Miracle-dz/UniFAR-FTUs huggingface dataset:
Elevated Expression of MiR-17 in Microglia of Alzheimer’s Disease Patients Abrogates Autophagy-Mediated Amyloid-β Degradation

Autophagy is a proposed route of amyloid-β (Aβ) clearance by microglia that is halted in Alzheimer’s Disease (AD), though mechanisms underlying this dysfunction remain elusive. Here, primary microglia from adult AD (5xFAD) mice were utilized to demonstrate that 5xFAD microglia fail to degrade Aβ and express low levels of autophagy cargo receptor NBR1. In 5xFAD mouse brains, we show for the first time that AD microglia express elevated levels of microRNA cluster Mirc1/Mir17-92a, which is known to downregulate autophagy proteins. By in situ hybridization in post-mortem AD human tissue sections, we observed that the Mirc1/Mir17-92a cluster member miR-17 is also elevated in human AD microglia, specifically in the vicinity of Aβ deposits, compared to non-disease controls. We show that NBR1 expression is negatively correlated with expression of miR-17 in human AD microglia via immunohistopathologic staining in human AD brain tissue sections. We demonstrate in healthy microglia that autophagy cargo receptor NBR1 is required for Aβ degradation. Inhibiting elevated miR-17 in 5xFAD mouse microglia improves Aβ degradation, autophagy, and NBR1 puncta formation in vitro and improves NBR1 expression in vivo. These findings offer a mechanism behind dysfunctional autophagy in AD microglia which may be useful for therapeutic interventions aiming to improve autophagy function in AD.
Associations Between the Kynurenine Pathway, Proinflammatory Cytokines, and Brain-Derived Neurotrophic Factor in Hospitalized Patients With Chronic Schizophrenia: A Preliminary Study

Purpose: The kynurenine (Kyn) pathway may play a role in the pathophysiology of schizophrenia. This pathway shows crosstalk with proinflammatory cytokines, including interleukin-1β (IL-1β), IL-6, and tumor necrosis factor-α (TNF-α), and/or brain-derived neurotrophic factor (BDNF). Moreover, Kyn metabolites affect neurotransmission and cause neurotoxicity. To date, the influence of the Kyn pathway on proinflammatory cytokines and BDNF remains to be fully elucidated. The aim of this study was to investigate the relationships of the Kyn pathway with proinflammatory cytokines, BDNF, and psychiatric symptoms in patients with schizophrenia. Methods: Thirty patients with schizophrenia and ten healthy control participants were recruited for this study. All patients were diagnosed with schizophrenia using the Diagnostic and Statistical Manual for Mental Disorders, Fifth Edition (DSM-5). The healthy controls were those who did not fulfill any of the diagnostic criteria in the DSM-5. The serum levels of Kyn and its metabolites, proinflammatory cytokines, and BDNF were measured in patients with schizophrenia and healthy controls. Patients with schizophrenia were also assessed for psychiatric symptoms using the Positive and Negative Syndrome Scale (PANSS). Results: Patients with schizophrenia and healthy controls showed no significant differences in the levels of Kyn and its metabolites, proinflammatory cytokines, and BDNF. A significant positive correlation was found between the serum levels of TNF-α and Kyn (r = 0.53, p = 0.0026) and the Kyn/tryptophan (Trp) value (r = 0.67, p = 0.000046) in the schizophrenia group, but not in the healthy control group. Conclusion: TNF-α affects the Kyn pathway in patients with chronic schizophrenia, but not in the healthy individuals, although serum TNF-α levels showed no difference between the two groups. Associations between the Kyn pathway and the levels of proinflammatory cytokines and BDNF or psychotic symptoms might be complicated in hospitalized patients with chronic schizophrenia.
RedwoodNLP at SemEval-2021 Task 7: Ensembled Pretrained and Lightweight Models for Humor Detection

