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18265 papers
This paper describes our system to predict enhanced dependencies for Universal Dependencies (UD) treebanks, which ranked 2 nd in the Shared Task on Enhanced Dependency Parsing with an average ELAS of 82.60%. Our system uses a hybrid two-step approach. First, we use a graph-based parser to extract a basic syntactic dependency tree. Then, we use a set of linguistic rules which generate the enhanced dependencies for the syntactic tree. The application of these rules is optimized using a classifier which predicts their suitability in the given context. A key advantage of this approach is its language independence, as rules rely solely on dependency trees and UPOS tags which are shared across all languages.
This paper presents the first dependency treebank for Bhojpuri, an Indo-Aryan language. Bhojpuri is one of the resource-poor Indian languages. The objective of the Bhojpuri Treebank (BHTB) project is to provide a substantial, syntactically annotated treebank for Bhojpuri which helps in building language technological tools. This project will also help in cross-lingual learning and typological research. Currently, the treebank consists of 4,881 tokens using the annotation scheme of Universal Dependencies (UD). We develop a Bhojpuri tagger and parser using the machine learning approach. The accuracy of the model is 57.49% UAS, 45.50% LAS, 79.69% UPOS accuracy and 77.64% XPOS accuracy. Finally, we discuss linguistic analysis and annotation process of the Bhojpuri UD treebank.
Memory-based learning can be characterized as a lazy learning method in machine learning terminology because it delays the processing of input by storing the input until needed. Linguistic structure parsing, which has been in a performance improvement bottleneck since the latest series of works was presented, determines the syntactic or semantic structure of a sentence. In this article, we construct a memory component and use it to augment a linguistic structure parser which allows the parser to directly extract patterns from the known training treebank to form memory. The experimental results show that existing state-of-the-art parsers reach new heights of performance on the main benchmarks for dependency parsing and semantic role labeling with this memory network.
Contribution of emotional valence and arousal to attentional processing over time is not fully understood. We employed a rapid serial visual paradigm (RSVP) in three experiments to investigate the role of valence and arousal. In three experiments, participants had to identify the expression of the two targets (experiment 1 - happy and angry; experiment 2 - angry and surprise; experiment 3 - happy and surprise) presented among neutral upright face distracters. In the first and third experiments, the two targets differed both in valence and arousal ratings. In experiment 2, the surprise and angry expressions differed in terms of valence but were matched for arousal. There was a happy expression advantage (lesser attentional blink) when the first target was anger (experiment 1) or surprise (experiment 3) and a surprise expression advantage when the first target was anger (experiment 2). There was a backward blink with reduced detection of the first target primarily by the relatively more positive valence second target. These results indicate that the benefit for happy and surprise expressions in comparison to angry expression identification is probably due to valence (more positive) and not arousal. Our results demonstrate a novel dynamic interplay of emotional information on temporal attention.
The connection between dependency trees and spanning trees is exploited by the NLP community to train and to decode graph-based dependency parsers. However, the NLP literature has missed an important difference between the two structures: only one edge may emanate from the root in a dependency tree. We analyzed the output of state-of-the-art parsers on many languages from the Universal Dependency Treebank: although these parsers are often able to learn that trees which violate the constraint should be assigned lower probabilities, their ability to do so unsurprisingly degrades as the size of the training set decreases. In fact, the worst constraint-violation rate we observe is 24%. Prior work has proposed an inefficient algorithm to enforce the constraint, which adds a factor of n to the decoding runtime. We adapt an algorithm due to
In this paper, we demonstrate that by utilizing sparse word representations, it becomes possible to surpass the results of more complex task-specific models on the task of finegrained all-words word sense disambiguation. Our proposed algorithm relies on an overcomplete set of semantic basis vectors that allows us to obtain sparse contextualized word representations. We introduce such an information theory-inspired synset representation based on the co-occurrence of word senses and nonzero coordinates for word forms which allows us to achieve an aggregated F-score of 78.8 over a combination of five standard word sense disambiguating benchmark datasets. We also demonstrate the general applicability of our proposed framework by evaluating it towards part-of-speech tagging on four different treebanks. Our results indicate a significant improvement over the application of the dense word representations.
