Visualising Discourse Structure in Interactive Documents

In this paper we introduce a method for generating interactive documents which exploits the visual features of hypertext to represent discourse structure. We explore the consistent and principled use of graphics and animation to support navigation and comprehension of non-linear text, where textual discourse markers do not always work effectively

STARC: Structured Annotations for Reading Comprehension

We present STARC (Structured Annotations for Reading Comprehension), a new annotation framework for assessing reading comprehension with multiple choice questions. Our framework introduces a principled structure for the answer choices and ties them to textual span annotations. The framework is implemented in OneStopQA, a new high-quality dataset for evaluation and analysis of reading comprehension in English. We use this dataset to demonstrate that STARC can be leveraged for a key new application for the development of SAT-like reading comprehension materials: automatic annotation quality probing via span ablation experiments. We further show that it enables in-depth analyses and comparisons between machine and human reading comprehension behavior, including error distributions and guessing ability. Our experiments also reveal that the standard multiple choice dataset in NLP, RACE, is limited in its ability to measure reading comprehension. 47% of its questions can be guessed by machines without accessing the passage, and 18% are unanimously judged by humans as not having a unique correct answer. OneStopQA provides an alternative test set for reading comprehension which alleviates these shortcomings and has a substantially higher human ceiling performance.Comment: ACL 2020. OneStopQA dataset, STARC guidelines and human experiments data are available at https://github.com/berzak/onestop-q

An iterative approach for lexicon characterization in juridical context

In the juridical context, knowledge management applications have a central role. In order to improve the effectiveness of document management procedures, techniques for automatic comprehension of textual content are required. In this work, a methodology for semi-automatic derivation of knowledge from document collections is proposed. In order to extract relevant information from document text, a process integrating both statistical and lexical approaches is applied. Moreover, we propose a system for the evaluation of the extracted peculiar lexicon quality. The system is used for the processing of heterogeneous documents corpus issued by Italy’s juridical domain

Reading in a foreign language: Strategic variation between readers of differing proficiency

For university language students who are required to deal with literary texts for linguistic or literary purposes, there is hardly any transitional stage between short adapted expository texts, read in the early stages of language learning, and complex literary texts, encountered at university in the literature class. Language readers must then make a substantial mental effort to understand texts intended for a native readership. In challenging reading mode, the quality of reading depends on the efficiency of problem-solving operations, including evaluative and executive strategies, put into place in order to attempt to fill in the comprehension gaps present in complex texts. Although reading strategies used by foreign language learners have been identified and categorised by research, the conditions of their use and their relationships are still unclear. Moreover, to my knowledge, no empirical investigation has focused specifically on comprehension monitoring in the context of foreign language literary texts. Literature instruction would benefit from such a study. Using verbal reports to elicit data, this study proposes to examine how proficient and less proficient university students of French, at intermediate level of instruction, implement problem-solving strategies when reading literary texts. Strategies such as guessing at words, consulting a dictionary, and translating mentally, are studied in relation to their contribution to the overall monitoring cycle. The results obtained indicate that proficient and less proficient readers tend to use the same strategies but with different purposes. The study demonstrates that the major difference between the two groups of respondents resides in ability some readers have to integrate meaning and construct text in a cohesive and synthetic fashion

Assessing the Effect of Visualizations on Bayesian Reasoning through Crowdsourcing

People have difficulty understanding statistical information and are unaware of their wrong judgments, particularly in Bayesian reasoning. Psychology studies suggest that the way Bayesian problems are represented can impact comprehension, but few visual designs have been evaluated and only populations with a specific background have been involved. In this study, a textual and six visual representations for three classic problems were compared using a diverse subject pool through crowdsourcing. Visualizations included area-proportional Euler diagrams, glyph representations, and hybrid diagrams combining both. Our study failed to replicate previous findings in that subjects' accuracy was remarkably lower and visualizations exhibited no measurable benefit. A second experiment confirmed that simply adding a visualization to a textual Bayesian problem is of little help, even when the text refers to the visualization, but suggests that visualizations are more effective when the text is given without numerical values. We discuss our findings and the need for more such experiments to be carried out on heterogeneous populations of non-experts

Aprender Ciencia con textos: Bases teóricas y directrices

In this work we analyse the cognitive processes and the variables that take part in texts comprehension. We present the textual characteristics that, in physical sciences, favour the elaboration of the mental representation called situation model, for his important role in comprehension. Finally, we emphasize the aspects of the activities (questions, problem solving, practical work) that improve learning from texts of physical sciences