Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learning

• URL: http://arxiv.org/abs/2010.15875v1
• Date: Thu, 29 Oct 2020 18:28:16 GMT
• Title: Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learning
• Authors: Yuncheng Hua, Yuan-Fang Li, Gholamreza Haffari, Guilin Qi and Wei Wu
• Abstract summary: We present a novel method that automatically learns a retrieval model alternately with the programmer from weak supervision. Our system leads to state-of-the-art performance on a large-scale task for complex question answering over knowledge bases.
• Score: 56.771557756836906
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A compelling approach to complex question answering is to convert the question to a sequence of actions, which can then be executed on the knowledge base to yield the answer, aka the programmer-interpreter approach. Use similar training questions to the test question, meta-learning enables the programmer to adapt to unseen questions to tackle potential distributional biases quickly. However, this comes at the cost of manually labeling similar questions to learn a retrieval model, which is tedious and expensive. In this paper, we present a novel method that automatically learns a retrieval model alternately with the programmer from weak supervision, i.e., the system's performance with respect to the produced answers. To the best of our knowledge, this is the first attempt to train the retrieval model with the programmer jointly. Our system leads to state-of-the-art performance on a large-scale task for complex question answering over knowledge bases. We have released our code at https://github.com/DevinJake/MARL.

Related papers

• Multimodal Reranking for Knowledge-Intensive Visual Question Answering [77.24401833951096]
  We introduce a multi-modal reranker to improve the ranking quality of knowledge candidates for answer generation. Experiments on OK-VQA and A-OKVQA show that multi-modal reranker from distant supervision provides consistent improvements.
  arXiv  Detail & Related papers  (2024-07-17T02:58:52Z)
• Self-Bootstrapped Visual-Language Model for Knowledge Selection and Question Answering [11.183845003492964]
  We use Passage Retrieval (DPR) to retrieve related knowledge to help the model answer questions. DPR conduct retrieving in natural language space, which may not ensure comprehensive acquisition of image information. We propose a novel framework that leverages the visual-language model to select the key knowledge retrieved by DPR and answer questions.
  arXiv  Detail & Related papers  (2024-04-22T07:44:20Z)
• Open-Set Knowledge-Based Visual Question Answering with Inference Paths [79.55742631375063]
  The purpose of Knowledge-Based Visual Question Answering (KB-VQA) is to provide a correct answer to the question with the aid of external knowledge bases. We propose a new retriever-ranker paradigm of KB-VQA, Graph pATH rankER (GATHER for brevity) Specifically, it contains graph constructing, pruning, and path-level ranking, which not only retrieves accurate answers but also provides inference paths that explain the reasoning process.
  arXiv  Detail & Related papers  (2023-10-12T09:12:50Z)
• Self-Knowledge Guided Retrieval Augmentation for Large Language Models [59.771098292611846]
  Large language models (LLMs) have shown superior performance without task-specific fine-tuning. Retrieval-based methods can offer non-parametric world knowledge and improve the performance on tasks such as question answering. Self-Knowledge guided Retrieval augmentation (SKR) is a simple yet effective method which can let LLMs refer to the questions they have previously encountered.
  arXiv  Detail & Related papers  (2023-10-08T04:22:33Z)
• 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)
• Enhancing Question Generation with Commonsense Knowledge [33.289599417096206]
  We propose a multi-task learning framework to introduce commonsense knowledge into question generation process. Experimental results on SQuAD show that our proposed methods are able to noticeably improve the QG performance on both automatic and human evaluation metrics.
  arXiv  Detail & Related papers  (2021-06-19T08:58:13Z)
• Attention-based model for predicting question relatedness on Stack Overflow [0.0]
  We propose an Attention-based Sentence pair Interaction Model (ASIM) to predict the relatedness between questions on Stack Overflow automatically. ASIM has made significant improvement over the baseline approaches in Precision, Recall, and Micro-F1 evaluation metrics. Our model also performs well in the duplicate question detection task of Ask Ubuntu.
  arXiv  Detail & Related papers  (2021-03-19T12:18:03Z)
• 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)
• Procedural Generation of STEM Quizzes [0.0]
  We argue that procedural question generation greatly facilitates the task of creating varied, formative, up-to-date, adaptive question banks for STEM quizzes. We present and evaluate a proof-of-concept Python API for script-based question generation. A side advantage of our system is that the question bank is actually embedded in Python code, making collaboration, version control, and maintenance tasks very easy.
  arXiv  Detail & Related papers  (2020-09-08T17:15:16Z)

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.