A Re-ranking Model for Dependency Parser with Recursive Convolutional Neural Network

In this work, we address the problem to model all the nodes (words or phrases) in a dependency tree with the dense representations. We propose a recursive convolutional neural network (RCNN) architecture to capture syntactic and compositional-semantic representations of phrases and words in a dependency tree. Different with the original recursive neural network, we introduce the convolution and pooling layers, which can model a variety of compositions by the feature maps and choose the most informative compositions by the pooling layers. Based on RCNN, we use a discriminative model to re-rank a $k$-best list of candidate dependency parsing trees. The experiments show that RCNN is very effective to improve the state-of-the-art dependency parsing on both English and Chinese datasets

Overexpression of THI4 and HAP4 Improves Glucose Metabolism and Ethanol Production in Saccharomyces cerevisiae

Redox homeostasis is essential to the maintenance of cell metabolism. Changes in the redox state cause global metabolic and transcriptional changes. Our previous study indicated that the overexpression of NADH oxidase in Saccharomyces cerevisiae led to increased glucose consumption and ethanol production. Gene expression related to thiamine synthesis and osmotolerance as well as HAP4 expression was increased in response to redox change caused by the overexpression of NADH oxidase. To identify detailed relationships among cofactor levels, thiamine synthesis, expression of HAP4, and osmotolerance, and to determine whether these changes are interdependent, THI4 and HAP4 were overexpressed in S. cerevisiae BY4741. The glucose consumption rate of THI4-overexpressing strain (thi4-OE) was the highest, followed by HAP4-overexpressing strain (hap4-OE) > NADH oxidase-overexpressing strain (nox-OE) > control strain (con), while strain hap4-OE showed the highest concentration of ethanol after 26 h of fermentation. Reduced glycerol production and increased osmotolerance were observed in thi4-OE and hap4-OE, as well as in nox-OE. HAP4 globally regulated thiamine synthesis, biomass synthesis, respiration, and osmotolerance of cells, which conferred the recombinant strain hap4-OE with faster glucose metabolism and enhanced stress resistance. Moreover, overexpression of HAP4 might extend the life span of cells under caloric restriction by lowering the NADH level. Although overexpression of THI4 and HAP4 induced various similar changes at both the metabolic and the transcriptional level, the regulatory effect of THI4 was more limited than that of HAP4, and was restricted to the growth phase of cells. Our findings are expected to benefit the bio-ethanol industry

Overexpression of a Water-Forming NADH Oxidase Improves the Metabolism and Stress Tolerance of Saccharomyces cerevisiae in Aerobic Fermentation

Recognising that the world into which students emerge upon graduation is characterised by constant change, we embrace a critical pedagogy that can be implemented in the classroom through the use of freehand drawing. Freehand drawing is a technique that can stimulate a critical stance, as visual representations allow us to comprehend the world differently, while permitting us see how others understand the world. First year students, in their first lecture, were asked to draw their interpretations of Irish politics and to explain in writing what they had drawn. The students were then placed in groups and asked to note what they saw in each other’s drawings, allowing for the identification of general patterns and themes. In this context, freehand drawing facilitates our ability to: ‘see’ how we understand a topic and that there are multiple ways of understanding; test theories, orthodoxies and accepted truths; scrutinise tacit assumptions; and ponder other possibilities. In employing freehand drawing in this manner, our aim is to create a learning environment where students develop their capacity for critical self-reflection

Capturing Argument Interaction in Semantic Role Labeling with Capsule Networks

Semantic role labeling (SRL) involves extracting propositions (i.e. predicates and their typed arguments) from natural language sentences. State-of-the-art SRL models rely on powerful encoders (e.g., LSTMs) and do not model non-local interaction between arguments. We propose a new approach to modeling these interactions while maintaining efficient inference. Specifically, we use Capsule Networks: each proposition is encoded as a tuple of \textit{capsules}, one capsule per argument type (i.e. role). These tuples serve as embeddings of entire propositions. In every network layer, the capsules interact with each other and with representations of words in the sentence. Each iteration results in updated proposition embeddings and updated predictions about the SRL structure. Our model substantially outperforms the non-refinement baseline model on all 7 CoNLL-2019 languages and achieves state-of-the-art results on 5 languages (including English) for dependency SRL. We analyze the types of mistakes corrected by the refinement procedure. For example, each role is typically (but not always) filled with at most one argument. Whereas enforcing this approximate constraint is not useful with the modern SRL system, iterative procedure corrects the mistakes by capturing this intuition in a flexible and context-sensitive way.Comment: 11 pages, 6 figures, accepted as a long paper at EMNLP 201