An understanding of humor is an essential component of human-facing NLP systems. In this paper, we investigate several methods for detecting humor in short statements as part of Semeval-2021 Shared Task 7. For Task 1a, we apply an ensemble of fine-tuned pre-trained language models; for Tasks 1b, 1c, and 2a, we investigate various tree-based and linear machine learning models. Our final system achieves an F1-score of 0.9571 (ranked 24 / 58) on Task 1a, an RMSE of 0.5580 (ranked 18 / 50) on Task 1b, an F1-score of 0.5024 (ranked 26 / 36) on Task 1c, and an RMSE of 0.7229 (ranked 45 / 48) on Task 2a.
Assessing the Representations of Idiomaticity in Vector Models with a Noun Compound Dataset Labeled at Type and Token Levels

Accurate assessment of the ability of embedding models to capture idiomaticity may require evaluation at token rather than type level, to account for degrees of idiomaticity and possible ambiguity between literal and idiomatic usages. However, most existing resources with annotation of idiomaticity include ratings only at type level. This paper presents the Noun Compound Type and Token Idiomaticity (NCTTI) dataset, with human annotations for 280 noun compounds in English and 180 in Portuguese at both type and token level. We compiled 8,725 and 5,091 token level annotations for English and Portuguese, respectively, which are strongly correlated with the corresponding scores obtained at type level. The NCTTI dataset is used to explore how vector space models reflect the variability of idiomaticity across sentences. Several experiments using state-of-the-art contextualised models suggest that their representations are not capturing the noun compounds idiomaticity as human annotators. This new multilingual resource also contains suggestions for paraphrases of the noun compounds both at type and token levels, with uses for lexical substitution or disambiguation in context.
Gas concentration measurement based on fibercircled cavity ring-down method

In order to realize the trace gas concentration on-line measurement and analysis,a multi-type gas concentration on-line measurement system based on fiber-circled cavity ring-down method was designed and developed.Based on the nonlinear optical cavity ring-down technology,the experimental analysis of the gas concentration in-situ measurement with such system was performed.The gas-to-light absorption characteristics of the same gas with different concentrations changed with time and their relationship was given.The resolution of concentration measurement reaches 1 ppb.The experiment proves that the system can be used to measure trace gas concentration effectively.
End-to-End AMR Coreference Resolution

Although parsing to Abstract Meaning Representation (AMR) has become very popular and AMR has been shown effective on the many sentence-level downstream tasks, little work has studied how to generate AMRs that can represent multi-sentence information. We introduce the first end-to-end AMR coreference resolution model in order to build multi-sentence AMRs. Compared with the previous pipeline and rule-based approaches, our model alleviates error propagation and it is more robust for both in-domain and out-domain situations. Besides, the document-level AMRs obtained by our model can significantly improve over the AMRs generated by a rule-based method (Liu et al., 2015) on text summarization.
N-ary Constituent Tree Parsing with Recursive Semi-Markov Model

In this paper, we study the task of graph-based constituent parsing in the setting that binarization is not conducted as a pre-processing step, where a constituent tree may consist of nodes with more than two children. Previous graph-based methods on this setting typically generate hidden nodes with the dummy label inside the n-ary nodes, in order to transform the tree into a binary tree for prediction. The limitation is that the hidden nodes break the sibling relations of the n-ary node’s children. Consequently, the dependencies of such sibling constituents might not be accurately modeled and is being ignored. To solve this limitation, we propose a novel graph-based framework, which is called “recursive semi-Markov model”. The main idea is to utilize 1-order semi-Markov model to predict the immediate children sequence of a constituent candidate, which then recursively serves as a child candidate of its parent. In this manner, the dependencies of sibling constituents can be described by 1-order transition features, which solves the above limitation. Through experiments, the proposed framework obtains the F1 of 95.92% and 92.50% on the datasets of PTB and CTB 5.1 respectively. Specially, the recursive semi-Markov model shows advantages in modeling nodes with more than two children, whose average F1 can be improved by 0.3-1.1 points in PTB and 2.3-6.8 points in CTB 5.1.
Enhancing Entity Boundary Detection for Better Chinese Named Entity Recognition

