Abstract: Studies have shown that a dominant class of questions asked by visually impaired users on images of their surroundings involves reading text in the image. But today’s VQA models can not read! Our paper takes a first step towards addressing this problem. First, we introduce a new “TextVQA” dataset to facilitate progress on this important problem. Existing datasets either have a small proportion of questions about text (e.g., the VQA dataset) or are too small (e.g., the VizWiz dataset). TextVQA contains 45,336 questions on 28,408 images that require reasoning about text to answer. Second, we introduce a novel model architecture that reads text in the image, reasons about it in the context of the image and the question, and predicts an answer which might be a deduction based on the text and the image or composed of the strings found in the image. Consequently, we call our approach Look, Read, Reason & Answer (LoRRA). We show that LoRRA outperforms existing state-of-the-art VQA models on our TextVQA dataset. We find that the gap between human performance and machine performance is significantly larger on TextVQA than on VQA 2.0, suggesting that TextVQA is well-suited to benchmark progress along directions complementary to VQA 2.0.

Abstract: We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns visual concepts, words, and semantic parsing of sentences without explicit supervision on any of them; instead, our model learns by simply looking at images and reading paired questions and answers. Our model builds an object-based scene representation and translates sentences into executable, symbolic programs. To bridge the learning of two modules, we use a neuro-symbolic reasoning module that executes these programs on the latent scene representation. Analogical to human concept learning, the perception module learns visual concepts based on the language description of the object being referred to. Meanwhile, the learned visual concepts facilitate learning new words and parsing new sentences. We use curriculum learning to guide the searching over the large compositional space of images and language. Extensive experiments demonstrate the accuracy and efficiency of our model on learning visual concepts, word representations, and semantic parsing of sentences. Further, our method allows easy generalization to new object attributes, compositions, language concepts, scenes and questions, and even new program domains. It also empowers applications including visual question answering and bidirectional image-text retrieval.

Abstract: Do we read character in faces? What information do faces actually provide? What are the social and psychological consequences of reading character in faces? Zebrowitz unmasks the face and provides the first systematic, scientific account of our tendency to judge people by their appearance. Offering an in-depth discussion of two appearance qualities that influence our impressions of others—“baby-faceness” and “attractiveness”—and an analysis of these impressions, Zebrowitz has written an accessible and valuable book for professionals and general readers alike.

Abstract: In this paper, we present the SemEval-2019 Task 3 - EmoContext: Contextual Emotion Detection in Text. Lack of facial expressions and voice modulations make detecting emotions in text a challenging problem. For instance, as humans, on reading “Why don’t you ever text me!” we can either interpret it as a sad or angry emotion and the same ambiguity exists for machines. However, the context of dialogue can prove helpful in detection of the emotion. In this task, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes - Happy, Sad, Angry and Others. To facilitate the participation in this task, textual dialogues from user interaction with a conversational agent were taken and annotated for emotion classes after several data processing steps. A training data set of 30160 dialogues, and two evaluation data sets, Test1 and Test2, containing 2755 and 5509 dialogues respectively were released to the participants. A total of 311 teams made submissions to this task. The final leader-board was evaluated on Test2 data set, and the highest ranked submission achieved 79.59 micro-averaged F1 score. Our analysis of systems submitted to the task indicate that Bi-directional LSTM was the most common choice of neural architecture used, and most of the systems had the best performance for the Sad emotion class, and the worst for the Happy emotion class.

Abstract: With the growing cosmopolitan culture of modern cities, the need of robust Multi-Lingual scene Text (MLT) detection and recognition systems has never been more immense. With the goal to systematically benchmark and push the state-of-the-art forward, the proposed competition builds on top of the RRC-MLT-2017 with an additional end-to-end task, an additional language in the real images dataset, a large scale multi-lingual synthetic dataset to assist the training, and a baseline End-to-End recognition method. The real dataset consists of 20,000 images containing text from 10 languages. The challenge has 4 tasks covering various aspects of multi-lingual scene text: (a) text detection, (b) cropped word script classification, (c) joint text detection and script classification and (d) end-to-end detection and recognition. In total, the competition received 60 submissions from the research and industrial communities. This paper presents the dataset, the tasks and the findings of the presented RRC-MLT-2019 challenge.

