论文标题
通过结构化知识和统一的检索生成来增强多模式和多跳的问题回答
Enhancing Multi-modal and Multi-hop Question Answering via Structured Knowledge and Unified Retrieval-Generation
论文作者
论文摘要
多模式的多跳跃问题回答涉及通过对来自不同模式的多个输入来源进行推理来回答问题。现有方法通常会分别检索证据，然后使用语言模型根据检索的证据生成答案，因此不能充分连接候选人，并且无法在检索过程中建模相互依赖的关系。此外，当检索性能较低时，管道的检索和发电方法可能会导致发电性能差。为了解决这些问题，我们提出了一种结构化的知识和统一的检索生成（SKURG）方法。 Skurg使用以实体为中心的融合编码器使用共享实体来对齐不同模式的来源。然后，它使用统一的检索生成解码器来整合答案生成的中间检索结果，并自适应地确定检索步骤的数量。在两个代表性的多模式多跳跃QA数据集MultimodalQA和WebQA上进行的广泛实验表明，Skurg在源检索和答案生成性能中以更少的参数均优于最先进的模型。我们的代码可在https://github.com/hitsz-tmg/skurg上找到。
Multi-modal multi-hop question answering involves answering a question by reasoning over multiple input sources from different modalities. Existing methods often retrieve evidences separately and then use a language model to generate an answer based on the retrieved evidences, and thus do not adequately connect candidates and are unable to model the interdependent relations during retrieval. Moreover, the pipelined approaches of retrieval and generation might result in poor generation performance when retrieval performance is low. To address these issues, we propose a Structured Knowledge and Unified Retrieval-Generation (SKURG) approach. SKURG employs an Entity-centered Fusion Encoder to align sources from different modalities using shared entities. It then uses a unified Retrieval-Generation Decoder to integrate intermediate retrieval results for answer generation and also adaptively determine the number of retrieval steps. Extensive experiments on two representative multi-modal multi-hop QA datasets MultimodalQA and WebQA demonstrate that SKURG outperforms the state-of-the-art models in both source retrieval and answer generation performance with fewer parameters. Our code is available at https://github.com/HITsz-TMG/SKURG.
