Calculating Question Similarity is Enough:A New Method for KBQA Tasks

• URL: http://arxiv.org/abs/2111.07658v1
• Date: Mon, 15 Nov 2021 10:31:46 GMT
• Title: Calculating Question Similarity is Enough:A New Method for KBQA Tasks
• Authors: Hanyu Zhao, Sha Yuan, Jiahong Leng, Xiang Pan and Guoqiang Wang
• Abstract summary: This paper proposes a Corpus Generation - Retrieve Method (CGRM) with Pre-training Language Model (PLM) and Knowledge Graph (KG) Firstly, based on the mT5 model, we designed two new pre-training tasks: knowledge masked language modeling and question generation based on the paragraph. Secondly, after preprocessing triples of knowledge graph with a series of rules, the kT5 model generates natural language QA pairs based on processed triples.
• Score: 8.056701645706404
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge Base Question Answering (KBQA) aims to answer natural language questions with the help of an external knowledge base. The core idea is to find the link between the internal knowledge behind questions and known triples of the knowledge base. The KBQA task pipeline contains several steps, including entity recognition, relationship extraction, and entity linking. This kind of pipeline method means that errors in any procedure will inevitably propagate to the final prediction. In order to solve the above problem, this paper proposes a Corpus Generation - Retrieve Method (CGRM) with Pre-training Language Model (PLM) and Knowledge Graph (KG). Firstly, based on the mT5 model, we designed two new pre-training tasks: knowledge masked language modeling and question generation based on the paragraph to obtain the knowledge enhanced T5 (kT5) model. Secondly, after preprocessing triples of knowledge graph with a series of heuristic rules, the kT5 model generates natural language QA pairs based on processed triples. Finally, we directly solve the QA by retrieving the synthetic dataset. We test our method on NLPCC-ICCPOL 2016 KBQA dataset, and the results show that our framework improves the performance of KBQA and the out straight-forward method is competitive with the state-of-the-art.

Related papers

• Exploring Hint Generation Approaches in Open-Domain Question Answering [16.434748534272014]
  We introduce a novel context preparation approach called HINTQA. Unlike traditional methods, HINTQA prompts LLMs to produce hints about potential answers for the question. We demonstrate that hints enhance the accuracy of answers more than retrieved and generated contexts.
  arXiv  Detail & Related papers  (2024-09-24T13:50:32Z)
• 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)
• Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation [19.327008532572645]
  Question Generation over Knowledge Bases (KBQG) aims to convert a logical form into a natural language question. We propose Chain-of-Thought prompting, which is an in-context learning strategy for reasoning. We conduct extensive experiments over three public KBQG datasets.
  arXiv  Detail & Related papers  (2023-10-12T15:08:14Z)
• An Empirical Comparison of LM-based Question and Answer Generation Methods [79.31199020420827]
  Question and answer generation (QAG) consists of generating a set of question-answer pairs given a context. In this paper, we establish baselines with three different QAG methodologies that leverage sequence-to-sequence language model (LM) fine-tuning. Experiments show that an end-to-end QAG model, which is computationally light at both training and inference times, is generally robust and outperforms other more convoluted approaches.
  arXiv  Detail & Related papers  (2023-05-26T14:59:53Z)
• UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph [89.98762327725112]
  Multi-hop Question Answering over Knowledge Graph(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question. We propose UniKGQA, a novel approach for multi-hop KGQA task, by unifying retrieval and reasoning in both model architecture and parameter learning.
  arXiv  Detail & Related papers  (2022-12-02T04:08:09Z)
• Improving Unsupervised Question Answering via Summarization-Informed Question Generation [47.96911338198302]
  Question Generation (QG) is the task of generating a plausible question for a passage, answer> pair. We make use of freely available news summary data, transforming declarative sentences into appropriate questions using dependency parsing, named entity recognition and semantic role labeling. The resulting questions are then combined with the original news articles to train an end-to-end neural QG model.
  arXiv  Detail & Related papers  (2021-09-16T13:08:43Z)
• Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning [55.08037694027792]
  Complex question-answering (CQA) involves answering complex natural-language questions on a knowledge base (KB) The conventional neural program induction (NPI) approach exhibits uneven performance when the questions have different types. This paper proposes a meta-reinforcement learning approach to program induction in CQA to tackle the potential distributional bias in questions.
  arXiv  Detail & Related papers  (2020-10-29T18:34:55Z)
• 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)
• Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering [98.48363619128108]
  We propose an unsupervised approach to training QA models with generated pseudo-training data. We show that generating questions for QA training by applying a simple template on a related, retrieved sentence rather than the original context sentence improves downstream QA performance.
  arXiv  Detail & Related papers  (2020-04-24T17:57:45Z)

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.