Abstract: Understanding narratives requires reading between the lines, which in turn, requires interpreting the likely causes and effects of events, even when they are not mentioned explicitly. In this paper, we introduce Cosmos QA, a large-scale dataset of 35,600 problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. In stark contrast to most existing reading comprehension datasets where the questions focus on factual and literal understanding of the context paragraph, our dataset focuses on reading between the lines over a diverse collection of people’s everyday narratives, asking such questions as “what might be the possible reason of ...?", or “what would have happened if ..." that require reasoning beyond the exact text spans in the context. To establish baseline performances on Cosmos QA, we experiment with several state-of-the-art neural architectures for reading comprehension, and also propose a new architecture that improves over the competitive baselines. Experimental results demonstrate a significant gap between machine (68.4%) and human performance (94%), pointing to avenues for future research on commonsense machine comprehension. Dataset, code and leaderboard is publicly available at https://wilburone.github.io/cosmos.

Abstract: Understanding narratives requires reading between the lines, which in turn, requires interpreting the likely causes and effects of events, even when they are not mentioned explicitly. In this paper, we introduce Cosmos QA, a large-scale dataset of 35,600 problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. In stark contrast to most existing reading comprehension datasets where the questions focus on factual and literal understanding of the context paragraph, our dataset focuses on reading between the lines over a diverse collection of people's everyday narratives, asking such questions as "what might be the possible reason of ...?", or "what would have happened if ..." that require reasoning beyond the exact text spans in the context. To establish baseline performances on Cosmos QA, we experiment with several state-of-the-art neural architectures for reading comprehension, and also propose a new architecture that improves over the competitive baselines. Experimental results demonstrate a significant gap between machine (68.4%) and human performance (94%), pointing to avenues for future research on commonsense machine comprehension. Dataset, code and leaderboard is publicly available at this https URL.

Abstract: Change is a difficult-to-accept but inevitable condition of life. When it comes to the new forms of stories that we produce and consume, change becomes the topic of intense philosophical debates, r...

Abstract: In a class on popular fiction, this book leads students to a more sophisticated understanding of what Harlequin romances and similar books might mean to their readers.

Abstract: Although phonological awareness (PA) and rapid automatized naming (RAN) are confirmed as early predictors of reading in a large number of orthographies, it is as yet unclear whether the predictive patterns are universal or language specific. This was examined in a longitudinal study across Grades 1 and 2 with 1,120 children acquiring one of five alphabetic orthographies with different degrees of orthographic complexity (English, French, German, Dutch, and Greek). Path analyses revealed that a universal model could not be confirmed. When we specified the best-fitting model separately for each language, RAN was a consistent predictor of reading fluency in all orthographies, whereas the association between PA and reading was complex and mostly interactive. We conclude that RAN taps into a language-universal cognitive mechanism that is involved in reading alphabetic orthographies (independent of complexity), whereas the PA–reading relationship depends on many factors like task characteristics, develop...

Abstract: This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text - RRC-ArT that consists of three major challenges: i) scene text detection, ii) scene text recognition, and iii) scene text spotting. A total of 78 submissions from 46 unique teams/individuals were received for this competition. The top performing score of each challenge is as follows: i) T1 - 82.65%, ii) T2.1 - 74.3%, iii) T2.2 - 85.32%, iv) T3.1 - 53.86%, and v) T3.2 - 54.91%. Apart from the results, this paper also details the ArT dataset, tasks description, evaluation metrics and participants' methods. The dataset, the evaluation kit as well as the results are publicly available at the challenge website.

