Advancing Natural Language Processing in Educational Assessment examines the use of natural language technology in educational testing, measurement, and assessment. Recent developments in natural ...language processing (NLP) have enabled large-scale educational applications, though scholars and professionals may lack a shared understanding of the strengths and limitations of NLP in assessment as well as the challenges that testing organizations face in implementation. This first-of-its-kind book provides evidence-based practices for the use of NLP-based approaches to automated text and speech scoring, language proficiency assessment, technology-assisted item generation, gamification, learner feedback, and beyond. Spanning historical context, validity and fairness issues, emerging technologies, and implications for feedback and personalization, these chapters represent the most robust treatment yet about NLP for education measurement researchers, psychometricians, testing professionals, and policymakers.
Summary
Maize exhibits marked growth and yield response to supplemental nitrogen (N). Here, we report the functional characterization of a maize NIN‐like protein ZmNLP5 as a central hub in a ...molecular network associated with N metabolism. Predominantly expressed and accumulated in roots and vascular tissues, ZmNLP5 was shown to rapidly respond to nitrate treatment. Under limited N supply, compared with that of wild‐type (WT) seedlings, the zmnlp5 mutant seedlings accumulated less nitrate and nitrite in the root tissues and ammonium in the shoot tissues. The zmnlp5 mutant plants accumulated less nitrogen than the WT plants in the ear leaves and seed kernels. Furthermore, the mutants carrying the transgenic ZmNLP5 cDNA fragment significantly increased the nitrate content in the root tissues compared with that of the zmnlp5 mutants. In the zmnlp5 mutant plants, loss of the ZmNLP5 function led to changes in expression for a significant number of genes involved in N signalling and metabolism. We further show that ZmNLP5 directly regulates the expression of nitrite reductase 1.1 (ZmNIR1.1) by binding to the nitrate‐responsive cis‐element at the 5′ UTR of the gene. Interestingly, a natural loss‐of‐function allele of ZmNLP5 in Mo17 conferred less N accumulation in the ear leaves and seed kernels resembling that of the zmnlp5 mutant plants. Our findings show that ZmNLP5 is involved in mediating the plant response to N in maize.
Significance Statement
In this study, we report the functional characterization of ZmNLP5 for its role in modulating the N response, suggesting that ZmNLP5 is a potential candidate for improving N use efficiency in maize production.
Abstract
Because the computer cannot directly understand the text corpus in the NLP task, the first thing to do is to represent the characteristics of the natural language numerically, and the word ...vector technology provides a good way to express it. Because Word2vec considers context and has fewer dimensions, it is now more popular words embedded. However, due to the particularity of Chinese, word2vec cannot accurately identify the polysemy of words. In this paper, a lightweight and effective method is used to merge vocabulary into character representation. This approach avoids designing complex sequence modeling architectures. for any neural network model, simply fine-tuning the character input layer can introduce vocabulary information. The model also uses the modified LSTM to bridge the enormous LSTM and the Transformer model. The interaction between input and context provides a richer modeling space that significantly improves testing on all four public datasets.
Groundbreaking inventions and highly significant performance improvements in deep learning based Natural Language Processing are witnessed through the development of transformer based large ...Pre-trained Language Models (PLMs). The wide availability of unlabeled data within human generated data deluge along with self-supervised learning strategy helps to accelerate the success of large PLMs in language generation, language understanding, etc. But at the same time, latent historical bias/unfairness in human minds towards a particular gender, race, etc., encoded unintentionally/intentionally into the corpora harms and questions the utility and efficacy of large PLMs in many real-world applications, particularly for the protected groups. In this paper, we present an extensive investigation towards understanding the existence of “Affective Bias” in large PLMs to unveil any biased association of emotions such as anger, fear, joy, etc., towards a particular gender, race or religion with respect to the downstream task of textual emotion detection. We conduct our exploration of affective bias from the very initial stage of corpus level affective bias analysis by searching for imbalanced distribution of affective words within a domain, in large scale corpora that are used to pre-train and fine-tune PLMs. Later, to quantify affective bias in model predictions, we perform an extensive set of class-based and intensity-based evaluations using various bias evaluation corpora. Our results show the existence of statistically significant affective bias in the PLM based emotion detection systems, indicating biased association of certain emotions towards a particular gender, race, and religion.
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•Unveil the existence of latent Affective Bias in large Pre-trained Language Models.•Extensive set of experiments to identify gender, racial and religious affective bias.•Corpus Level Affective Bias analysis to identify affective bias in training corpora.•Prediction Level Affective Bias analysis in the context of textual emotion detection.•Extensive class-based & intensity-based evaluations using various evaluation corpora.
Random forests is currently one of the most used machine learning algorithms in the non-streaming (batch) setting. This preference is attributable to its high learning performance and low demands ...with respect to input preparation and hyper-parameter tuning. However, in the challenging context of evolving data streams, there is no random forests algorithm that can be considered state-of-the-art in comparison to bagging and boosting based algorithms. In this work, we present the adaptive random forest (ARF) algorithm for classification of evolving data streams. In contrast to previous attempts of replicating random forests for data stream learning, ARF includes an effective resampling method and adaptive operators that can cope with different types of concept drifts without complex optimizations for different data sets. We present experiments with a parallel implementation of ARF which has no degradation in terms of classification performance in comparison to a serial implementation, since trees and adaptive operators are independent from one another. Finally, we compare ARF with state-of-the-art algorithms in a traditional test-then-train evaluation and a novel delayed labelling evaluation, and show that ARF is accurate and uses a feasible amount of resources.
