Ask Me Anything: Free-form Visual Question Answering Based on Knowledge from External Sources

We propose a method for visual question answering which combines an internal representation of the content of an image with information extracted from a general knowledge base to answer a broad range of image-based questions. This allows more complex questions to be answered using the predominant neural network-based approach than has previously been possible. It particularly allows questions to be asked about the contents of an image, even when the image itself does not contain the whole answer. The method constructs a textual representation of the semantic content of an image, and merges it with textual information sourced from a knowledge base, to develop a deeper understanding of the scene viewed. Priming a recurrent neural network with this combined information, and the submitted question, leads to a very flexible visual question answering approach. We are specifically able to answer questions posed in natural language, that refer to information not contained in the image. We demonstrate the effectiveness of our model on two publicly available datasets, Toronto COCO-QA and MS COCO-VQA and show that it produces the best reported results in both cases.Comment: Accepted to IEEE Conf. Computer Vision and Pattern Recognitio

Improvements to the complex question answering models

x, 128 leaves : ill. ; 29 cmIn recent years the amount of information on the web has increased dramatically. As a result, it has become a challenge for the researchers to find effective ways that can help us query and extract meaning from these large repositories. Standard document search engines try to address the problem by presenting the users a ranked list of relevant documents. In most cases, this is not enough as the end-user has to go through the entire document to find out the answer he is looking for. Question answering, which is the retrieving of answers to natural language questions from a document collection, tries to remove the onus on the end-user by providing direct access to relevant information. This thesis is concerned with open-domain complex question answering. Unlike simple questions, complex questions cannot be answered easily as they often require inferencing and synthesizing information from multiple documents. Hence, we considered the task of complex question answering as query-focused multi-document summarization. In this thesis, to improve complex question answering we experimented with both empirical and machine learning approaches. We extracted several features of different types (i.e. lexical, lexical semantic, syntactic and semantic) for each of the sentences in the document collection in order to measure its relevancy to the user query. We have formulated the task of complex question answering using reinforcement framework, which to our best knowledge has not been applied for this task before and has the potential to improve itself by fine-tuning the feature weights from user feedback. We have also used unsupervised machine learning techniques (random walk, manifold ranking) and augmented semantic and syntactic information to improve them. Finally we experimented with question decomposition where instead of trying to find the answer of the complex question directly, we decomposed the complex question into a set of simple questions and synthesized the answers to get our final result

COMPLEX QUESTION ANSWERING BASED ON A SEMANTIC DOMAIN MODEL OF CLINICAL MEDICINE

Much research in recent years has focused on question answering. Due to significant advances in answering simple fact-seeking questions, research is moving towards resolving complex questions. An approach adopted by many researchers is to decompose a complex question into a series of fact-seeking questions and reuse techniques developed for answering simple questions. This thesis presents an alternative novel approach to domain-specific complex question answering based on consistently applying a semantic domain model to question and document understanding as well as to answer extraction and generation. This study uses a semantic domain model of clinical medicine to encode (a) a clinician's information need expressed as a question on the one hand and (b) the meaning of scientific publications on the other to yield a common representation. It is hypothesized that this approach will work well for (1) finding documents that contain answers to clinical questions and (2) extracting these answers from the documents. The domain of clinical question answering was selected primarily because of its unparalleled resources that permit providing a proof by construction for this hypothesis. In addition, a working prototype of a clinical question answering system will support research in informed clinical decision making. The proposed methodology is based on the semantic domain model developed within the paradigm of Evidence Based Medicine. Three basic components of this model - the clinical task, a framework for capturing a synopsis of a clinical scenario that generated the question, and strength of evidence presented in an answer - are identified and discussed in detail. Algorithms and methods were developed that combine knowledge-based and statistical techniques to extract the basic components of the domain model from abstracts of biomedical articles. These algorithms serve as a foundation for the prototype end-to-end clinical question answering system that was built and evaluated to test the hypotheses. Evaluation of the system on test collections developed in the course of this work and based on real life clinical questions demonstrates feasibility of complex question answering and high accuracy information retrieval using a semantic domain model