Abstract: This study aimed to determine the relations between fluid intelligence (Gf) and reading/mathematics and possible moderators. A meta-analysis of 680 studies involving 793 independent samples and more than 370,000 participants found that Gf was moderately related to reading, r = .38, 95% CI [.36, .39], and mathematics, r = .41, 95% CI [.39, 44]. Synthesis on the longitudinal correlations showed that Gf and reading/mathematics predicted each other in the development even after controlling for initial performance. Moderation analyses revealed the following findings: (a) Gf showed stronger relations to mathematics than to reading, (b) within reading or mathematics, Gf showed stronger relations to complex skills than to foundational skills, (c) the relations between Gf and reading/mathematics increased with age, and (d) family social economic status (SES) mostly affected the relations between Gf and reading/mathematics in the early development stage. These findings, taken together, are partially in line with the investment theory but are more in line with the intrinsic cognitive load theory, mutualism theory, and the gene-SES interaction hypothesis of cognition and learning. More importantly, these findings imply an integration model of these theories from an educational and developmental perspective: Children may rely on Gf to learn reading and mathematics early on, when high family SES can boost the effects of Gf on reading/mathematics performance. As children receive more formal schooling and gain more learning experiences, their reading and mathematics improvement may promote their Gf development. During development, the negative effects of low family SES on the relations between Gf and reading/mathematics may be offset by education/learning experiences. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Abstract: Background/Context The Internet has democratized access to information but in so doing has opened the floodgates to misinformation, fake news, and rank propaganda masquerading as dispassionate analysis. Despite mounting attention to the problem of online misinformation and growing agreement that digital literacy efforts are important, prior research offers few concrete ideas about what skilled evaluations look like. Purpose/Objective/Research Question/Focus of Study Our purpose in this study was to seek out those who are skilled in online evaluations in order to understand how their strategies and approaches to evaluating digital content might inform educational efforts. We sampled 45 experienced users of the Internet: 10 Ph.D. historians, 10 professional fact checkers, and 25 Stanford University undergraduates. Analysis focused on the strategies participants used to evaluate online information and arrive at judgments of credibility. Research Design In this expert/novice study, participants thought aloud as they evaluated live websites and searched for information on social and political issues such as bullying, minimum wage, and teacher tenure. We analyze and present findings from three of the tasks participants completed. Findings/Results Historians and students often fell victim to easily manipulated features of websites, such as official-looking logos and domain names. They read vertically, staying within a website to evaluate its reliability. In contrast, fact checkers read laterally, leaving a site after a quick scan and opening up new browser tabs in order to judge the credibility of the original site. Compared to the other groups, fact checkers arrived at more warranted conclusions in a fraction of the time. Conclusions/Recommendations We draw on insights gleaned from the fact checkers’ practices to examine current curricular approaches to teaching web credibility as well as to suggest alternatives.

Abstract: The purpose of this product is to support PST develop knowledge, understanding and skill in teaching literacy to children from the Foundation Year to Year 6. To assist in achieving these goals, the product outlines from the beginning that successful teaching involves knowing the students, the content and associated curriculum requirements, and understanding how to apply this knowledge in explicit and skilled ways to meet individual students’ literacy learning needs. The product emphasises that effective teachers continually engage in reflective practice to gauge if and how each student’s learning goals are achieved. It challenges the PST to consider ways of knowing, learning and teaching, providing opportunities to consider ways of using digital platforms to develop children’s reading, writing, speaking, listening and viewing skills. Developed for preservice teachers, practising teachers and those interested in English literacy teaching and learning, this product includes a range of vignettes drawn from classroom and university practice across Australia, examples that stand to authenticate the learning. [Book Synopsis]

Abstract: Reading comprehension is a complex task which depends on a range of cognitive and linguistic processes. According to the Simple View of Reading, this complexity can be captured as the product of tw...

Abstract: Dialog state tracking is used to estimate the current belief state of a dialog given all the preceding conversation. Machine reading comprehension, on the other hand, focuses on building systems that read passages of text and answer questions that require some understanding of passages. We formulate dialog state tracking as a reading comprehension task to answer the question what is the state of the current dialog? after reading conversational context. In contrast to traditional state tracking methods where the dialog state is often predicted as a distribution over a closed set of all the possible slot values within an ontology, our method uses a simple attention-based neural network to point to the slot values within the conversation. Experiments on MultiWOZ-2.0 cross-domain dialog dataset show that our simple system can obtain similar accuracies compared to the previous more complex methods. By exploiting recent advances in contextual word embeddings, adding a model that explicitly tracks whether a slot value should be carried over to the next turn, and combining our method with a traditional joint state tracking method that relies on closed set vocabulary, we can obtain a joint-goal accuracy of 47.33% on the standard test split, exceeding current state-of-the-art by 11.75%**.