In comparison with English, due to the lack of explicit word boundary and tenses information, Chinese Named Entity Recognition (NER) is much more challenging. In this paper, we propose a boundary enhanced approach for better Chinese NER. In particular, our approach enhances the boundary information from two perspectives. On one hand, we enhance the representation of the internal dependency of phrases by an additional Graph Attention Network(GAT) layer. On the other hand, taking the entity head-tail prediction (i.e., boundaries) as an auxiliary task, we propose an unified framework to learn the boundary information and recognize the NE jointly. Experiments on both the OntoNotes and the Weibo corpora show the effectiveness of our approach.
The Na⁺/H⁺ exchanger NHE1 plays a permissive role in regulating early neurite morphogenesis

The ubiquitously expressed plasma membrane Na/H exchanger isoform 1 (NHE1) plays an important role in directed cell migration in non-neuronal cells, an effect which requires both the ion translocation and actin cytoskeleton anchoring functions of the protein. In the present study, an analogous role for NHE1 as a modulator of neurite outgrowth was evaluated in vitro utilizing NGF-differentiated PC12 cells as well as mouse neocortical neurons in primary culture. Examined at 3 d.i. v. , endogenous NHE1 was found to be expressed in growth cones, where it gave rise to an elevated intracellular pH in actively-extending neurites. Application of the NHE inhibitor cariporide at an NHE1-selective concentration (1 μM) resulted in reductions in neurite extension and elaboration while application of 100 μM cariporide, to inhibit all known plasmalemmal NHE isoforms, failed to exert additional inhibitory effects, suggesting a dominant role for the NHE1 isoform in modulating neurite outgrowth. In addition, whereas transient overexpression of full-length NHE1 enhanced neurite outgrowth in a cariporide-sensitive manner in both NGF-differentiated PC12 cells and WT neocortical neurons, neurite outgrowth was reduced in NGF-differentiated PC12 cells overexpressing NHE1 mutants deficient in either ion translocation activity or actin cytoskeleton anchoring, suggesting that both functional domains of NHE1 are important for modulating neurite elaboration. A role for NHE1 in modulating neurite outgrowth was confirmed in neocortical neurons obtained from NHE1 mice which displayed reduced neurite outgrowth when compared to neurons obtained from their NHE1 littermates. Further, neurite outgrowth in NHE1 neurons was rescued by transient overexpression of fulllength NHE1 but not with mutant NHE1 constructs again suggesting that both functional domains of NHE1 are important for modulating neurite outgrowth. Finally, the growth promoting effects of netrin-1 but not BDNF or IGF-1 were abolished by cariporide in WT neocortical neurons and while both BDNF and IGF-1 were able to promote neurite outgrowth in iii NHE1 neurons, netrin-1 was unable to elicit this effect. Taken together, these results indicate that NHE1 is a permissive regulator of early neurite morphogenesis and also plays a novel role in netrin-1-stimulated neurite outgrowth.
Counterfactuals to Control Latent Disentangled Text Representations for Style Transfer

Disentanglement of latent representations into content and style spaces has been a commonly employed method for unsupervised text style transfer. These techniques aim to learn the disentangled representations and tweak them to modify the style of a sentence. In this paper, we propose a counterfactual-based method to modify the latent representation, by posing a ‘what-if’ scenario. This simple and disciplined approach also enables a fine-grained control on the transfer strength. We conduct experiments with the proposed methodology on multiple attribute transfer tasks like Sentiment, Formality and Excitement to support our hypothesis.
KACE: Generating Knowledge Aware Contrastive Explanations for Natural Language Inference