One of the assumptions underlying the debates around translingual vs. SLW approaches is that pragmatism and idealism are at odds with one another. This chapter delves into past scholarship to show how that assumption has been challenged. We first highlight the prevalence of a male-dominated, dualist, “I win” mentality in our fields. Then, we examine past scholarship to “rediscover” the middle ground between the two polarities of pragmatism (often framed as assimilation to linguistic norms) and idealism (often framed as resistance to linguistic norms). In particular, we trace the theme of “critical pragmatism” as reflected in the work of scholars who have employed critical literacy and/or critical EAP approaches, through which students learn the conventions of academic writing while engaging with critical perspectives on those conventions. We conclude with examples from our own pedagogical practice and professional development work, to illustrate how our work in writing studies can be both pragmatic and idealistic when it comes to academic language and linguistic diversity.
In recent years 360° videos have been becoming more popular. For traditional media presentations, e.g., on a computer screen, a wide range of assessment methods are available. Different constructs, such as perceived quality or the induced emotional state of viewers, can be reliably assessed by subjective scales. Many of the subjective methods have only been validated using stimuli presented on a computer screen. This paper is using 360° videos to induce varying emotional states. Videos were presented 1) via a head-mounted display (HMD) and 2) via a traditional computer screen. Furthermore, participants were asked to rate their emotional state 1) in retrospect on the self-assessment manikin scale and 2) continuously on a 2-dimensional arousal-valence plane. In a repeated measures design, all participants (N = 18) used both presentation systems and both rating systems. Results indicate that there is a statistically significant difference in induced presence due to the presentation system. Furthermore, there was no statistically significant difference in ratings gathered with the two presentation systems. Finally, it was found that for arousal measures, a statistically significant difference could be found for the different rating methods, potentially indicating an underestimation of arousal ratings gathered in retrospect for screen presentation. In the future, rating methods such as a 2-dimensional arousal-valence plane could offer the advantage of enabling a reliable measurement of emotional states while being more embedded in the experience itself, enabling a more precise capturing of the emotional states.
Music style is tightly connected with listeners’ emotional processes and neural activities. However, it remains unclear how the brain works when different music styles are processed emotionally. The current study analyzed the neural activation associated with five music styles during emotion-evoking. Twenty non-musicians participated in the functional magnetic resonance imaging (fMRI) scanning and the emotional ratings of pleasure and arousal evoked by pop, rock, jazz, folk, and classical music. Results showed that classical music was associated with the highest pleasure rating and deactivation of the corpus callosum. Rock music was associated with the highest arousal rating and deactivation of the cingulate gyrus. Pop music activated the bilateral supplementary motor areas (SMA) and the superior temporal gyrus (STG) with moderate pleasure and arousal. As the first fMRI experiment investigating the relationship between the music style and emotion, it provides neural correlates of different music styles during emotion-evoking.
To overcome the lack of NLP resources for the low-resource languages, we can utilize tools that are already available for other highresource languages and then modify the output to conform to the target language. In this study, we proposed an approach to convert an Indonesian constituency treebank to a dependency treebank by utilizing an English NLP tool (Stanford CoreNLP) to create the initial dependency treebank. Some annotations in this initial treebank did not conform to Indonesian grammar, especially noun phrases' head-directionality. Noun phrases in English usually have head-final direction, while in Indonesian is the opposite, head-initial. We proposed a variant of tree rotations algorithm named headSwap for dependency trees. We used this algorithm to convert the head-directionality for noun phrases that were initially labeled as a compound. Moreover, we also proposed a set of rules to rename the dependency relation labels to conform to the recent guidelines. To evaluate our proposed method, we created a gold standard of 2,846 tokens that were annotated manually. Experiment results showed that our proposed method improved the Unlabeled Attachment Score (UAS) with a margin of 32.5% from 61.6 to 94.1% and the Labeled Attachment Score (LAS) with a margin of 41% from 44.1 to 85.1%. Finally, we created a new Indonesian dependency treebank that converted automatically using our proposed method that consists of 25,416 tokens. The dependency parser model built using this treebank has UAS of 75.90% and LAS of 70.38%.
