@inproceedings{sun-etal-2024-efficient-accurate,
title = "Efficient and Accurate Contextual Re-Ranking for Knowledge Graph Question Answering",
author = "Sun, Kexuan and
Jedema, Nicolaas Paul and
Sharma, Karishma and
Janssen, Ruben and
Pujara, Jay and
Szekely, Pedro and
Moschitti, Alessandro",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.496",
pages = "5585--5595",
abstract = "The efficacy of neural {``}retrieve and generate{''} systems is well established for question answering (QA) over unstructured text. Recent efforts seek to extend this approach to knowledge graph (KG) QA by converting structured triples to unstructured text. However, the relevance of KG triples retrieved by these systems limits their accuracy. In this paper, we improve the relevance of retrieved triples using a carefully designed re-ranker. Specifically, our pipeline (i) retrieves over documents of triples grouped by entity, (ii) re-ranks triples from these documents with context: triples in the 1-hop neighborhood of the documents{'} subject entity, and (iii) generates an answer from highly relevant re-ranked triples. To train our re-ranker, we propose a novel {``}triple-level{''} labeling strategy that infers fine-grained labels and shows that these significantly improve the relevance of retrieved information. We show that the resulting {``}retrieve, re-rank, and generate{''} pipeline significantly improves upon prior KGQA systems, achieving a new state-of-the-art on FreebaseQA by 5.56{\%} Exact Match. We perform multiple ablations that reveal the distinct benefits of our contextual re-ranker and labeling strategy and conclude with a case study that highlights opportunities for future works.",
}
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<abstract>The efficacy of neural “retrieve and generate” systems is well established for question answering (QA) over unstructured text. Recent efforts seek to extend this approach to knowledge graph (KG) QA by converting structured triples to unstructured text. However, the relevance of KG triples retrieved by these systems limits their accuracy. In this paper, we improve the relevance of retrieved triples using a carefully designed re-ranker. Specifically, our pipeline (i) retrieves over documents of triples grouped by entity, (ii) re-ranks triples from these documents with context: triples in the 1-hop neighborhood of the documents’ subject entity, and (iii) generates an answer from highly relevant re-ranked triples. To train our re-ranker, we propose a novel “triple-level” labeling strategy that infers fine-grained labels and shows that these significantly improve the relevance of retrieved information. We show that the resulting “retrieve, re-rank, and generate” pipeline significantly improves upon prior KGQA systems, achieving a new state-of-the-art on FreebaseQA by 5.56% Exact Match. We perform multiple ablations that reveal the distinct benefits of our contextual re-ranker and labeling strategy and conclude with a case study that highlights opportunities for future works.</abstract>
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%0 Conference Proceedings
%T Efficient and Accurate Contextual Re-Ranking for Knowledge Graph Question Answering
%A Sun, Kexuan
%A Jedema, Nicolaas Paul
%A Sharma, Karishma
%A Janssen, Ruben
%A Pujara, Jay
%A Szekely, Pedro
%A Moschitti, Alessandro
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F sun-etal-2024-efficient-accurate
%X The efficacy of neural “retrieve and generate” systems is well established for question answering (QA) over unstructured text. Recent efforts seek to extend this approach to knowledge graph (KG) QA by converting structured triples to unstructured text. However, the relevance of KG triples retrieved by these systems limits their accuracy. In this paper, we improve the relevance of retrieved triples using a carefully designed re-ranker. Specifically, our pipeline (i) retrieves over documents of triples grouped by entity, (ii) re-ranks triples from these documents with context: triples in the 1-hop neighborhood of the documents’ subject entity, and (iii) generates an answer from highly relevant re-ranked triples. To train our re-ranker, we propose a novel “triple-level” labeling strategy that infers fine-grained labels and shows that these significantly improve the relevance of retrieved information. We show that the resulting “retrieve, re-rank, and generate” pipeline significantly improves upon prior KGQA systems, achieving a new state-of-the-art on FreebaseQA by 5.56% Exact Match. We perform multiple ablations that reveal the distinct benefits of our contextual re-ranker and labeling strategy and conclude with a case study that highlights opportunities for future works.
%U https://aclanthology.org/2024.lrec-main.496
%P 5585-5595
Markdown (Informal)
[Efficient and Accurate Contextual Re-Ranking for Knowledge Graph Question Answering](https://aclanthology.org/2024.lrec-main.496) (Sun et al., LREC-COLING 2024)
ACL
- Kexuan Sun, Nicolaas Paul Jedema, Karishma Sharma, Ruben Janssen, Jay Pujara, Pedro Szekely, and Alessandro Moschitti. 2024. Efficient and Accurate Contextual Re-Ranking for Knowledge Graph Question Answering. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 5585–5595, Torino, Italia. ELRA and ICCL.