Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
16504 papers
The chapter addresses the concept of linguistic norm in the tradition of classical grammar and rhetoric, paying special attention to activities concerning standardization processes in the Romance languages. Since a clear distinction between a prescriptive and a descriptive point of view is not given in "traditional grammar", the latter is manifested in the form of grammatical treatises which often also aimed at offering norms for "correct" language use. As a consequence thereof, our contribution will be concerned with aspects relating to the realm of the history of language sciences and, at least partially, to the history of rhetoric. The period taken into consideration ranges from Latin antiquity (Cicero, Quintilian) to the middle of the 17th century (Vaugelas). The topics to be discussed were selected with regard to the significance of the respective protagonists in the history of ideas in (Latin and) Romance language standardization.
Abstract The rise of world Englishes has challenged the emphasis on native-speaker accents and cultures in English language teaching. This study aimed to investigate the representation of world Englishes and cultures in three global language teaching textbooks, namely Interchange, English Result, and American English File. The textbooks were subjected to content analysis regarding their reference to Inner, Outer, and Expanding Circles’ varieties and their associated cultural contents. Kachru ( The alchemy of English: The spread functions and models of non-native Englishes. Oxford: Pergamon, 1986) notion of Concentric Circles, and the categorization proposed by Pfister and Borzelli ( Unterrichtspraxis 10:102–108, 1977) functioned as a framework to see which aspects of each culture (social, personal, religion/arts/humanities, political systems and institutions, and environmental concerns) were addressed in these textbooks. Findings revealed that most of the references to the three circles and cultural elements embodied in the textbooks were toward Inner Circle countries in American English File. Furthermore, in Interchange and English Result series, reference to Outer and Expanding Circles’ varieties and cultural elements were comparatively more evident. However, all the three textbook series mostly represented Inner-Circle accents. These findings have implications for materials developers to adopt an EIL-aware approach and to avoid the sole representation of native speakers’ linguistic norms and cultures in ELT textbooks.
Federated learning (FL) is a privacy-preserving technique for training a vast amount of decentralized data and making inferences on mobile devices. As a typical language modeling problem, mobile keyboard prediction aims at suggesting a probable next word or phrase and facilitating the human-machine interaction in a virtual keyboard of the smartphone or laptop. Mobile keyboard prediction with FL hopes to satisfy the growing demand that high-level data privacy be preserved in artificial intelligence applications even with the distributed models training. However, there are two major problems in the federated optimization for the prediction: (1) aggregating model parameters on the server-side and (2) reducing communication costs caused by model weights collection. To address the above issues, traditional FL methods simply use averaging aggregation or ignore communication costs. We propose a novel Federated Mediation (FedMed) framework with the adaptive aggregation, mediation incentive scheme, and topK strategy to address the model aggregation and communication costs. The performance is evaluated in terms of perplexity and communication rounds. Experiments are conducted on three datasets (i.e., Penn Treebank, WikiText-2, and Yelp) and the results demonstrate that our FedMed framework achieves robust performance and outperforms baseline approaches.
The training of Deep Neural Networks (DNNs) brings enormous memory requirements and computational complexity, which makes it a challenge to train DNN models on resource-constrained devices. Training DNNs with reduced-precision data representation is crucial to mitigate this problem. In this article, we conduct a thorough investigation on training DNNs with low-bit posit numbers, a Type-III universal number (Unum). Through a comprehensive analysis of quantization with various data formats, it is demonstrated that the posit format shows great potential to be employed in the training of DNNs. Moreover, a DNN training framework using 8-bit posit is proposed with a novel tensor-wise scaling scheme. The experiments show the same performance as the state-of-the-art (SOTA) across multiple datasets (MNIST, CIFAR-10, ImageNet, and Penn Treebank) and model architectures (LeNet-5, AlexNet, ResNet, MobileNet-V2, and LSTM). We further design an energy-efficient hardware prototype for our framework. Compared to the standard floating-point counterpart, our design achieves a reduction of 68, 51, and 75 percent in terms of area, power, and memory capacity, respectively.