Version 1.2 of the Late Latin Charter Treebank 1 (LLCT1). Contains a number of minor corrections, replaces the version 1.0 published at Zenodo in 2018. Early Medieval Latin documentary texts from Italy between AD 714-869 with morphological and syntactic annotation. Latin Dependency Treebank (LDT) compatible linguistic annotation, Prague style treebank format (PML). For a detailed description of the Late Latin Charter Treebanks, see the pre-print of the paper 'Late Latin Charter Treebank: contents and annotation', to be published in Corpora, 16:2 (2021), at the institutional repository of the University of Helsinki. See also Korkiakangas, T. and Lassila, M. (2013), <em>Abbreviations, fragmentary words, formulaic language: treebanking medieval charter material</em>, in Mambrini, F., Passarotti, M. and Sporleder, C., <em>Proceedings of the third workshop on annotation of corpora for research in the humanities</em>, pp. 61–72, and Korkiakangas, T. and Passarotti, M. (2011), <em>Challenges in Annotating Medieval Latin Charters</em>, in «Journal of Language Technology and Computational Linguistics», 26, pp. 103–114.
汉语学习者依存句法树库为非母语者语料提供依存句法分析,可以支持第二语言教学与研究,也对面向第二语言的句法分析、语法改错等相关研究具有重要意义。然而,现有的汉语学习者依存句法树库数量较少,且在标注方面仍存在一些问题。为此,本文改进依存句法标注规范,搭建在线标注平台,并开展汉语学习者依存句法标注。本文重点介绍了数据选取、标注流程等问题,并对标注结果进行质量分析,探索二语偏误对标注质量与句法分析的影响。
Cognitive reappraisal (CR) is a strategy used to regulate emotions that is thought to be effective but effortful, relying on higher-order cognitive control systems to engage in active regulation. Sleep deprivation is believed to impair the functioning of these control systems, suggesting that it may impede the ability to implement CR effectively. This study tested the causal effects of sleep deprivation on emotional reactivity and the neurobiological systems underlying CR. We employed a within-subject crossover design in which participants underwent fMRI scanning twice, once when fully rested and once after a night of total sleep deprivation. During scans, participants passively viewed or used CR to down-regulate their emotional response to negative and neutral images. Contrary to hypotheses, both self-reported negative affect ratings and neural responses to the images indicated no difference in the way participants implemented CR when sleep deprived and when fully rested. Meanwhile, neural regions that showed distinct reactivity responses to negative relative to neutral images lost this specificity under deprived conditions. Negative affect ratings and heart rate deceleration, a physiological response typically evoked by aversive pictures, exhibited a similar blunting. Together, these results suggest that, although sleep deprivation may reduce the discrimination between emotional reactivity responses to negative and neutral stimuli, it does not impact CR the way it is presently studied.
Version 1.2 of the Late Latin Charter Treebank 2 (LLCT2). Contains a number of minor corrections and replaces the version 1.0 published at Zenodo in 2019. Early Medieval Latin documentary texts from Italy between AD 774-897 with morphological and syntactic annotation. Latin Dependency Treebank (LDT) compatible linguistic annotation, CoNLL treebank format. Note that LLCT2 is also available open-access in the Universal Dependencies format at the website of the Universal Dependencies consortium. For a detailed description of the Late Latin Charter Treebanks, see the pre-print of the paper 'Late Latin Charter Treebank: contents and annotation', to be published in Corpora, 16:2 (2021), at the institutional repository of the University of Helsinki. See also Korkiakangas, T. and Lassila, M. (2013), <em>Abbreviations, fragmentary words, formulaic language: treebanking medieval charter material</em>, in Mambrini, F., Passarotti, M. and Sporleder, C., <em>Proceedings of the third workshop on annotation of corpora for research in the humanities</em>, pp. 61–72, and Korkiakangas, T. and Passarotti, M. (2011), <em>Challenges in Annotating Medieval Latin Charters</em>, in «Journal of Language Technology and Computational Linguistics», 26, pp. 103–114.
Emotion regulation is crucial for coping with stressors but in turn can also be influenced by stress. Initial studies provided mixed evidence showing either beneficial or impairing stress effects on cognitive emotion regulation depending on stress timing, sex or the regulatory strategy. Here, we investigated the impact of acute stress on different emotion regulation strategies in men and women. N = 118 healthy participants were subjected to the Trier Social Stress Test or a control condition after which they completed an emotion regulation paradigm, requiring them to regulate their emotions in response to negative pictures using reappraisal or distraction. Cortisol levels were repeatedly measured to quantify changes in HPA axis activity. Affective ratings and pupil dilation served to measure emotion regulation success and the cognitive effort to regulate emotions. Stress reduced arousal and increased valence and success ratings for reappraisal in men, whereas no significant stress effects were found in women. Moreover, stressed men displayed a significant expansion of pupil diameter during reappraisal suggesting enhanced cognitive regulatory engagement, which ultimately may have led to better emotion regulation outcomes. Cortisol secretion positively correlated with subjective reappraisal success in men, suggesting a glucocorticoid-driven mechanism that may promote emotion regulatory performance in the aftermath of stress.