Summary
Plants evolved sophisticated machineries to monitor levels of external nitrogen supply, respond to nitrogen demand from different tissues and integrate this information for coordinating its ...assimilation. Although roles of inorganic nitrogen in orchestrating developments have been studied in model plants and crops, systematic understanding of the origin and evolution of its assimilation and signaling machineries remains largely unknown.
We expanded taxon samplings of algae and early‐diverging land plants, covering all main lineages of Archaeplastida, and reconstructed the evolutionary history of core components involved in inorganic nitrogen assimilation and signaling.
Most components associated with inorganic nitrogen assimilation were derived from the ancestral Archaeplastida. Improvements of assimilation machineries by gene duplications and horizontal gene transfers were evident during plant terrestrialization. Clusterization of genes encoding nitrate assimilation proteins might be an adaptive strategy for algae to cope with changeable nitrate availability in different habitats. Green plants evolved complex nitrate signaling machinery that was stepwise improved by domains shuffling and regulation co‐option.
Our study highlights innovations in inorganic nitrogen assimilation and signaling machineries, ranging from molecular modifications of proteins to genomic rearrangements, which shaped developmental and metabolic adaptations of plants to changeable nutrient availability in environments.
Neural sequence-to-sequence models have proven to be accurate and robust for many sequence prediction tasks, and have become the standard approach for automatic translation of text. The models work ...with a five-stage blackbox pipeline that begins with encoding a source sequence to a vector space and then decoding out to a new target sequence. This process is now standard, but like many deep learning methods remains quite difficult to understand or debug. In this work, we present a visual analysis tool that allows interaction and "what if"-style exploration of trained sequence-to-sequence models through each stage of the translation process. The aim is to identify which patterns have been learned, to detect model errors, and to probe the model with counterfactual scenario. We demonstrate the utility of our tool through several real-world sequence-to-sequence use cases on large-scale models.
In many practical applications of supervised learning the task involves the prediction of multiple target variables from a common set of input variables. When the prediction targets are binary the ...task is called multi-label classification, while when the targets are continuous the task is called multi-target regression. In both tasks, target variables often exhibit statistical dependencies and exploiting them in order to improve predictive accuracy is a core challenge. A family of multi-label classification methods address this challenge by building a separate model for each target on an expanded input space where other targets are treated as additional input variables. Despite the success of these methods in the multi-label classification domain, their applicability and effectiveness in multi-target regression has not been studied until now. In this paper, we introduce two new methods for multi-target regression, called
stacked single-target
and
ensemble of regressor chains
, by adapting two popular multi-label classification methods of this family. Furthermore, we highlight an inherent problem of these methods—a discrepancy of the values of the additional input variables between training and prediction—and develop extensions that use out-of-sample estimates of the target variables during training in order to tackle this problem. The results of an extensive experimental evaluation carried out on a large and diverse collection of datasets show that, when the discrepancy is appropriately mitigated, the proposed methods attain consistent improvements over the independent regressions baseline. Moreover, two versions of Ensemble of Regression Chains perform significantly better than four state-of-the-art methods including regularization-based multi-task learning methods and a multi-objective random forest approach.
Forecasting of multivariate time series data, for instance the prediction of electricity consumption, solar power production, and polyphonic piano pieces, has numerous valuable applications. However, ...complex and non-linear interdependencies between time steps and series complicate this task. To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved by recurrent neural networks (RNNs) with an attention mechanism. The typical attention mechanism reviews the information at each previous time step and selects relevant information to help generate the outputs; however, it fails to capture temporal patterns across multiple time steps. In this paper, we propose using a set of filters to extract time-invariant temporal patterns, similar to transforming time series data into its “frequency domain”. Then we propose a novel attention mechanism to select relevant time series, and use its frequency domain information for multivariate forecasting. We apply the proposed model on several real-world tasks and achieve state-of-the-art performance in almost all of cases. Our source code is available at
https://github.com/gantheory/TPA-LSTM
.
The accessibility of online hate speech has increased significantly, making it crucial for social-media companies to prioritize efforts to curb its spread. Although deep learning models demonstrate ...vulnerability to adversarial attacks, whether models fine-tuned for hate speech detection exhibit similar susceptibility remains underexplored. Textual adversarial attacks involve making subtle alterations to the original samples. These alterations are designed so that the adversarial examples produced can effectively deceive the target model, even when correctly classified by human observers. Though many approaches have been proposed to conduct word-level adversarial attacks on textual data, they face the obstacle of preserving the semantic coherence of texts during the generation of adversarial counterparts. Moreover, the adversarial examples produced are often easily distinguishable by human observers. This work presents a novel methodology that uses visually confusable glyphs and invisible characters to generate semantically and visually similar adversarial examples in a black-box setting. In the hate speech detection task context, our attack was effectively applied to several state-of-the-art deep learning models, fine-tuned on two benchmark datasets. The major contributions of this study are: (1) demonstrating the vulnerability of deep learning models fine-tuned for hate speech detection; (2) a novel attack framework based on a simple yet potent modification strategy; (3) superior outcomes in terms of accuracy degradation, attack success rate, average perturbation, semantic similarity, and perplexity when compared to existing baselines; (4) strict adherence to prescribed linguistic constraints while formulating adversarial samples; and (5) preservation of ground truth label while perturbing original input using imperceptible adversarial examples.
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