We trained a model to distinguish an extreme high arousal, unpleasant drink from regular drinks based on a range of implicit behavioral and physiological responses to naturalistic tasting. The trained model predicted arousal ratings of regular drinks, highlighting the possibility to estimate affective experience without having to rely on subjective ratings.
This qualitative study investigates the lived experiences of interculturality among international students enrolled in a top-rated comprehensive university in Shanghai and a key provincial university in China’s hinterland. Based on an analysis of narrative interviews with 20 international students from different sociocultural backgrounds, the study examines how the international students’ intercultural experiences are simultaneously constrained and facilitated by the linguacultural resources distributed among interactants in the universities and cities. The results suggest that the international students’ intercultural experiences differ in substantial ways, mediated by varied distributions of sociolinguistic resources which are closely associated with their academic socialisation through English, local interactional norms, relevant career opportunities, and linguistic norms for everyday interaction with both Chinese students and local residents in the two universities. It is also found that international students play agentive roles to construct and negotiate scales among the sociolinguistic resources at their disposal, in order to create meanings for and reflect upon their lived experiences of interculturality in relation to their previous and prospective life trajectories.
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.
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.
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.
Implicit relation classification on Penn Discourse TreeBank (PDTB) 2.0 is a common benchmark task for evaluating the understanding of discourse relations. However, the lack of consistency in preprocessing and evaluation poses challenges to fair comparison of results in the literature. In this work, we highlight these inconsistencies and propose an improved evaluation protocol. Paired with this protocol, we report strong baseline results from pretrained sentence encoders, which set the new state-of-the-art for PDTB 2.0. Furthermore, this work is the first to explore fine-grained relation classification on PDTB 3.0. We expect our work to serve as a point of comparison for future work, and also as an initiative to discuss models of larger context and possible data augmentations for downstream transferability.
Foreign language education primarily aims to cultivate learners’ competence to communicate in an additional language. However, the meaning of communication competence is not entirely transparent, especially given the current neoliberal valorization of communication in the knowledge economy. The meaning of communication can be scrutinized in two contradictory trends observed in language education: the exclusive focus on teaching English as a global language, signifying a homogenizing trend, and increased scholarly attention to the heterogeneity of linguistic forms and practices. This article examines how communication competence is differentially understood by policymakers and corporate workers in Japan. The authors examine a government report that evaluated the attainment of educational goals for coping with globalization and contrasting it with interview data drawn from another study on the communicative experiences of Japanese transnational workers in Asia. Political discourse analysis and content analysis reveal the paradoxical nature of what can be called neoliberal communication competence, which on the one hand conflates global communication with use of the four measurable skills in English to transmit information and, on the other hand, challenges linguistic norms, foregrounding plurilingualism and co‐constructed interactional competence. Transformation of policies and pedagogies can be pursued by appropriating neoliberal communication competence for achieving broader educational goals.