Prague Tectogrammatical Graphs (PTG) is a meaning representation framework that originates in the tectogrammatical layer of the Prague Dependency Treebank (PDT) and is theoretically founded in Functional Generative Description of language (FGD). PTG in its present form has been prepared for the CoNLL 2020 shared task on Cross-Framework Meaning Representation Parsing (MRP). It is generated automatically from the Prague treebanks and stored in the JSON-based MRP graph interchange format. The conversion is partially lossy; in this paper we describe what part of annotation was included and how it is represented in PTG.
Positive emotion regulation is important for enhancing psychological well-being. Previous studies have adopted a cross-sectional design to examine the relationships between positive emotion regulation strategies and psychological functioning. Relatively little is known about the role of positive emotion regulation strategies in positive and negative emotions in everyday life. This study examines associations between perceived capability of savoring and trait positive rumination and everyday emotions. Among 300 Hong Kong Chinese (age 18-77 years), perceived capability of savoring the moment and positive rumination predicted higher positive emotions during positive-valenced events. Perceived capability of savoring the moment predicted lower negative emotions during positive-valenced events and higher positive emotions during negative-valenced events. Positive links between positive valence ratings of events and positive emotions were stronger among individuals who reported higher levels of perceived capability of savoring through reminiscence and greater trait emotion-focused rumination. Positive links between negative valence ratings of events and negative emotions were significant only among individuals who reported low/medium levels of perceived capability of savoring through anticipation. These findings suggest that: (a) perceived capability of savoring and trait positive rumination are key mechanisms for increasing positive emotions and (b) perceived capability of savoring fosters adaptive adjustment to stress during negative events.
The deep inside-outside recursive autoencoder (DIORA; Drozdov et al. 2019a) is a selfsupervised neural model that learns to induce syntactic tree structures for input sentences without access to labeled training data. In this paper, we discover that while DIORA exhaustively encodes all possible binary trees of a sentence with a soft dynamic program, its vector averaging approach is locally greedy and cannot recover from errors when computing the highest scoring parse tree in bottom-up chart parsing. To fix this issue, we introduce S-DIORA, an improved variant of DIORA that encodes a single tree rather than a softlyweighted mixture of trees by employing a hard argmax operation and a beam at each cell in the chart. Our experiments show that through fine-tuning a pre-trained DIORA with our new algorithm, we improve the state of the art in unsupervised constituency parsing on the English WSJ Penn Treebank by 2.2 6% F1, depending on the data used for fine-tuning.
We report the results of our system on the Metaphor Detection Shared Task at the Second Workshop on Figurative Language Processing 2020. Our model is an ensemble, utilising contextualised and static distributional semantic representations, along with word-type concreteness ratings. Using these features, it predicts word metaphoricity with a deep multilayer perceptron. We are able to best the stateof-the-art from the 2018 Shared Task by an average of 8.0% F 1, and finish fourth in both subtasks in which we participate.
for free word order languages such as Myanmar. This paper describes a dependency head annotation scheme with Universal part-of-speech and Universal Dependencies for Myanmar dependency treebank. Currently 22,810 sentences and 680,218 tokens were annotated from three corpora for Myanmar dependency treebank. Some language specific issues are also described with examples. Raw syntactic structures were annotated automatically by UDPipe according to the Universal Dependencies based on Universalpart-of-speech tag scheme. Then unsupervised annotated dependency head structures have been manually updated in post processing. To be reliable and speedy post process with reduced errors for manual updating, selected sentences were added to the training data after being updated. After that the model has been retrained and the remaining sentences were parsed by UDPipe. Post processing was repeated until all sentences were updated. Some specifications of dependency annotation schemes in sentences encountered in post processing are presented with examples. For parsing performance of annotated data, cross validation tests and parsing experiments were performed. Moreover, annotated treebank data have also been evaluated by CoNLL 2017 evaluation script for parsing performance. Results of parsing experiments and evaluation are also reported by unlabeled and labeled attachment scores and demonstrated that the proposed method is a suitable way for building Myanmar dependency trees. Moreover, syntax structures of treebank are also analyzed and syntax information is also presented. This dependency head annotation for dependency treebank is the first work for Myanmar language as far as we know.