In order to better understand the reason behind model behaviors (i.e., making predictions), most recent works have exploited generative models to provide complementary explanations. However, existing approaches in NLP mainly focus on “WHY A” rather than contrastive “WHY A NOT B”, which is shown to be able to better distinguish confusing candidates and improve data efficiency in other research fields.In this paper, we focus on generating contrastive explanations with counterfactual examples in NLI and propose a novel Knowledge-Aware Contrastive Explanation generation framework (KACE). Specifically, we first identify rationales (i.e., key phrases) from input sentences, and use them as key perturbations for generating counterfactual examples. After obtaining qualified counterfactual examples, we take them along with original examples and external knowledge as input, and employ a knowledge-aware generative pre-trained language model to generate contrastive explanations. Experimental results show that contrastive explanations are beneficial to fit the scenarios by clarifying the difference between the predicted answer and other possible wrong ones. Moreover, we train an NLI model enhanced with contrastive explanations and achieves an accuracy of 91.9% on SNLI, gaining improvements of 5.7% against ETPA (“Explain-Then-Predict-Attention”) and 0.6% against NILE (“WHY A”).
A Neural Transition-based Model for Argumentation Mining

The goal of argumentation mining is to automatically extract argumentation structures from argumentative texts. Most existing methods determine argumentative relations by exhaustively enumerating all possible pairs of argument components, which suffer from low efficiency and class imbalance. Moreover, due to the complex nature of argumentation, there is, so far, no universal method that can address both tree and non-tree structured argumentation. Towards these issues, we propose a neural transition-based model for argumentation mining, which incrementally builds an argumentation graph by generating a sequence of actions, avoiding inefficient enumeration operations. Furthermore, our model can handle both tree and non-tree structured argumentation without introducing any structural constraints. Experimental results show that our model achieves the best performance on two public datasets of different structures.
Duluth at SemEval-2021 Task 11: Applying DeBERTa to Contributing Sentence Selection and Dependency Parsing for Entity Extraction

This paper describes the Duluth system that participated in SemEval-2021 Task 11, NLP Contribution Graph. It details the extraction of contribution sentences and scientific entities and their relations from scholarly articles in the domain of Natural Language Processing. Our solution uses deBERTa for multi-class sentence classification to extract the contributing sentences and their type, and dependency parsing to outline each sentence and extract subject-predicate-object triples. Our system ranked fifth of seven for Phase 1: end-to-end pipeline, sixth of eight for Phase 2 Part 1: phrases and triples, and fifth of eight for Phase 2 Part 2: triples extraction.
Hierarchical Context-aware Network for Dense Video Event Captioning

Dense video event captioning aims to generate a sequence of descriptive captions for each event in a long untrimmed video. Video-level context provides important information and facilities the model to generate consistent and less redundant captions between events. In this paper, we introduce a novel Hierarchical Context-aware Network for dense video event captioning (HCN) to capture context from various aspects. In detail, the model leverages local and global context with different mechanisms to jointly learn to generate coherent captions. The local context module performs full interaction between neighbor frames and the global context module selectively attends to previous or future events. According to our extensive experiment on both Youcook2 and Activitynet Captioning datasets, the video-level HCN model outperforms the event-level context-agnostic model by a large margin. The code is available at https://github.com/KirkGuo/HCN.
Adversarial Learning for Discourse Rhetorical Structure Parsing

Text-level discourse rhetorical structure (DRS) parsing is known to be challenging due to the notorious lack of training data. Although recent top-down DRS parsers can better leverage global document context and have achieved certain success, the performance is still far from perfect. To our knowledge, all previous DRS parsers make local decisions for either bottom-up node composition or top-down split point ranking at each time step, and largely ignore DRS parsing from the global view point. Obviously, it is not sufficient to build an entire DRS tree only through these local decisions. In this work, we present our insight on evaluating the pros and cons of the entire DRS tree for global optimization. Specifically, based on recent well-performing top-down frameworks, we introduce a novel method to transform both gold standard and predicted constituency trees into tree diagrams with two color channels. After that, we learn an adversarial bot between gold and fake tree diagrams to estimate the generated DRS trees from a global perspective. We perform experiments on both RST-DT and CDTB corpora and use the original Parseval for performance evaluation. The experimental results show that our parser can substantially improve the performance when compared with previous state-of-the-art parsers.
Stanford MLab at SemEval-2021 Task 8: 48 Hours Is All You Need