In this paper we describe and evaluate syntactic and semantic aspects of Arbobanko, a treebank for the artificial language Esperanto, as well as tools and methods used in the production of the treebank. In addition to classical morphosyntax and dependency structure, the treebank was enriched with a lexical-semantic layer covering named entities, a semantic type ontology for nouns and adjectives and a framenet-inspired semantic classification of verbs. For an under-resourced language, the quality of automatic syntactic and semantic pre-annotation is of obvious importance, and by evaluating the underlying parser and the coverage of its semantic ontologies, we try to answer the question whether the language's extremely regular morphology and transparent semantic affixes translate into a more regular syntax and higher parsing accuracy. On the linguistic side, the treebank allows us to address and quantify typological issues such as the question of word order, auxiliary constructions, lexical transparency and semantic type ambiguity in Esperanto.<br/>
Abstract ICON Biased affective ratings and reduced electrodermal responses in women with fibromyalgia Jennifer Héraut, MSc., Khira El Bouragui, PhD, Alice Bodart, MSc., Laurent Lefebvre, PhD, Mandy Rossignol, PhD Cognitive psychology and neuropsychology department. University of Mons, Belgium. Corresponding author: Mandy.Rossignol@umons.ac.be Objective: To evaluate the relationship between electrodermal response and emotional appraisal of tactile and visual stimuli in women with fibromyalgia as compared to healthy controls. Research question: Fibromyalgia syndrome (FMS) is a chronic disorder characterized by widespread musculoskeletal pain, fatigue and mood issues. The central sensitization theory postulates that pain in FMS comes from an abnormal processing of sensory impulses. However, altered emotional appraisal may also contribute to complains in FMS. This study aimed to evaluate whether FMS disturbs subjective appraisal and electrodermal activity in response to neutral and emotional pictures, and to non-painful tactile stimuli. Methods: 22 women (Mean age = 45.86; SD = 12.03) diagnosed with FMS and 20 healthy control (HC) women paired on age and education took part to the experiment. Participants completed the Beck Depression Inventory - 13 items before to complete the experimental task consisting to appraise 45 positive, negative and neutral pictures from the IAPS and eight tactile stimulations (two for each of the four categories - tact, pressure, temperature, vibration). After each stimulus, participants had to provide an evaluation using the Self-Assessment Manikin (SAM) on three dimensions: valence, arousal and dominance. Moreover, electrodermal activity (EDA) was monitored during the entire experience. Results: First, EDA did not differ between groups for visual stimuli, but FMS women evaluated positive pictures more negatively than HC. Second, FMS women produced reduced EDA for tactile stimuli as compared to HC, without group difference on the subjective appraisal of these stimuli Finally, FMS patients showed greater levels of depression than HC and, in the complete sample, depression score was correlated to reduced EDA responses for visual and tactile stimuli. Conclusions: First, we suggest that deficits in subjective appraisal and EDA responses were not associated in FMS, since EDA reduction appears for tactile stimuli without modification of subjective appraisal, while the reverse was observed for positive visual stimuli. Second, depression may mediate the reduction of EDA in response to visual stimuli and increases the affective disturbances in these patients. Our results suggest a dissociation between physiological responses and subjective evaluation of external stimuli in FMS, as well as a need to distinguish effects due to FMS from those linked to low mood and depressive affects. Poster or Open Paper Keywords: Fibromyalgia, emotion, emotional rating electrodermal activity, IAPS.
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.
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.
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
This is the first prerelease of PapyGreek annotations. The full release is located at https://doi.org/10.5281/zenodo.5062996.
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.
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.
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.
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.
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 present a named entity annotation for the Danish Universal Dependencies treebank using the CoNLL-2003 annotation scheme: DaNE. It is the largest publicly available, Danish named entity gold annotation. We evaluate the quality of our annotations intrinsically by double annotating the entire treebank and extrinsically by comparing our annotations to a recently released named entity annotation of the validation and test sections of the Danish Universal Dependencies treebank. We benchmark the new resource by training and evaluating competitive architectures for supervised named entity recognition (NER), including FLAIR, monolingual (Danish) BERT and multilingual BERT. We explore cross-lingual transfer in multilingual BERT from five related languages in zero-shot and direct transfer setups, and we show that even with our modestly-sized training set, we improve Danish NER over a recent cross-lingual approach, as well as over zero-shot transfer from five related languages. Using multilingual BERT, we achieve higher performance by fine-tuning on both DaNE and a larger Bokmål (Norwegian) training set compared to only using DaNE. However, the highest performance is achieved by using a Danish BERT fine-tuned on DaNE. Our dataset enables improvements and applicability for Danish NER beyond cross-lingual methods. We employ a thorough error analysis of the predictions of the best models for seen and unseen entities, as well as their robustness on un-capitalized text. The annotated dataset and all the trained models are made publicly available.
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).
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.