Implicit discourse relation recognition is a challenging task due to the lack of connectives as strong linguistic clues. Previous methods primarily encode two arguments separately or extract the specific interaction patterns for the task, which have not fully exploited the annotated relation signal. Therefore, we propose a novel TransS-driven joint learning architecture to address the issues. Specifically, based on the multi-level encoder, we 1) translate discourse relations in low-dimensional embedding space (called TransS), which could mine the latent geometric structure information of argumentrelation instances; 2) further exploit the semantic features of arguments to assist discourse understanding; 3) jointly learn 1) and 2) to mutually reinforce each other to obtain the better argument representations, so as to improve the performance of the task. Extensive experimental results on the Penn Discourse TreeBank (PDTB) show that our model achieves competitive results against several state-of-the-art systems.
The large communication cost for exchanging gradients between different nodes significantly limits the scalability of distributed training for large-scale learning models. Motivated by this observation, there has been significant recent interest in techniques that reduce the communication cost of distributed Stochastic Gradient Descent (SGD), with gradient sparsification techniques such as top-k and random-k shown to be particularly effective. The same observation has also motivated a separate line of work in distributed statistical estimation theory focusing on the impact of communication constraints on the estimation efficiency of different statistical models. The primary goal of this paper is to connect these two research lines and demonstrate how statistical estimation models and their analysis can lead to new insights in the design of communication-efficient training techniques. We propose a simple statistical estimation model for the stochastic gradients which captures the sparsity and skewness of their distribution. The statistically optimal communication scheme arising from the analysis of this model leads to a new sparsification technique for SGD, which concatenates random-k and top-k, considered separately in the prior literature. We show through extensive experiments on both image and language domains with CIFAR-10, ImageNet, and Penn Treebank datasets that the concatenated application of these two sparsification methods consistently and significantly outperforms either method applied alone.
This paper presents theoretical and methodological questions related to the creation of a Linguistic Database, made up of samples from the Cazumbá Iracema Extractive Reserve, located in the state of Acre, and discusses the main challenges found and contributions to the teaching and learning process of Portuguese. The methodology for collecting and organizing this database is based on the theoretical assumptions of sociolinguistic patterns, the empirical foundations of the Theory of Linguistic Variation and Change, and the methodology for collecting and manipulating data in sociolinguistics. The implementation of the proposal involves the use of software that can be used in education. The results show contributions of this sample use for the creation of teaching proposals, focusing on the language in use, identification of the sociocultural factors that influence the emergence and permanence of linguistic variation and researches in the scope of natural languages.
We tackle implicit discourse relation recognition. Both self-attention and interactive-attention mechanisms have been applied for attention-aware representation learning, which improves the current discourse analysis models. To take advantages of the two attention mechanisms simultaneously, we develop a propagative attention learning model using a cross-coupled two-channel network. We experiment on Penn Discourse Treebank. The test results demonstrate that our model yields substantial improvements over the baselines (BiLSTM and BERT).
Savoring is an emotion regulation technique that aims to increase, sustain, and deepen positive emotion. It has been incorporated into several novel, "positive affect" interventions for anxiety, depression, and chronic pain, but has not been studied in a laboratory setting. As such, it is unknown whether savoring can modulate subjective and neural correlates of emotion-processing and whether savoring might exert a persistent effect on stimulus processing (i.e., modulating response at subsequent encounter). Here, 49 participants savored or viewed positive and neutral pictures, before seeing the same pictures again approximately 20 min later without instructions to savor (or view) pictures. Subjective valence and arousal ratings and the picture-elicited late positive potential (LPP) were assessed during both tasks. Results showed that savoring increased participant ratings of picture pleasantness and arousal as well as a picture-elicited LPP. Moreover, pictures that had previously been savored continued to elicit higher ratings during the subsequent picture viewing task. A larger LPP was observed for previously savored positive and neutral pictures during an early portion of picture viewing; later on during picture viewing, this effect was limited to positive pictures only (i.e., it was not evident for neutral pictures). Results validate savoring as an effective and durable means of increasing positive emotion and are discussed in the context of a broader emotion regulation literature, which has primarily examined the downregulation of negative picture processing.
