Aligned Image-Word Representations Improve Inductive Transfer Across Vision-Language Tasks

An important goal of computer vision is to build systems that learn visual representations over time that can be applied to many tasks. In this paper, we investigate a vision-language embedding as a core representation and show that it leads to better cross-task transfer than standard multi-task learning. In particular, the task of visual recognition is aligned to the task of visual question answering by forcing each to use the same word-region embeddings. We show this leads to greater inductive transfer from recognition to VQA than standard multitask learning. Visual recognition also improves, especially for categories that have relatively few recognition training labels but appear often in the VQA setting. Thus, our paper takes a small step towards creating more general vision systems by showing the benefit of interpretable, flexible, and trainable core representations.Comment: Accepted in ICCV 2017. The arxiv version has an extra analysis on correlation with human attentio

A scientific information extraction dataset for nature inspired engineering

Nature has inspired various ground-breaking technological developments in applications ranging from robotics to aerospace engineering and the manufacturing of medical devices. However, accessing the information captured in scientific biology texts is a time-consuming and hard task that requires domain-specific knowledge. Improving access for outsiders can help interdisciplinary research like Nature Inspired Engineering. This paper describes a dataset of 1,500 manually-annotated sentences that express domain-independent relations between central concepts in a scientific biology text, such as trade-offs and correlations. The arguments of these relations can be Multi Word Expressions and have been annotated with modifying phrases to form non-projective graphs. The dataset allows for training and evaluating Relation Extraction algorithms that aim for coarse-grained typing of scientific biological documents, enabling a high-level filter for engineers.Comment: Published in Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020). Updated dataset statistics, results unchange

Development in the early years : Its importance for school performance and adult outcomes [Wider Benefits of Learning Research Report No. 20]

Early development of children’s intellectual, social and physical abilities has the potential to affect their long term achievement, beyond the initial introduction to the classroom, through their school lives and into adulthood. A greater understanding of the processes at work in these early years and their role in later success is therefore important to ensure that resources are appropriately targeted. Past research has shown that early cognitive attainment is strongly related to later academic success. But we are also interested in the benefit that children gain from arriving at school with particular personal characteristics and the relationship which these may have to cognitive development. We also seek to explore the role of development (as opposed to innate capability) in the pre-school years. Data from the 1970 British Cohort Study is used to examine the importance of early measures of children’s cognitive ability and behavioural development for their subsequent school and labour market achievement. Our results suggest that, of the various measures used in this study, the most powerful predictor of later academic and labour market success is the ability of children to copy basic designs. However, we do not ignore the influence of behavioural factors and highlight the particular importance of skills related to attention with respect to these outcomes. The results clearly show that early development of both cognitive and behavioural skills have a role in subsequent achievement. In this respect, we believe that the findings in this report add to the debate on the appropriate balance between cognitive and non-cognitive skills at different ages and for different groups of children. In particular, failure to place sufficient emphasis on cognitive development may run counter to the interests of children from low SES groups. We believe that pedagogy should continue to address ways in which cognitive and non-cognitive abilities can support one another and how the interactions between these different groups of skills can best be harnessed for different groups of children

Structuring Wikipedia Articles with Section Recommendations

Sections are the building blocks of Wikipedia articles. They enhance readability and can be used as a structured entry point for creating and expanding articles. Structuring a new or already existing Wikipedia article with sections is a hard task for humans, especially for newcomers or less experienced editors, as it requires significant knowledge about how a well-written article looks for each possible topic. Inspired by this need, the present paper defines the problem of section recommendation for Wikipedia articles and proposes several approaches for tackling it. Our systems can help editors by recommending what sections to add to already existing or newly created Wikipedia articles. Our basic paradigm is to generate recommendations by sourcing sections from articles that are similar to the input article. We explore several ways of defining similarity for this purpose (based on topic modeling, collaborative filtering, and Wikipedia's category system). We use both automatic and human evaluation approaches for assessing the performance of our recommendation system, concluding that the category-based approach works best, achieving precision@10 of about 80% in the human evaluation.Comment: SIGIR '18 camera-read

Protocoles d'évaluation pour l'extraction d'information libre

On voudrait apprendre à "lire automatiquement". L'extraction d'information consiste à transformer des paragraphes de texte écrits en langue naturelle en une liste d'éléments d'information autosuffisants, de façon à pouvoir comparer et colliger l'information extraite de plusieurs sources. Les éléments d'information sont ici représentés comme des relations entre entités : (Athéna ; est la fille de ; Zeus). L'extraction d'information libre (EIL) est un paradigme récent, visant à extraire un grand nombre de relations contenues dans le texte analysé, découvertes au fur et à mesure, par opposition à un nombre restreint de relations prédéterminées comme il est plus courant. Cette thèse porte sur l'évaluation des méthodes d'EIL. Dans les deux premiers chapitres, on évalue automatiquement les extractions d'un système d'EIL, en les comparant à des références écrites à la main, mettant respectivement l'accent sur l'informativité de l'extraction, puis sur son exhaustivité. Dans les deux chapitres suivants, on étudie et propose des alternatives à la fonction de confiance, qui juge des productions d'un système. En particulier, on y analyse et remet en question les méthodologies suivant lesquelles cette fonction est évaluée : d'abord comme modèle de validation de requêtes, puis en comparaison du cadre bien établi de la complétion de bases de connaissances.Information extraction consists in the processing of natural language documents into a list of self-sufficient informational elements, which allows for cross collection into Knowledge Bases, and automatic processing. The facts that result from this process are in the form of relationships between entities : (Athena ; is the daughter of ; Zeus). Open Information Extraction (OIE) is a recent paradigm the purpose of which is to extract an order of magnitude more relations from the input corpus than classical IE methods, what is achieved by encoding or learning more general patterns, in a less supervised fashion. In this thesis, I study and propose new evaluation protocols for the task of Open Information Extraction, with links to that of Knowledge Base Completion. In the first two chapters, I propose to automatically score the output of an OIE system, against a manually established reference, with particular attention paid to the informativity and exhaustivity of the extractions. I then turn my focus to the confidence function that qualifies all extracted elements, to evaluate it in a variety of settings, and propose alternative models

