Visual Question Answering (VQA) aims to answer the natural language question about a given image by understanding multimodal content. However, the answer quality of most existing visual-language pre-training (VLP) methods is still limited, mainly due to: \textbf{(1) \emph{Incompatibility.}} Upstream pre-training tasks are generally incompatible with downstream question answering tasks, which makes the knowledge from the language model not well transferable to downstream tasks, and greatly limits their performance in few-shot scenarios; \textbf{(2) \emph{Under-fitting.}} they generally do not integrate human priors to compensate for universal knowledge from language models, so as to fit the challenging VQA problem and generate reliable answers. To address these issues, we propose HybridPrompt, a \emph{cloze-} and \emph{verify-style} hybrid prompt framework with bridging language models and human priors in prompt tuning for VQA.
Specifically, we first modify the input questions into the \emph{cloze-style} prompts to narrow the gap between upstream pre-training tasks and downstream VQA task, which ensures that the universal knowledge in the language model can be better transferred to subsequent human prior-guided prompt tuning. Then, we imitate the cognitive process of human brain to introduce topic and sample related priors to construct a dynamic learnable prompt template for human prior-guided prompt learning. Finally, we add fixed-length learnable free-parameters to further enhance the generalizability and scalability of prompt learning in the VQA model.
Experimental results verify the effectiveness of HybridPrompt, showing that it achieves competitive performance against previous methods on widely-used VQAv2 dataset and obtains new state-of-the-art results. Our code is released at: \url{https://github.com/AnonymousPony/hybrid}.

HybridPrompt: Bridging Language Models and Human Priors in Prompt Tuning for Visual Question Answering

Accurate fact veriﬁcation depends on performing ﬁne-grained reasoning over crucial entities by capturing their latent logical relations hidden in multiple evidence clues, which is generally lacking in existing fact veriﬁcation models. In this work, we propose a novel Globalto-Local Aggregation and Fission network (GLAF) to ﬁll this gap. Instead of treating entire sentences or all semantic elements within them as nodes to construct a coarse-grained or unstructured evidence graph as in previous methods, GLAF constructs a ﬁne-grained and structured evidence graph by parsing the rambling sentences into structural triple-level reasoning clues and regarding them as graph nodes to achieve ﬁne-grained and interpretable evidence graph reasoning. Speciﬁcally, to capture latent logical relations between the clues, GLAF ﬁrst employs a local ﬁssion reasoning layer to conduct ﬁne-grained multi-hop reasoning, and then uses a global evidence aggregation layer to achieve information sharing and the interchange of evidence clues for ﬁnal claim label prediction. Experimental results on the FEVER dataset demonstrate the effectiveness of GLAF, showing that it achieves the state-of-the-art performance.

GLAF: Global-to-Local Aggregation and Fission Network for Semantic Level Fact Verification

As a more natural and intelligent interaction manner, multimodal task-oriented dialog system recently has received great attention and many remarkable progresses have been achieved. Nevertheless, almost all existing studies follow the pipeline to ﬁrst learn intra-modal features separately and then conduct simple feature concatenation or attention-based feature fusion to generate responses, which hampers them from learning inter-modal interactions and conducting crossmodal feature alignment for generating more intention-aware responses. To address these issues, we propose UniTranSeR, a Uniﬁed Transformer Semantic Representation framework with feature alignment and intention reasoning for multimodal dialog systems. Specifically, we ﬁrst embed the multimodal features into a uniﬁed Transformer semantic space to prompt inter-modal interactions, and then devise a feature alignment and intention reasoning (FAIR) layer to perform cross-modal entity alignment and ﬁne-grained key-value reasoning, so as to effectively identify user’s intention for generating more accurate responses. Experimental results verify the effectiveness of UniTranSeR, showing that it signiﬁcantly outperforms state-of-the-art approaches on the representative MMD dataset.

UniTranSeR: A Unified Transformer Semantic Representation Framework for Multimodal Task-Oriented Dialog  System

Recent years has witnessed the remarkable success in end-to-end task-oriented dialog system, especially when incorporating external knowledge information. However, the quality of most existing models’ generated response is still limited, mainly due to their lack of fine-grained reasoning on deterministic knowledge (w.r.t. conceptual tokens), which makes them difficult to capture the concept shifts and identify user’s real intention in cross-task scenarios. To address these issues, we propose a novel intention mechanism to better model deterministic entity knowledge. Based on such a mechanism, we further propose an intention reasoning network (IR-Net), which consists of joint and multi-hop reasoning, to obtain intention-aware representations of conceptual tokens that can be used to capture the concept shifts involved in task-oriented conversations, so as to effectively identify user’s intention and generate more accurate responses. Experimental results verify the effectiveness of IR-Net, showing that it achieves the state-of-the-art performance on two representative multi-domain dialog datasets.

Zhiyuan Ma

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