We show how we can adapt parsing to low-resource domains by combining treebanks across languages for a parser model with treebank embeddings. We demonstrate how we can take advantage of in-domain treebanks from other languages, and show that this is especially useful when only out-of-domain treebanks are available for the target language. The method is also extended to low-resource languages by using out-of-domain treebanks from related languages. Two parameter-free methods for applying treebank embeddings at test time are proposed, which give competitive results to tuned methods when applied to Twitter data and transcribed speech. This gives us a method for selecting treebanks and training a parser targeted at any combination of domain and language.
It is commonly believed that knowledge of syntactic structure should improve language modeling. However, effectively and computationally efficiently incorporating syntactic structure into neural language models has been a challenging topic. In this paper, we make use of a multi-task objective, i.e., the models simultaneously predict words as well as ground truth parse trees in a form called "syntactic distances", where information between these two separate objectives shares the same intermediate representation. Experimental results on the Penn Treebank and Chinese Treebank datasets show that when ground truth parse trees are provided as additional training signals, the model is able to achieve lower perplexity and induce trees with better quality. * Equal contribution.
The aim of this study is to present a robot-assisted therapy protocol for children with ASD based on the current state-of-the-art in both ASD intervention research and robotics research, and critically evaluate its adherence and acceptability based on child as well as parent ratings. The robot-assisted therapy was designed based on motivational components of Pivotal Response Treatment (PRT), a highly promising and feasible intervention focused at training "pivotal" (key) areas such as motivation for social interaction and self-initiations, with the goal of establishing collateral gains in untargeted areas of functioning and development, affected by autism spectrum disorders. Overall, children (3-8 y) could adhere to the robot-assisted therapy protocol (Mean percentage of treatment adherence 85.5%), showed positive affect ratings after therapy sessions (positive in 86.6% of sessions) and high robot likability scores (high in 79.4% of sessions). Positive likability ratings were mainly given by school-aged children (H(1) = 7.91, p =.005) and related to the movements, speech and game scenarios of the robot. Parent ratings on the added value of the robot were mainly positive (Mean of 84.8 on 0-100 scale), while lower parent ratings were related to inflexibility of robot behaviour.
Abnormalities in olfactory function have been identified in a number of neurological and psychiatric disorders, including Parkinson's disease and schizophrenia. However, little is known about olfactory function in autism spectrum disorder (ASD). The present study aims to assess the olfactory profiles of children with ASD, compared to an age- and sex-matched comparison group of typically developing children and a second clinical control group consisting of non-ASD children with sensory processing dysfunction (SPD). Participants completed a battery of sensory and behavioral assessments including olfactory tasks (Sniffin' Sticks Threshold Test and self-reported valence ratings for two target odorants (phenylethyl alcohol and vanillin) and the University of Pennsylvania Smell Identification Test), and an autism evaluation (Autism Diagnostic Observation Schedule-2). Children with ASD showed intact odor detection with reduced odor identification ability. Poor odor identification was significantly correlated with autism symptom severity. Children with SPD demonstrated reduced odor detection and identification ability. These findings provide evidence for differential patterns of smell processing among ASD and non-ASD neurodevelopmental disorders. Future studies are needed to determine whether the association of impaired olfaction and increased autism symptoms is due to shared etiology.
This is the first prerelease of PapyGreek annotations. The full release is located at https://doi.org/10.5281/zenodo.5062996.
The Icelandic Contemporary Corpus (IceConTree) is a machine-parsed treebank parsed according to the IcePaHC annotation scheme. It consists of texts from the Icelandic Gigaword Corpus, parsed using the IceNeuralParsingPipeline. It contains 524,601,329 words in 29,929,132 clauses. The treebank consists of 14 texts which are mainly media, law and parliamentary text. Within each text, files are divided according to years. This division was done after the text was parsed and is therefore not completely correct. The texts are labelled according to the following genres: • par: parliamentary text • spe: speech • law: law text • med: text from media • rad: text from radio • onl: text from the Internet • tel: text from television • enc: encyclopedia
We present the first Universal Dependencies treebank for Hittite. This paper expands on earlier efforts at Hittite corpus creation (Molina and Molin, 2016; Molina, 2016) and discussions of annotation guidelines for Hittite within the UD framework (Inglese, 2015; Inglese et al., 2018). We build on the expertise of the above works to create a small corpus which we hope will serve as a stepping-stone to more expansive UD treebanking for Hittite.