Thinking outside the graph: scholarly knowledge graph construction leveraging natural language processing

Despite improved digital access to scholarly knowledge in recent decades, scholarly communication remains exclusively document-based. The document-oriented workflows in science publication have reached the limits of adequacy as highlighted by recent discussions on the increasing proliferation of scientific literature, the deficiency of peer-review and the reproducibility crisis. In this form, scientific knowledge remains locked in representations that are inadequate for machine processing. As long as scholarly communication remains in this form, we cannot take advantage of all the advancements taking place in machine learning and natural language processing techniques. Such techniques would facilitate the transformation from pure text based into (semi-)structured semantic descriptions that are interlinked in a collection of big federated graphs. We are in dire need for a new age of semantically enabled infrastructure adept at storing, manipulating, and querying scholarly knowledge. Equally important is a suite of machine assistance tools designed to populate, curate, and explore the resulting scholarly knowledge graph. In this thesis, we address the issue of constructing a scholarly knowledge graph using natural language processing techniques. First, we tackle the issue of developing a scholarly knowledge graph for structured scholarly communication, that can be populated and constructed automatically. We co-design and co-implement the Open Research Knowledge Graph (ORKG), an infrastructure capable of modeling, storing, and automatically curating scholarly communications. Then, we propose a method to automatically extract information into knowledge graphs. With Plumber, we create a framework to dynamically compose open information extraction pipelines based on the input text. Such pipelines are composed from community-created information extraction components in an effort to consolidate individual research contributions under one umbrella. We further present MORTY as a more targeted approach that leverages automatic text summarization to create from the scholarly article's text structured summaries containing all required information. In contrast to the pipeline approach, MORTY only extracts the information it is instructed to, making it a more valuable tool for various curation and contribution use cases. Moreover, we study the problem of knowledge graph completion. exBERT is able to perform knowledge graph completion tasks such as relation and entity prediction tasks on scholarly knowledge graphs by means of textual triple classification. Lastly, we use the structured descriptions collected from manual and automated sources alike with a question answering approach that builds on the machine-actionable descriptions in the ORKG. We propose JarvisQA, a question answering interface operating on tabular views of scholarly knowledge graphs i.e., ORKG comparisons. JarvisQA is able to answer a variety of natural language questions, and retrieve complex answers on pre-selected sub-graphs. These contributions are key in the broader agenda of studying the feasibility of natural language processing methods on scholarly knowledge graphs, and lays the foundation of which methods can be used on which cases. Our work indicates what are the challenges and issues with automatically constructing scholarly knowledge graphs, and opens up future research directions

Does Childhood Executive Function Predict Adolescent Functional Outcomes in Girls with ADHD?

We prospectively followed an ethnically and socioeconomically diverse sample of preadolescent girls with ADHD (n = 140) and matched comparison girls (n = 88) over a period of 5 years, from middle childhood through early/mid-adolescence. Our aim was to examine the ability of measures of childhood executive function (EF) to predict functional outcomes in adolescence. Measures of neuropsychological functioning comprised the childhood predictors, with academic, social, and global functioning serving as adolescent criterion measures. Results indicated that childhood EF predicted (a) academic achievement and social functioning across our entire sample (independent of diagnostic group status) and (b) global functioning only in girls with ADHD (independent of IQ). These results highlight the non-specificity of EF deficits and suggest the importance of assessing and developing interventions that target EF impairments, particularly in those at high-risk for negative outcomes, in order to prevent long-term difficulties across a range of important functional domains

Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!

Large Language Models (LLMs) have made remarkable strides in various tasks. Whether LLMs are competitive few-shot solvers for information extraction (IE) tasks, however, remains an open problem. In this work, we aim to provide a thorough answer to this question. Through extensive experiments on nine datasets across four IE tasks, we demonstrate that current advanced LLMs consistently exhibit inferior performance, higher latency, and increased budget requirements compared to fine-tuned SLMs under most settings. Therefore, we conclude that LLMs are not effective few-shot information extractors in general. Nonetheless, we illustrate that with appropriate prompting strategies, LLMs can effectively complement SLMs and tackle challenging samples that SLMs struggle with. And moreover, we propose an adaptive filter-then-rerank paradigm to combine the strengths of LLMs and SLMs. In this paradigm, SLMs serve as filters and LLMs serve as rerankers. By prompting LLMs to rerank a small portion of difficult samples identified by SLMs, our preliminary system consistently achieves promising improvements (2.4% F1-gain on average) on various IE tasks, with an acceptable time and cost investment.Comment: Accepted by EMNLP 2023 Finding