Compositional generalization is a key challenge in grounding natural language to visual perception. While deep learning models have achieved great success in multimodal tasks like visual question answering, recent studies have shown that they fail to generalize to new inputs that are simply an unseen combination of those seen in the training distribution. In this paper, we propose to tackle this challenge by employing neural factor graphs to induce a tighter coupling between concepts in different modalities (e.g. Graph representations are inherently compositional in nature and allow us to capture entities, attributes and relations in a scalable manner. Our model first creates a multimodal graph, processes it with a graph neural network to induce a factor correspondence matrix, and then outputs a symbolic program to predict answers to questions.

Visual question answering (VQA) is designed to examine the visual-textual reasoning ability of an intelligent agent. However, recent observations show that many VQA models may only capture the biases between questions and answers in a dataset rather than showing real reasoning abilities. For example, given a question, some VQA models tend to output the answer that occurs frequently in the dataset and ignore the images. To reduce this tendency, existing methods focus on weakening the language bias. Meanwhile, only a few works also consider vision bias implicitly.

Electronic Health Records (EHRs), which contain patients' medical histories in various multi-modal formats, often overlook the potential for joint reasoning across imaging and table modalities underexplored in current EHR Question Answering (QA) systems. In this paper, we introduce EHRXQA, a novel multi-modal question answering dataset combining structured EHRs and chest X-ray images. To develop our dataset, we first construct two uni-modal resources: 1) The MIMIC- CXR-VQA dataset, our newly created medical visual question answering (VQA) benchmark, specifically designed to augment the imaging modality in EHR QA, and 2) EHRSQL (MIMIC-IV), a refashioned version of a previously established table-based EHR QA dataset. By integrating these two uni-modal resources, we successfully construct a multi-modal EHR QA dataset that necessitates both uni-modal and cross-modal reasoning. To address the unique challenges of multi-modal questions within EHRs, we propose a NeuralSQL-based strategy equipped with an external VQA API.

Answering complex queries over knowledge graphs (KG) is an important yet challenging task because of the KG incompleteness issue and cascading errors during reasoning. Recent query embedding (QE) approaches embed the entities and relations in a KG and the first-order logic (FOL) queries into a low dimensional space, making the query can be answered by dense similarity searching. However, previous works mainly concentrate on the target answers, ignoring intermediate entities' usefulness, which is essential for relieving the cascading error problem in logical query answering. In addition, these methods are usually designed with their own geometric or distributional embeddings to handle logical operators like union, intersection, and negation, with the sacrifice of the accuracy of the basic operator -- projection, and they could not absorb other embedding methods to their models. In this work, we propose a Neural and Symbolic Entangled framework (ENeSy) for complex query answering, which enables the neural and symbolic reasoning to enhance each other to alleviate the cascading error and KG incompleteness.

Automatically determining whether a text and a corresponding image are semantically aligned is a significant challenge for vision-language models, with applications in generative text-to-image and image-to-text tasks. In this work, we study methods for automatic text-image alignment evaluation. We first introduce SeeTRUE: a comprehensive evaluation set, spanning multiple datasets from both text-to-image and image-to-text generation tasks, with human judgements for whether a given text-image pair is semantically aligned. We then describe two automatic methods to determine alignment: the first involving a pipeline based on question generation and visual question answering models, and the second employing an end-to-end classification approach by finetuning multimodal pretrained models. Both methods surpass prior approaches in various text-image alignment tasks, with significant improvements in challenging cases that involve complex composition or unnatural images.

There has been an increasing interest in detecting hallucinations in model-generated texts, both manually and automatically, at varying levels of granularity. However, most existing methods fail to precisely pinpoint the errors. In this work, we introduce QASemConsistency, a new formalism for localizing factual inconsistencies in attributable text generation, at a fine-grained level. Drawing inspiration from Neo-Davidsonian formal semantics, we propose decomposing the generated text into minimal predicate-argument level propositions, expressed as simple question-answer (QA) pairs, and assess whether each individual QA pair is supported by a trusted reference text. As each QA pair corresponds to a single semantic relation between a predicate and an argument, QASemConsistency effectively localizes the unsupported information. We first demonstrate the effectiveness of the QASemConsistency methodology for human annotation, by collecting crowdsourced annotations of granular consistency errors, while achieving a substantial inter-annotator agreement ($\kappa > 0.7)$. Then, we implement several methods for automatically detecting localized factual inconsistencies, with both supervised entailment models and open-source LLMs.

The predicted graph connectivity (at least in these few examples) looks quite intuitive and interpretable, even when the model predicts the incorrect answer. Weaknesses -- Figure 2 caption says "[insert quick recap here]":) -- The paper emphasizes multiple times that the proposed approach achieves state of the art accuracies on VQA v2, but that does not seem to be the case. The best published result so far -- the counting module by Zhang et al., ICLR 2018 -- performs 3% better than the proposed approach (as shown in Table 1 as well). This claim needs to be sufficiently toned down. Also, the proposed approach is marginally better than the base Bottom-Up architecture.

The paper introduces a novel modular neural network for multimodal tasks such as Visual Question Answering. The paper argues that a single visual representation is not sufficient for VQA and using some task specific visual features such as scene classification or object detection would result in a better VQA model. Following this motivation, the paper proposes a VQA model with modules tailored for specific tasks -- scene classification, object detection/classification, face detection/analysis -- and pushes the state-of-the-art performance. Strengths -- -- Since VQA spans many lower level vision tasks such as object detection, scene classification, etc., it makes a lot of sense that the visual features tailored for these tasks should help for the task of VQA. According to my knowledge, this is the first paper which explicitly uses this information in building their model, and shows the importance of visual features from each task in their ablation studies.

This paper proposes a semantic parsing method for dialog-based QA over a large-scale knowledge base. The method significantly outperforms the existing state of the art on CSQA, a recently-released conversational QA dataset. One of the major novelties of this paper is breaking apart the logical forms in the dialog history into smaller subsequences, any of which can be copied over into the logical form for the current question. While I do have some concerns with the method and the writing (detailed below), overall I liked this paper and I think that some of the ideas within it could be useful more broadly for QA researchers. Detailed comments: - I found many parts of the paper to be confusing, requiring multiple reads to fully understand.

This ignores the inherent graph structure of the knowledge base, and performs reasoning from facts to answer one at a time, which is computationally inefficient. Two entities have a connecting edge if they belong to the same fact. Strengths -- The proposed approach is intuitive, sufficiently novel, and outperforms prior work by a large margin -- 10% better than the previous best approach, which is an impressive result. Weaknesses -- Given that the fact retrieval step is still the bottleneck in terms of accuracy (Table 4), it would be useful to check how sensitive downstream accuracy is to the choice of retrieving 100 facts. What is the answering accuracy if 50 facts are retrieved?