Despite achieving prominent performance on many important tasks, it has been reported that neural networks are vulnerable to adversarial examples. Previously studies along this line mainly focused on semantic tasks such as sentiment analysis, question answering and reading comprehension. In this study, we show that adversarial examples also exist in dependency parsing: we propose two approaches to study where and how parsers make mistakes by searching over perturbations to existing texts at sentence and phrase levels, and design algorithms to construct such examples in both of the black-box and white-box settings. Our experiments with one of state-of-the-art parsers on the English Penn Treebank (PTB) show that up to 77% of input examples admit adversarial perturbations, and we also show that the robustness of parsing models can be improved by crafting high-quality adversaries and including them in the training stage, while suffering little to no performance drop on the clean input data.
Cross-lingual dependency parsing approaches have been employed to develop dependency parsers for the languages for which little or no treebanks are available using the treebanks of other languages. A language for which the cross-lingual parser is developed is usually referred to as the target language and the language whose treebank is used to train the cross-lingual parser model is referred to as the source language. The cross-lingual parsing approaches for dependency parsing may be broadly classified into three categories: model transfer, annotation projection, and treebank translation. This survey provides an overview of the various aspects of the model transfer approach of cross-lingual dependency parsing. In this survey, we present a classification of the model transfer approaches based on the different aspects of the method. We discuss some of the challenges associated with cross-lingual parsing and the techniques used to address these challenges. In order to address the difference in vocabulary between two languages, some approaches use only non-lexical features of the words to train the models while others use shared representations of the words. Some approaches address the morphological differences by chunk-level transfer rather than word-level transfer. The syntactic differences between the source and target languages are sometimes addressed by transforming the source language treebanks or by combining the resources of multiple source languages. Besides cross-lingual transfer parser models may be developed for a specific target language or it may be trained to parse sentences of multiple languages. With respect to the above-mentioned aspects, we look at the different ways in which the methods can be classified. We further classify and discuss the different approaches from the perspective of the corresponding aspects. We also demonstrate the performance of the transferred models under different settings corresponding to the classification aspects on a common dataset.
In this paper we propose a novel data augmentation approach where guided outputs of a language generation model, e.g. GPT-2, when labeled, can improve the performance of text classifiers through an active learning process. We transform the data generation task into an optimization problem which maximizes the usefulness of the generated output, using Monte Carlo Tree Search (MCTS) as the optimization strategy and incorporating entropy as one of the optimization criteria. We test our approach against a Non-Guided Data Generation (NGDG) process that does not optimize for a reward function. Starting with a small set of data, our results show an increased performance with MCTS of 26% on the TREC-6 Questions dataset, and 10% on the Stanford Sentiment Treebank SST-2 dataset. Compared with NGDG, we are able to achieve increases of 3% and 5% on TREC-6 and SST-2.
Several studies have suggested that females and males differ in reward behaviors and their underlying neural circuitry. Whether human sex differences extend across neural and behavioral levels for both rewards and punishments remains unclear. We studied a community sample of 221 young women and men who performed a monetary incentive task known to engage the mesoaccumbal pathway and salience network. Both stimulus salience (behavioral relevance) and valence (win vs loss) varied during the task. In response to high- vs low-salience stimuli presented during the monetary incentive task, men showed greater subjective arousal ratings, behavioral accuracy and skin conductance responses (P < 0.006, Hedges' effect size g = 0.38 to 0.46). In a subsample studied with functional magnetic resonance imaging (n = 44), men exhibited greater responsiveness to stimulus salience in the nucleus accumbens, midbrain, anterior insula and dorsal anterior cingulate cortex (P < 0.02, g = 0.86 to 1.7). Behavioral, autonomic and neural sensitivity to the valence of stimuli did not differ by sex, indicating that responses to rewards vs punishments were similar in women and men. These results reveal novel and robust sex differences in reward- and punishment-related traits, behavior, autonomic activity and neural responses. These convergent results suggest a neurobehavioral basis for sexual dimorphism observed in the reward system, including reward-related disorders.
