-of-the-art by a large margin on the GRAILQA leaderboard. In addition, RnGKBQA outperforms all prior approaches on the popular WEBQSP benchmark, even including the ones that use the oracle entity linking. The experimental results demonstrate the effectiveness of the interplay between ranking and generation, which leads to the superior performance of our proposed approach across all settings with especially strong improvements in zero-shot generalization.

Paper link: https://arxiv.org/pdf/2109.08678.pdf

Requirements

The code is tested under the following environment setup

  • python==3.8.10
  • pytorch==1.7.0
  • transformers==3.3.1
  • spacy==3.1.1
  • other requirments please see requirements.txt

System requirements:

It’s recommended to use a machine with over 300G memory to train the models, and use a machine with 128G memory for inference. However, 256G memory will still be sufficient for runing inference and training all of the models (some tricks for saving memorry is needed in training ranker model for GrailQA).

General Setup

Setup Experiment Directory

Before Running the scripts, please use the setup.sh to setup the experiment folder. Basically it creates some symbolic links in each exp directory.

Setup Freebase

All of the datasets use Freebase as the knowledge source. Please follow Freebase Setup to set up a Virtuoso triplestore service. If you modify the default url, you may need to change the url in /framework/executor/sparql_executor.py accordingly, after starting your virtuoso service,

Reproducing the Results on GrailQA

Please use /GrailQA as the working directory when running experiments on GrailQA.

Prepare dataset and pretrained checkpoints

Dataset

Please download the dataset and put the them under outputs so as to organize dataset as outputs/grailqa_v1.0_train/dev/test.json. (Please rename test-public split to test split).

NER Checkpoints

We use the NER system (under directory entity_linking and entity_linker) from Original GrailQA Code Repo. Please use the following instructions (copied from oringinal repo) to pull related data

Other Checkpoints

Please download the following checkpoints for entity disambiguation, candidate ranking, and augmented generation checkpoints, unzip and put them under checkpoints/ directory

KB Cache

We attach the cache of query results from KB, which can help save some time. Please download the cache file for grailqa, unzip and put them under cache/, so that we have cache/grail-LinkedRelation.bin and cache/grail-TwoHopPath.bin in the place.

Running inference

Demo for Checking the Pipeline

It’s recommended to use the one-click demo scripts first to test if everything mentioned above is setup correctly. If it successfully run through, you’ll get a final F1 of around 0.86. Please make sure you successfully reproduce the results on this small demo set first, as inference on dev and test can take a long time.

sh scripts/walk_through_demo.sh

Step by Step Instructions

We also provide step-by-step inference instructions as below:

(i) Detecting Entities

Once having the entity linker ready, run

python detect_entity_mention.py --split

# eg. --split test

This will write entity mentions to outputs/grail_

_entities.json
, we extract up to 10 entities for each mention, which will be further disambiguate in the next step.

!! Running entity detection for the first time will require building surface form index, which can take a long time (but it’s only needed for the first time).

(ii) Disambiguating Entities (Entity Linking)

We have provided pretrained ranker model

sh scripts/run_disamb.sh predict

E.g., sh scripts/run_disamb.sh predict checkpoints/grail_bert_entity_disamb test

This will write the prediction results (in the form of selected entity index for each mention) to misc/grail_

_entity_linking.json
.

(iii) Enumerating Logical Form Candidates

python enumerate_candidates.py --split

--pred_file

E.g., python enumerate_candidates.py --split test --pred_file misc/grail_test_entity_linking.json.

This will write enumerated candidates to outputs/grail_

_candidates-ranking.jsonline
.

(iv) Running Ranker

sh scripts/run_ranker.sh predict

E.g., sh scripts/run_ranker.sh predict checkpoints/grail_bert_ranking test

This will write prediction candidate logits (the logits of each candidate for each example) to misc/grail_

_candidates_logits.bin
, and prediction result (in original GrailQA prediction format) to misc/grail_

_ranker_results.txt

You may evaluate the ranker results by python grail_evaluate.py

E.g., python grail_evaluate.py outputs/grailqa_v1.0_dev.json misc/grail_dev_ranker_results.txt

(v) Running Generator

First, make prepare generation model inputs

python make_generation_dataset.py --split

--logit_file

E.g., python make_generation_dataset.py --split test --logit_file misc/grail_test_candidate_logits.bin.

This will read the canddiates and the use logits to select top-k candidates and write generation model inputs to outputs/grail_

_gen.json
.

Second, run generation model to get the top-k prediction

sh scripts/run_gen.sh predict

E.g., sh scripts/run_gen.sh predict checkpoints/grail_t5_generation test.