Encyclopaedic question answering

Open-domain question answering (QA) is an established NLP task which enables users to search for speciVc pieces of information in large collections of texts. Instead of using keyword-based queries and a standard information retrieval engine, QA systems allow the use of natural language questions and return the exact answer (or a list of plausible answers) with supporting snippets of text. In the past decade, open-domain QA research has been dominated by evaluation fora such as TREC and CLEF, where shallow techniques relying on information redundancy have achieved very good performance. However, this performance is generally limited to simple factoid and deVnition questions because the answer is usually explicitly present in the document collection. Current approaches are much less successful in Vnding implicit answers and are diXcult to adapt to more complex question types which are likely to be posed by users. In order to advance the Veld of QA, this thesis proposes a shift in focus from simple factoid questions to encyclopaedic questions: list questions composed of several constraints. These questions have more than one correct answer which usually cannot be extracted from one small snippet of text. To correctly interpret the question, systems need to combine classic knowledge-based approaches with advanced NLP techniques. To Vnd and extract answers, systems need to aggregate atomic facts from heterogeneous sources as opposed to simply relying on keyword-based similarity. Encyclopaedic questions promote QA systems which use basic reasoning, making them more robust and easier to extend with new types of constraints and new types of questions. A novel semantic architecture is proposed which represents a paradigm shift in open-domain QA system design, using semantic concepts and knowledge representation instead of words and information retrieval. The architecture consists of two phases, analysis – responsible for interpreting questions and Vnding answers, and feedback – responsible for interacting with the user. This architecture provides the basis for EQUAL, a semantic QA system developed as part of the thesis, which uses Wikipedia as a source of world knowledge and iii employs simple forms of open-domain inference to answer encyclopaedic questions. EQUAL combines the output of a syntactic parser with semantic information from Wikipedia to analyse questions. To address natural language ambiguity, the system builds several formal interpretations containing the constraints speciVed by the user and addresses each interpretation in parallel. To Vnd answers, the system then tests these constraints individually for each candidate answer, considering information from diUerent documents and/or sources. The correctness of an answer is not proved using a logical formalism, instead a conVdence-based measure is employed. This measure reWects the validation of constraints from raw natural language, automatically extracted entities, relations and available structured and semi-structured knowledge from Wikipedia and the Semantic Web. When searching for and validating answers, EQUAL uses the Wikipedia link graph to Vnd relevant information. This method achieves good precision and allows only pages of a certain type to be considered, but is aUected by the incompleteness of the existing markup targeted towards human readers. In order to address this, a semantic analysis module which disambiguates entities is developed to enrich Wikipedia articles with additional links to other pages. The module increases recall, enabling the system to rely more on the link structure of Wikipedia than on word-based similarity between pages. It also allows authoritative information from diUerent sources to be linked to the encyclopaedia, further enhancing the coverage of the system. The viability of the proposed approach was evaluated in an independent setting by participating in two competitions at CLEF 2008 and 2009. In both competitions, EQUAL outperformed standard textual QA systems as well as semi-automatic approaches. Having established a feasible way forward for the design of open-domain QA systems, future work will attempt to further improve performance to take advantage of recent advances in information extraction and knowledge representation, as well as by experimenting with formal reasoning and inferencing capabilities.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering

The most approaches to Knowledge Base Question Answering are based on semantic parsing. In this paper, we address the problem of learning vector representations for complex semantic parses that consist of multiple entities and relations. Previous work largely focused on selecting the correct semantic relations for a question and disregarded the structure of the semantic parse: the connections between entities and the directions of the relations. We propose to use Gated Graph Neural Networks to encode the graph structure of the semantic parse. We show on two data sets that the graph networks outperform all baseline models that do not explicitly model the structure. The error analysis confirms that our approach can successfully process complex semantic parses.Comment: Accepted as COLING 2018 Long Paper, 12 page