This paper presents our system for the Quantity span identification, Unit of measurement identification and Value modifier classification subtasks of the MeasEval 2021 task. The purpose of the Quantity span identification task was to locate spans of text that contain a count or measurement, consisting of a value, usually followed by a unit and occasionally additional modifiers. The goal of the modifier classification task was to determine whether an associated text fragment served to indicate range, tolerance, mean value, etc. of a quantity. The developed systems used pre-trained BERT models which were fine-tuned for the task at hand. We present our system, investigate how architectural decisions affected model predictions, and conduct an error analysis. Overall, our system placed 12 / 19 in the shared task and in the 2nd place for the Unit subcategory.
DuluthNLP at SemEval-2021 Task 7: Fine-Tuning RoBERTa Model for Humor Detection and Offense Rating

This paper presents the DuluthNLP submission to Task 7 of the SemEval 2021 competition on Detecting and Rating Humor and Offense. In it, we explain the approach used to train the model together with the process of fine-tuning our model in getting the results. We focus on humor detection, rating, and of-fense rating, representing three out of the four subtasks that were provided. We show that optimizing hyper-parameters for learning rate, batch size and number of epochs can increase the accuracy and F1 score for humor detection
TUDA-CCL at SemEval-2021 Task 1: Using Gradient-boosted Regression Tree Ensembles Trained on a Heterogeneous Feature Set for Predicting Lexical Complexity

In this paper, we present our systems submitted to SemEval-2021 Task 1 on lexical complexity prediction.The aim of this shared task was to create systems able to predict the lexical complexity of word tokens and bigram multiword expressions within a given sentence context, a continuous value indicating the difficulty in understanding a respective utterance. Our approach relies on gradient boosted regression tree ensembles fitted using a heterogeneous feature set combining linguistic features, static and contextualized word embeddings, psycholinguistic norm lexica, WordNet, word- and character bigram frequencies and inclusion in wordlists to create a model able to assign a word or multiword expression a context-dependent complexity score. We can show that especially contextualised string embeddings can help with predicting lexical complexity.
POS-Constrained Parallel Decoding for Non-autoregressive Generation

The multimodality problem has become a major challenge of existing non-autoregressive generation (NAG) systems. A common solution often resorts to sequence-level knowledge distillation by rebuilding the training dataset through autoregressive generation (hereinafter known as “teacher AG”). The success of such methods may largely depend on a latent assumption, i.e., the teacher AG is superior to the NAG model. However, in this work, we experimentally reveal that this assumption does not always hold for the text generation tasks like text summarization and story ending generation. To provide a feasible solution to the multimodality problem of NAG, we propose incorporating linguistic structure (Part-of-Speech sequence in particular) into NAG inference instead of relying on teacher AG. More specifically, the proposed POS-constrained Parallel Decoding (POSPD) method aims at providing a specific POS sequence to constrain the NAG model during decoding. Our experiments demonstrate that POSPD consistently improves NAG models on four text generation tasks to a greater extent compared to knowledge distillation. This observation validates the necessity of exploring the alternatives for sequence-level knowledge distillation.
Concept-Based Label Embedding via Dynamic Routing for Hierarchical Text Classification

Hierarchical Text Classification (HTC) is a challenging task that categorizes a textual description within a taxonomic hierarchy. Most of the existing methods focus on modeling the text. Recently, researchers attempt to model the class representations with some resources (e.g., external dictionaries). However, the concept shared among classes which is a kind of domain-specific and fine-grained information has been ignored in previous work. In this paper, we propose a novel concept-based label embedding method that can explicitly represent the concept and model the sharing mechanism among classes for the hierarchical text classification. Experimental results on two widely used datasets prove that the proposed model outperforms several state-of-the-art methods. We release our complementary resources (concepts and definitions of classes) for these two datasets to benefit the research on HTC.