Abstract: It is widely believed that reading to preschool children promotes their language and literacy skills. Yet, whether early parent-child book reading is an index of generally rich linguistic input or a unique predictor of later outcomes remains unclear. To address this question, we asked whether naturally occurring parent-child book reading interactions between 1 and 2.5 years-of-age predict elementary school language and literacy outcomes, controlling for the quantity of other talk parents provide their children, family socioeconomic status, and children's own early language skill. We find that the quantity of parent-child book reading interactions predicts children's later receptive vocabulary, reading comprehension, and internal motivation to read (but not decoding, external motivation to read, or math skill), controlling for these other factors. Importantly, we also find that parent language that occurs during book reading interactions is more sophisticated than parent language outside book reading interactions in terms of vocabulary diversity and syntactic complexity.

Abstract: Reading strategies have been shown to improve comprehension levels, especially for readers lacking adequate prior knowledge. Just as the process of knowledge accumulation is time-consuming for human readers, it is resource-demanding to impart rich general domain knowledge into a deep language model via pre-training. Inspired by reading strategies identified in cognitive science, and given limited computational resources - just a pre-trained model and a fixed number of training instances - we propose three general strategies aimed to improve non-extractive machine reading comprehension (MRC): (i) BACK AND FORTH READING that considers both the original and reverse order of an input sequence, (ii) HIGHLIGHTING, which adds a trainable embedding to the text embedding of tokens that are relevant to the question and candidate answers, and (iii) SELF-ASSESSMENT that generates practice questions and candidate answers directly from the text in an unsupervised manner. By fine-tuning a pre-trained language model (Radford et al., 2018) with our proposed strategies on the largest general domain multiple-choice MRC dataset RACE, we obtain a 5.8% absolute increase in accuracy over the previous best result achieved by the same pre-trained model fine-tuned on RACE without the use of strategies. We further fine-tune the resulting model on a target MRC task, leading to an absolute improvement of 6.2% in average accuracy over previous state-of-the-art approaches on six representative non-extractive MRC datasets from different domains (i.e., ARC, OpenBookQA, MCTest, SemEval-2018 Task 11, ROCStories, and MultiRC). These results demonstrate the effectiveness of our proposed strategies and the versatility and general applicability of our fine-tuned models that incorporate these strategies. Core code is available at https://github.com/nlpdata/strategy/.

Abstract: Reading comprehension is one of the most complex cognitive activities in which humans engage, making it difficult to teach, measure, and research. Despite decades of research in reading comprehensi...

Abstract: The relationship between nonverbal communication and deception continues to attract much interest, but there are many misconceptions about it. In this review, we present a scientific view on this r...

Abstract: This meta-analysis synthesized the relation between measures of socioeconomic status (SES) and students' information and communication technology (ICT) literacy—a skillset that has found its way in educational curricula. Using three-level random-effects modeling across 32 independent K-12 student samples that provided 75 correlation coefficients, we identified a positive, significant, and small correlation, r ¯ = 0.214, 95% CI [0.184, 0.244]. This correlation varied between studies and was moderated by the type of SES measure, the type of ICT literacy assessment, the broad categories of ICT skills assessed, the assessment of test fairness, and the sampling procedure employed. The findings of this meta-analysis suggest that students’ ICT literacy differs between socioeconomic status groups, thus pointing to a gap in the domain of ICT. However, the relation between SES and ICT literacy was weaker than those reported in other educational domains, such as mathematics and reading. Carefully designed studies and measures for which a validity argument has been crafted are needed when studying achievement gaps in the domain of ICT in future studies.

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Abstract: Scholars continue to search for solutions to shift climate change skeptics’ views on climate science and policy. However, research has shown that certain audiences are resistant to change regarding...

Abstract: The recently published fourth edition of the Wechsler Intelligence Scale for Children (WISC-IV) represents a considerable departure from previous versions of the scale. The structure of the instrum...