This will generate top-k decoded logical forms stored at misc/grail_

_topk_generations.json
.

(vi) Final Inference Steps

Having the decoded top-k predictions, we’ll go down the top-k list, execute the logical form one by one until we find one logical form return valid answers.

python eval_topk_prediction.py --split

--pred_file

E.g., python eval_topk_prediction.py --split test --pred_file misc/grail_test_topk_generations.json

prediction result (in original GrailQA prediction format) to misc/grail_

_final_results.txt
.

You can then use official GrailQA evaluate script to run evaluation

python grail_evaluate.py

E.g., python grail_evaluate.py outputs/grailqa_v1.0_dev.json misc/grail_dev_final_results.txt

Training Models

We already attached pretrained-models ready for running inference. If you’d like to train your own models please checkout the README at /GrailQA folder.

Reproducing the Results on WebQSP

Please use /WebQSP as the working directory when running experiments on WebQSP.

Prepare dataset and pretrained checkpoints

Dataset

Please download the WebQSP dataset and put the them under outputs so as to organize dataset as outputs/WebQSP.train[test].json.

Evaluation Script

Please make a copy of the official evaluation script (eval/eval.py in the WebQSP zip file) and put the script under this directory (WebQSP) with the name legacy_eval.py.

Model Checkpoints

Please download the following checkpoints for candidate ranking, and augmented generation checkpoints, unzip and put them under checkpoints/ directory

KB Cache

Please download the cache file for webqsp, unzip and put them under cache/ so that we have cache/webqsp-LinkedRelation.bin and cache/webqsp-TwoHopPath.bin in the place.

Running inference

(i) Parsing Sparql-Query to S-Expression

As stated in the paper, we generate s-expressions, which is not provided by the original dataset, so we provide scripts to parse sparql-query to s-expressions.

Run python parse_sparql.py, which will augment original dataset files with s-expressions and save them in outputs as outputs/WebQSP.train.expr.json and outputs/WebQSP.dev.expr.json. Since there is no validation set, we further randomly select 200 examples from the training set for validation, yielding ptrain split and pdev split.

(ii) Entity Detection and Linking using ELQ

This step can be skipped, as we’ve already include outputs of this step (misc/webqsp_train_elq-5_mid.json, outputs/webqsp_test_elq-5_mid.json).

The scripts and config of ELQ model can be found in elq_linking/run_elq_linker.py. If you’d like to use the script to run entity linking, please copy the run_elq_linker.py python script to ELQ model and run the script there.

(iii) Enumerating Logical Form Candidates

python enumerate_candidates.py --split test

This will write enumerated candidates to outputs/webqsp_test_candidates-ranking.jsonline.

(iv) Runing Ranker

sh scripts/run_ranker.sh predict checkpoints/webqsp_bert_ranking test

This will write prediction candidate logits (the logits of each candidate for each example) to misc/webqsp_test_candidates_logits.bin, and prediction result (in original GrailQA prediction format) to misc/webqsp_test_ranker_results.txt

(v) Running Generator

First, make prepare generation model inputs

python make_generation_dataset.py --split test --logit_file misc/webqsp_test_candidate_logits.bin.

This will read the candidates and the use logits to select top-k candidates and write generation model inputs to outputs/webqsp_test_gen.json.

Second, run generation model to get the top-k prediction

sh scripts/run_gen.sh predict checkpoints/webqsp_t5_generation test

This will generate top-k decoded logical forms stored at misc/webqsp_test_topk_generations.json.

(vi) Final Inference Steps

Having the decoded top-k predictions, we’ll go down the top-k list, execute the logical form one by one until we find one logical form return valid answers.

python eval_topk_prediction.py --split test --pred_file misc/webqsp_test_topk_generations.json

Prediction result will be stored (in GrailQA prediction format) to misc/webqsp_test_final_results.txt.

You can then use official WebQSP (only modified in I/O) evaluate script to run evaluation

python webqsp_evaluate.py outputs/WebQSP.test.json misc/webqsp_test_final_results.txt.

Training Models

We already attached pretrained-models ready for running inference. If you’d like to train your own models please checkout the README at /WebQSP folder.

Citation

@misc{ye2021rngkbqa,
    title={RnG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering}, 
    author={Xi Ye and Semih Yavuz and Kazuma Hashimoto and Yingbo Zhou and Caiming Xiong},
    year={2021},
    eprint={2109.08678},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

Questions?

For any questions, feel free to open issues, or shoot emails to

License

The code is released under BSD 3-Clause – see LICENSE for details.

GitHub

https://github.com/salesforce/rng-kbqa