Miracle-dz/UniFAR-FTUs huggingface dataset:
Meta-Information Guided Meta-Learning for Few-Shot Relation Classification

Few-shot classification requires classifiers to adapt to new classes with only a few training instances. State-of-the-art meta-learning approaches such as MAML learn how to initialize and fast adapt parameters from limited instances, which have shown promising results in few-shot classification. However, existing meta-learning models solely rely on implicit instance-based statistics, and thus suffer from instance unreliability and weak interpretability. To solve this problem, we propose a novel meta-information guided meta-learning (MIML) framework, where semantic concepts of classes provide strong guidance for meta-learning in both initialization and adaptation. In effect, our model can establish connections between instance-based information and semantic-based information, which enables more effective initialization and faster adaptation. Comprehensive experimental results on few-shot relation classification demonstrate the effectiveness of the proposed framework. Notably, MIML achieves comparable or superior performance to humans with only one shot on FewRel evaluation.
On the Helpfulness of Document Context to Sentence Simplification

Most of the research on text simplification is limited to sentence level nowadays. In this paper, we are the first to investigate the helpfulness of document context on sentence simplification and apply it to the sequence-to-sequence model. We firstly construct a sentence simplification dataset in which the contexts for the original sentence are provided by Wikipedia corpus. The new dataset contains approximately 116K sentence pairs with context. We then propose a new model that makes full use of the context information. Our model uses neural networks to learn the different effects of the preceding sentences and the following sentences on the current sentence and applies them to the improved transformer model. Evaluated on the newly constructed dataset, our model achieves 36.52 on SARI value, which outperforms the best performing model in the baselines by 2.46 (7.22%), indicating that context indeed helps improve sentence simplification. In the ablation experiment, we show that using either the preceding sentences or the following sentences as context can significantly improve simplification.
WAUC: A Multi-Modal Database for Mental Workload Assessment Under Physical Activity

Assessment of mental workload is crucial for applications that require sustained attention and where conditions such as mental fatigue and drowsiness must be avoided. Previous work that attempted to devise objective methods to model mental workload were mainly based on neurological or physiological data collected when the participants performed tasks that did not involve physical activity. While such models may be useful for scenarios that involve static operators, they may not apply in real-world situations where operators are performing tasks under varying levels of physical activity, such as those faced by first responders, firefighters, and police officers. Here, we describe WAUC, a multimodal database of mental Workload Assessment Under physical aCtivity. The study involved 48 participants who performed the NASA Revised Multi-Attribute Task Battery II under three different activity level conditions. Physical activity was manipulated by changing the speed of a stationary bike or a treadmill. During data collection, six neural and physiological modalities were recorded, namely: electroencephalography, electrocardiography, breathing rate, skin temperature, galvanic skin response, and blood volume pulse, in addition to 3-axis accelerometry. Moreover, participants were asked to answer the NASA Task Load Index questionnaire after each experimental section, as well as rate their physical fatigue level on the Borg fatigue scale. In order to bring our experimental setup closer to real-world situations, all signals were monitored using wearable, off-the-shelf devices. In this paper, we describe the adopted experimental protocol, as well as validate the subjective, neural, and physiological data collected. The WAUC database, including the raw data and features, subjective ratings, and scripts to reproduce the experiments reported herein will be made available at: http://musaelab.ca/resources/.
Effects of Cyclocarya paliurus Aqueous and Ethanol Extracts on Glucolipid Metabolism and the Underlying Mechanisms: A Meta-Analysis and Systematic Review

Background and Aims: Cyclocarya paliurus (CP) has been used as an herbal tea to treat diabetes mellitus and obesity for hundreds of years. Previous research suggests that CP specifically restores glucolipid metabolic homeostasis, and the two most studied preparations are aqueous and ethanol extracts. In order to verify the effect of CP on glucolipid metabolism in animal models with metabolic syndrome, a meta-analysis was performed, and the active components and underlying mechanisms were systematically reviewed. Methods: Four databases: PubMed, Web of Science, Embase, and Cochrane Library were searched to identify potential literature. Data of blood glucose (BG) level, area under curve (AUC) of oral glucose tolerance test (OGTT), total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL), and low-density lipoprotein (LDL) levels were extracted as indicators of the assessment of CP's effects. Follow-up analyses including subgroup analysis, meta-regressions, and publication bias were also conducted. Results: A total of 96 papers were identified from the databases and 11 papers including 31 data reports were involved in the meta-analysis. CP had a positive effect in down-regulating BG, AUC of OGTT, TC, TG, and LDL, and up-regulating HDL (P < 0.001, 95% confidence interval of standard mean difference did not incorporate the null value 0). Conclusion: CP showed definite activity of regulating glucolipid metabolism in animal models, and it exerted its function through multiple mechanisms including but not limited to: (1) improving insulin resistance; (2) protecting pancreatic β cells; (3) decreasing inflammatory infiltration; and (4) anti-oxidative stress.
Exploring Morality in Argumentation