Visual-Semantic Learning

Visual-semantic learning is an attractive and challenging research direction aiming to understand complex semantics of heterogeneous data from two domains, i.e., visual signals (i.e., images and videos) and natural language (i.e., captions and questions). It requires memorizing the rich information in a single modality and a joint comprehension of multiple modalities. Artificial intelligence (AI) systems with human-level intelligence are claimed to learn like humans, such as efficiently leveraging brain memory for better comprehension, rationally incorporating common-sense knowledge into reasoning, quickly gaining in-depth understanding given a few samples, and analyzing relationships among abundant and informative events. However, these intelligence capacities are effortless for humans but challenging for machines. To bridge the discrepancy between human-level intelligence and present-day visual-semantic learning, we start from its basic understanding ability by studying the visual question answering (e.g., Image-QA and Video-QA) tasks from the perspectives of memory augmentation and common-sense knowledge incorporation. Furthermore, we stretch it to a more challenging situation with limited and partially unlabeled training data (i.e., Few-shot Visual-Semantic Learning) to imitate the fast learning ability of humans. Finally, to further enhance visual-semantic performance in natural videos with numerous spatio-temporal dynamics, we investigate exploiting event-correlated information for a comprehensive understanding of cross-modal semantics. To study the essential visual-semantic understanding ability of the human brain with memory, we first propose a novel Memory Augmented Deep Recurrent Neural Network (i.e., MA-DRNN) model for Video-QA, which features a new method for encoding videos and questions, and memory augmentation using the emerging Differentiable Neural Computer (i.e., DNC). Specifically, we encode semantic (i.e., questions) information before visual (i.e., videos) information, which leads to better visual-semantic representations. Moreover, we leverage Differentiable Neural Computer (with external memory) to store and retrieve valuable information in questions and videos and model the long-term visual-semantic dependency. In addition to basic understanding, to tackle visual-semantic reasoning that requires external knowledge beyond visible contents (e.g., KB-Image-QA), we propose a novel framework that endows the model with capabilities of answering more general questions and achieves better exploitation of external knowledge through generating Multiple Clues for Reasoning with Memory Neural Networks (i.e., MCR-MemNN). Specifically, a well-defined detector is adopted to predict image-question-related relation phrases, each delivering two complementary clues to retrieve the supporting facts from an external knowledge base (i.e., KB). These facts are encoded into a continuous embedding space using a content-addressable memory. Afterward, mutual interactions between visual-semantic representation and the supporting facts stored in memory are captured to distill the most relevant information in three modalities (i.e., image, question, and KB). Finally, the optimal answer is predicted by choosing the supporting fact with the highest score. Furthermore, to enable a fast, in-depth understanding given a small number of samples, especially with heterogeneity in the multi-modal scenarios such as image question answering (i.e., Image-QA) and image captioning (i.e., IC), we study the few-shot visual-semantic learning and present the Hierarchical Graph ATtention Network (i.e., HGAT). This two-stage network models the intra- and inter-modal relationships with limited image-text samples. The main contributions of HGAT can be summarized as follows: 1) it sheds light on tackling few-shot multi-modal learning problems, which focuses primarily, but not exclusively, on visual and semantic modalities, through better exploitation of the intra-relationship of each modality and an attention-based co-learning framework between modalities using a hierarchical graph-based architecture; 2) it achieves superior performance on both visual question answering and image captioning in the few-shot setting; 3) it can be easily extended to the semi-supervised setting where image-text samples are partially unlabeled. Although various attention mechanisms have been utilized to manage contextualized representations by modeling intra- and inter-modal relationships of the two modalities, one limitation of the predominant visual-semantic methods is the lack of reasoning with event correlation, sensing, and analyzing relationships among abundant and informative events contained in the video. To this end, we introduce the dense caption modality as a new auxiliary and distill event-correlated information to infer the correct answer. We propose a novel end-to-end trainable model, Event-Correlated Graph Neural Networks (EC-GNNs), to perform cross-modal reasoning over information from the three modalities (i.e., caption, video, and question). Besides exploiting a new modality, we employ cross-modal reasoning modules to explicitly model inter-modal relationships and aggregate relevant information across different modalities. We propose a question-guided self-adaptive multi-modal fusion module to collect the question-oriented and event-correlated evidence through multi-step reasoning. To evaluate our proposed models, we conduct extensive experiments on VTW, MSVD-QA, and TGIF-QA datasets for Video-QA task, Toronto COCO-QA, Visual Genome-QA datasets for few-shot Image-QA task, COCO-FITB dataset for few-shot IC task, and FVQA, Visual7W + ConceptNet datasets for KB-Image-QA task. The experimental results justify these models’ effectiveness and superiority over baseline methods

Improving Retrieval-Based Question Answering with Deep Inference Models

Question answering is one of the most important and difficult applications at the border of information retrieval and natural language processing, especially when we talk about complex science questions which require some form of inference to determine the correct answer. In this paper, we present a two-step method that combines information retrieval techniques optimized for question answering with deep learning models for natural language inference in order to tackle the multi-choice question answering in the science domain. For each question-answer pair, we use standard retrieval-based models to find relevant candidate contexts and decompose the main problem into two different sub-problems. First, assign correctness scores for each candidate answer based on the context using retrieval models from Lucene. Second, we use deep learning architectures to compute if a candidate answer can be inferred from some well-chosen context consisting of sentences retrieved from the knowledge base. In the end, all these solvers are combined using a simple neural network to predict the correct answer. This proposed two-step model outperforms the best retrieval-based solver by over 3% in absolute accuracy.Comment: 8 pages, 2 figures, 8 tables, accepted at IJCNN 201

Proceedings of QG2010: The Third Workshop on Question Generation

These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge". QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)