Injecting Numerical Reasoning Skills into Knowledge Base Question Answering Models

• URL: http://arxiv.org/abs/2112.06109v1
• Date: Sun, 12 Dec 2021 01:30:29 GMT
• Title: Injecting Numerical Reasoning Skills into Knowledge Base Question Answering Models
• Authors: Yu Feng, Jing Zhang, Xiaokang Zhang, Lemao Liu, Cuiping Li, Hong Chen
• Abstract summary: This paper proposes a new embedding-based KBQA framework which takes numerical reasoning into account. We present NumericalTransformer on top of NSM, a state-of-the-art embedding-based KBQA model, to create NT-NSM. Experiments on KBQA benchmarks demonstrate that NT-NSM is empowered with numerical reasoning skills and substantially outperforms the baselines in answering ordinal constrained questions.
• Score: 19.964729281684363
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Embedding-based methods are popular for Knowledge Base Question Answering (KBQA), but few current models have numerical reasoning skills and thus struggle to answer ordinal constrained questions. This paper proposes a new embedding-based KBQA framework which particularly takes numerical reasoning into account. We present NumericalTransformer on top of NSM, a state-of-the-art embedding-based KBQA model, to create NT-NSM. To enable better training, we propose two pre-training tasks with explicit numerical-oriented loss functions on two generated training datasets and a template-based data augmentation method for enriching ordinal constrained QA dataset. Extensive experiments on KBQA benchmarks demonstrate that with the help of our training algorithm, NT-NSM is empowered with numerical reasoning skills and substantially outperforms the baselines in answering ordinal constrained questions.

Related papers

• Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context Learning [20.80841972133938]
  Existing Knowledge Base Question Answering (KBQA) architectures are hungry for annotated data. We introduce the problem of few-shot transfer learning for KBQA, where the target domain offers only a few labeled examples. We propose a novel KBQA architecture called FuSIC-KBQA that performs KB-retrieval using multiple source-trained retrievers.
  arXiv  Detail & Related papers  (2023-11-15T11:56:56Z)
• ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models [19.85526116658481]
  We introduce ChatKBQA, a novel and simple generate-then-retrieve KBQA framework. Experimental results show that ChatKBQA achieves new state-of-the-art performance on standard KBQA datasets. This work can also be regarded as a new paradigm for combining LLMs with knowledge graphs for interpretable and knowledge-required question answering.
  arXiv  Detail & Related papers  (2023-10-13T09:45:14Z)
• Few-shot In-context Learning for Knowledge Base Question Answering [31.73274700847965]
  We propose KB-BINDER, which for the first time enables few-shot in-context learning over KBQA tasks. The experimental results on four public heterogeneous KBQA datasets show that KB-BINDER can achieve a strong performance with only a few in-context demonstrations.
  arXiv  Detail & Related papers  (2023-05-02T19:31:55Z)
• Improving Visual Question Answering Models through Robustness Analysis and In-Context Learning with a Chain of Basic Questions [70.70725223310401]
  This work proposes a new method that utilizes semantically related questions, referred to as basic questions, acting as noise to evaluate the robustness of VQA models. The experimental results demonstrate that the proposed evaluation method effectively analyzes the robustness of VQA models.
  arXiv  Detail & Related papers  (2023-04-06T15:32:35Z)
• Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering [38.071375112873675]
  We propose a question-answer augmented encoder-decoder model and accompanying pretraining strategy. This yields an end-to-end system that outperforms prior QA retrieval methods on single-hop QA tasks.
  arXiv  Detail & Related papers  (2022-04-10T02:33:00Z)
• SYGMA: System for Generalizable Modular Question Answering OverKnowledge Bases [57.89642289610301]
  We present SYGMA, a modular approach facilitating general-izability across multiple knowledge bases and multiple rea-soning types. We demonstrate effectiveness of our system by evaluating on datasets belonging to two distinct knowledge bases,DBpedia and Wikidata.
  arXiv  Detail & Related papers  (2021-09-28T01:57:56Z)
• Question Answering over Knowledge Bases by Leveraging Semantic Parsing and Neuro-Symbolic Reasoning [73.00049753292316]
  We propose a semantic parsing and reasoning-based Neuro-Symbolic Question Answering(NSQA) system. NSQA achieves state-of-the-art performance on QALD-9 and LC-QuAD 1.0.
  arXiv  Detail & Related papers  (2020-12-03T05:17:55Z)
• Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases [63.43418760818188]
  We release a new large-scale, high-quality dataset with 64,331 questions, GrailQA. We propose a novel BERT-based KBQA model. The combination of our dataset and model enables us to thoroughly examine and demonstrate, for the first time, the key role of pre-trained contextual embeddings like BERT in the generalization of KBQA.
  arXiv  Detail & Related papers  (2020-11-16T06:36:26Z)
• Cross Learning in Deep Q-Networks [82.20059754270302]
  We propose a novel cross Q-learning algorithm, aim at alleviating the well-known overestimation problem in value-based reinforcement learning methods. Our algorithm builds on double Q-learning, by maintaining a set of parallel models and estimate the Q-value based on a randomly selected network.
  arXiv  Detail & Related papers  (2020-09-29T04:58:17Z)
• A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges [71.4531144086568]
  Question Answering (QA) over Knowledge Base (KB) aims to automatically answer natural language questions. Researchers have shifted their attention from simple questions to complex questions, which require more KB triples and constraint inference.
  arXiv  Detail & Related papers  (2020-07-26T07:13:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.