Both syntactic and semantic structures are key linguistic contextual clues, in which parsing the latter has been well shown beneficial from parsing the former. However, few works ever made an attempt to let semantic parsing help syntactic parsing. As linguistic representation formalisms, both syntax and semantics may be represented in either span (constituent/phrase) or dependency, on both of which joint learning was also seldom explored. In this paper, we propose a novel joint model of syntactic and semantic parsing on both span and dependency representations, which incorporates syntactic information effectively in the encoder of neural network and benefits from two representation formalisms in a uniform way. The experiments show that semantics and syntax can benefit each other by optimizing joint objectives. Our single model achieves new state-of-the-art or competitive results on both span and dependency semantic parsing on Propbank benchmarks and both dependency and constituent syntactic parsing on Penn Treebank.
Text discourse parsing plays an important role in understanding information flow and argumentative structure in natural language. Previous research under the Rhetorical Structure Theory (RST) has mostly focused on inducing and evaluating models from the English treebank. However, the parsing tasks for other languages such as German, Dutch, and Portuguese are still challenging due to the shortage of annotated data. In this work, we investigate two approaches to establish a neural, cross-lingual discourse parser via: (1) utilizing multilingual vector representations; and
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.
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.
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.
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.
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.
Parsing sentences into syntax trees can benefit downstream applications in NLP. Transition-based parsers build trees by executing actions in a state transition system. They are computationally efficient, and can leverage machine learning to predict actions based on partial trees. However, existing transition-based parsers are predominantly based on the shift-reduce transition system, which does not align with how humans are known to parse sentences. Psycholinguistic research suggests that human parsing is strongly incremental: humans grow a single parse tree by adding exactly one token at each step. In this paper, we propose a novel transition system called attach-juxtapose. It is strongly incremental; it represents a partial sentence using a single tree; each action adds exactly one token into the partial tree. Based on our transition system, we develop a strongly incremental parser. At each step, it encodes the partial tree using a graph neural network and predicts an action. We evaluate our parser on Penn Treebank (PTB) and Chinese Treebank (CTB). On PTB, it outperforms existing parsers trained with only constituency trees; and it performs on par with state-of-the-art parsers that use dependency trees as additional training data. On CTB, our parser establishes a new state of the art. Code is available at https://github.com/princeton-vl/attach-juxtapose-parser.
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.
Berkeley Neural Parser model for Korean using the Sejong treebank Berkeley Neural Parser https://github.com/nikitakit/self-attentive-parser COLLINS-SJTREE.prm for evalb is available at http://doi.org/10.5281/zenodo.1004604
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.
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.
With the rapid development of big data and deep learning, breakthroughs have been made in phonetic and textual research, the two fundamental attributes of language. Language is an essential medium of information exchange in teaching activity. The aim is to promote the transformation of the training mode and content of translation major and the application of the translation service industry in various fields. Based on previous research, the SCN-LSTM (Skip Convolutional Network and Long Short Term Memory) translation model of deep learning neural network is constructed by learning and training the real dataset and the public PTB (Penn Treebank Dataset). The feasibility of the model's performance, translation quality, and adaptability in practical teaching is analyzed to provide a theoretical basis for the research and application of the SCN-LSTM translation model in English teaching. The results show that the capability of the neural network for translation teaching is nearly one times higher than that of the traditional N-tuple translation model, and the fusion model performs much better than the single model, translation quality, and teaching effect. To be specific, the accuracy of the SCN-LSTM translation model based on deep learning neural network is 95.21%, the degree of translation confusion is reduced by 39.21% compared with that of the LSTM (Long Short Term Memory) model, and the adaptability is 0.4 times that of the N-tuple model. With the highest level of satisfaction in practical teaching evaluation, the SCN-LSTM translation model has achieved a favorable effect on the translation teaching of the English major. In summary, the performance and quality of the translation model are improved significantly by learning the language characteristics in translations by teachers and students, providing ideas for applying machine translation in professional translation teaching.
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.
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.