Most syntactic dependency parsing models may fall into one of two categories: transition- and graph-based models. The former models enjoy high inference efficiency with linear time complexity, but they rely on the stacking or re-ranking of partially-built parse trees to build a complete parse tree and are stuck with slower training for the necessity of dynamic oracle training. The latter, graph-based models, may boast better performance but are unfortunately marred by polynomial time inference. In this paper, we propose a novel parsing order objective, resulting in a novel dependency parsing model capable of both global (in sentence scope) feature extraction as in graph models and linear time inference as in transitional models. The proposed global greedy parser only uses two arc-building actions, left and right arcs, for projective parsing. When equipped with two extra non-projective arc-building actions, the proposed parser may also smoothly support non-projective parsing. Using multiple benchmark treebanks, including the Penn Treebank (PTB), the CoNLL-X treebanks, and the Universal Dependency Treebanks, we evaluate our parser and demonstrate that the proposed novel parser achieves good performance with faster training and decoding.
Abstract Two research traditions explain the way we deal with emotional situations: emotional intelligence (EI) and emotion regulation (ER). EI refers to the individual differences in the knowledge, identification, and regulation of emotions. ER describes processes in which emotions are experienced, expressed, and altered. Our study examined the EI-ER link and their moderating role on affective responses. We used self-report questionnaires and a cognitive reappraisal (CR) task, in which subjective affective responses were registered. We found that higher levels of ER difficulties correlated with lower EI. Gender had an overall impact on affective changes, indicating a more unpleasant and more arousing affective state for women compared with men. Regarding the moderating role of EI and ER difficulties, the ability to utilize emotions (Utilization) decreased the valence into a more unpleasant direction, similar to the effect of the inability to identify and differentiate emotions (Clarity). A weak control over emotions (Impulse), however, increased the valence into a more pleasant direction. The lack of attention to emotional signals (Awareness) marginally decreased the initial intensity (i.e., lower level of arousal). We demonstrated that EI and ER have distinctive routes and a different influence on the affective outcome defined by valence and arousal ratings: (1) EI has an impact through the utilization of emotions mainly on the valence dimension; and (2) individual differences in ER have a moderating effect on both valence and arousal dimensions. This study provided evidence on how individual differences contribute to a successful ER process when using a CR strategy.
This paper analyzes linguistic deficit discourse as it emerges in language gap research, gets appropriated by language gap foundations, and is reported in the media. Through intertextual analysis, we show how language deficit ideologies combine with neoliberal logic to normalize the marginalization of minoritized families, linguistic and sociolinguistic hierarchies, and the privileging of White middle-class (socio)linguistic norms. Language gap discourse turns parents into scapegoats by blaming them for the linguistic deficiencies of their children and low-income families are encouraged to misrecognize the inherent value of their communication abilities. IN the process, social processes that engender economic and educational inequality are obfuscated. Rather than attempting to find real answers to real problems, language gap discourse emphasizes a quick fix solution (filling your kids up with words) instead of engaging with the real causes of educational inequity.
We present our work of constructing the first treebank for the Xibe language following the Universal Dependencies (UD) annotation scheme. Xibe is a low-resourced and severely endangered Tungusic language spoken by the Xibe minority living in the Xinjiang Uygur Autonomous Region of China. We collected 810 sentences so far, including 544 sentences from a grammar book on written Xibe and 266 sentences from Cabcal News. We annotated those sentences manually from scratch. In this paper, we report the procedure of building this treebank and analyze several important annotation issues of our treebank. Finally, we propose our plans for future work.
Ouyang and Jiang (2018) measured the second language proficiency of English as a foreign language (EFL) learners based on the probability distribution of dependency distance. However, the typological features of the native language (Chinese) and the target language (English) they adopted are generally considered similar in word order and dependency direction. In addition, their method of classifying the learners’ proficiency levels is based on the learners’ grades, which might weaken the validity of the results. These results are strengthened and verified further in the current research by analysing a treebank of Japanese EFL learners’ interlanguage since their native language and the target language are typologically distinctive. Moreover, the TOEIC score was used as a benchmark to classify the second language proficiency levels of the learners. We found that (1) the mean dependency distance can measure the syntactic complexity of Japanese EFL learners’ interlanguage; (2) constrained by human working memory, the probability distribution of dependency distance based on Japanese EFL learners’ interlanguage follows certain distribution patterns as unveiled in other natural human languages; (3) the parameters of the right truncated modified Zipf-Alekseev distribution can well reflect the changes of the Japanese EFL learners’ second language proficiency, indicating the development of interlanguage.