FiTs: Fine-grained Two-stage Training for Knowledge-aware Question Answering

• URL: http://arxiv.org/abs/2302.11799v1
• Date: Thu, 23 Feb 2023 06:25:51 GMT
• Title: FiTs: Fine-grained Two-stage Training for Knowledge-aware Question Answering
• Authors: Qichen Ye, Bowen Cao, Nuo Chen, Weiyuan Xu, Yuexian Zou
• Abstract summary: We propose a Fine-grained Two-stage training framework (FiTs) to boost the KAQA system performance. The first stage aims at aligning representations from the PLM and the KG, thus bridging the modality gaps between them. The second stage, called knowledge-aware fine-tuning, aims to improve the model's joint reasoning ability.
• Score: 47.495991137191425
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge-aware question answering (KAQA) requires the model to answer questions over a knowledge base, which is essential for both open-domain QA and domain-specific QA, especially when language models alone cannot provide all the knowledge needed. Despite the promising result of recent KAQA systems which tend to integrate linguistic knowledge from pre-trained language models (PLM) and factual knowledge from knowledge graphs (KG) to answer complex questions, a bottleneck exists in effectively fusing the representations from PLMs and KGs because of (i) the semantic and distributional gaps between them, and (ii) the difficulties in joint reasoning over the provided knowledge from both modalities. To address the above two problems, we propose a Fine-grained Two-stage training framework (FiTs) to boost the KAQA system performance: The first stage aims at aligning representations from the PLM and the KG, thus bridging the modality gaps between them, named knowledge adaptive post-training. The second stage, called knowledge-aware fine-tuning, aims to improve the model's joint reasoning ability based on the aligned representations. In detail, we fine-tune the post-trained model via two auxiliary self-supervised tasks in addition to the QA supervision. Extensive experiments demonstrate that our approach achieves state-of-the-art performance on three benchmarks in the commonsense reasoning (i.e., CommonsenseQA, OpenbookQA) and medical question answering (i.e., MedQA-USMILE) domains.

Related papers

• Knowledge Condensation and Reasoning for Knowledge-based VQA [20.808840633377343]
  Recent studies retrieve the knowledge passages from external knowledge bases and then use them to answer questions. We propose two synergistic models: Knowledge Condensation model and Knowledge Reasoning model. Our method achieves state-of-the-art performance on knowledge-based VQA datasets.
  arXiv  Detail & Related papers  (2024-03-15T06:06:06Z)
• Merging Generated and Retrieved Knowledge for Open-Domain QA [72.42262579925911]
  COMBO is a compatibility-Oriented knowledge Merging for Better Open-domain QA framework. We show that COMBO outperforms competitive baselines on three out of four tested open-domain QA benchmarks.
  arXiv  Detail & Related papers  (2023-10-22T19:37:06Z)
• Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question Answering [30.858737348472626]
  Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. Recent works have resorted to using a powerful large language model (LLM) as an implicit knowledge engine to acquire the necessary knowledge for answering. We present a conceptually simple, flexible, and general framework designed to prompt LLM with answers for knowledge-based VQA.
  arXiv  Detail & Related papers  (2023-03-03T13:05:15Z)
• Utilizing Background Knowledge for Robust Reasoning over Traffic Situations [63.45021731775964]
  We focus on a complementary research aspect of Intelligent Transportation: traffic understanding. We scope our study to text-based methods and datasets given the abundant commonsense knowledge. We adopt three knowledge-driven approaches for zero-shot QA over traffic situations.
  arXiv  Detail & Related papers  (2022-12-04T09:17:24Z)
• VQA-GNN: Reasoning with Multimodal Knowledge via Graph Neural Networks for Visual Question Answering [79.22069768972207]
  We propose VQA-GNN, a new VQA model that performs bidirectional fusion between unstructured and structured multimodal knowledge to obtain unified knowledge representations. Specifically, we inter-connect the scene graph and the concept graph through a super node that represents the QA context. On two challenging VQA tasks, our method outperforms strong baseline VQA methods by 3.2% on VCR and 4.6% on GQA, suggesting its strength in performing concept-level reasoning.
  arXiv  Detail & Related papers  (2022-05-23T17:55:34Z)
• 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)
• QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering [122.84513233992422]
  We propose a new model, QA-GNN, which addresses the problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) We show its improvement over existing LM and LM+KG models, as well as its capability to perform interpretable and structured reasoning.
  arXiv  Detail & Related papers  (2021-04-13T17:32:51Z)
• KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA [107.7091094498848]
  One of the most challenging question types in VQA is when answering the question requires outside knowledge not present in the image. In this work we study open-domain knowledge, the setting when the knowledge required to answer a question is not given/annotated, neither at training nor test time. We tap into two types of knowledge representations and reasoning. First, implicit knowledge which can be learned effectively from unsupervised language pre-training and supervised training data with transformer-based models.
  arXiv  Detail & Related papers  (2020-12-20T20:13:02Z)

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.