Sentiment and stance are two important concepts for the analysis of arguments. We propose to add another perspective to the analysis, namely moral sentiment. We argue that moral values are crucial for ideological debates and can thus add useful information for argument mining. In the paper, we present different models for automatically predicting moral sentiment in debates and evaluate them on a manually annotated testset. We then apply our models to investigate how moral values in arguments relate to argument quality, stance and audience reactions.
Event Coreference Resolution with their Paraphrases and Argument-aware Embeddings

Event coreference resolution aims to classify all event mentions that refer to the same real-world event into the same group, which is necessary to information aggregation and many downstream applications. To resolve event coreference, existing methods usually calculate the similarities between event mentions and between specific kinds of event arguments. However, they fail to accurately identify paraphrase relations between events and may suffer from error propagation while extracting event components (i.e., event mentions and their arguments). Therefore, we propose a new model based on Event-specific Paraphrases and Argument-aware Semantic Embeddings, thus called EPASE, for event coreference resolution. EPASE recognizes deep paraphrase relations in an event-specific context of sentences and can cover event paraphrases of more situations, bringing about a better generalization. Additionally, the embeddings of argument roles are encoded into event embedding without relying on a fixed number and type of arguments, which results in the better scalability of EPASE. Experiments on both within- and cross-document event coreference demonstrate its consistent and significant superiority compared to existing methods.
A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification

Relation Classification (RC) plays an important role in natural language processing (NLP). Current conventional supervised and distantly supervised RC models always make a closed-world assumption which ignores the emergence of novel relations in open environment. To incrementally recognize the novel relations, current two solutions (i.e, re-training and lifelong learning) are designed but suffer from the lack of large-scale labeled data for novel relations. Meanwhile, prototypical network enjoys better performance on both fields of deep supervised learning and few-shot learning. However, it still suffers from the incompatible feature embedding problem when the novel relations come in. Motivated by them, we propose a two-phase prototypical network with prototype attention alignment and triplet loss to dynamically recognize the novel relations with a few support instances meanwhile without catastrophic forgetting. Extensive experiments are conducted to evaluate the effectiveness of our proposed model.
Sonal.kumari at SemEval-2020 Task 12: Social Media Multilingual Offensive Text Identification and Categorization Using Neural Network Models

In this paper, we present our approaches and results for SemEval-2020 Task 12, Multilingual Offensive Language Identification in Social Media (OffensEval 2020). The OffensEval 2020 had three subtasks: A) Identifying the tweets to be offensive (OFF) or non-offensive (NOT) for Arabic, Danish, English, Greek, and Turkish languages, B) Detecting if the offensive tweet is targeted (TIN) or untargeted (UNT) for the English language, and C) Categorizing the offensive targeted tweets into three classes, namely: individual (IND), Group (GRP), or Other (OTH) for the English language. We participate in all the subtasks A, B, and C. In our solution, first we use the pre-trained BERT model for all subtasks, A, B, and C and then we apply the BiLSTM model with attention mechanism (Attn-BiLSTM) for the same. Our result demonstrates that the pre-trained model is not giving good results for all types of languages and is compute and memory intensive whereas the Attn-BiLSTM model is fast and gives good accuracy with fewer resources. The Attn-BiLSTM model is giving better accuracy for Arabic and Greek where the pre-trained model is not able to capture the complete context of these languages due to lower vocab-size.
Facts2Story: Controlling Text Generation by Key Facts

Recent advancements in self-attention neural network architectures have raised the bar for open-ended text generation. Yet, while current methods are capable of producing a coherent text which is several hundred words long, attaining control over the content that is being generated—as well as evaluating it—are still open questions. We propose a controlled generation task which is based on expanding a sequence of facts, expressed in natural language, into a longer narrative. We introduce human-based evaluation metrics for this task, as well as a method for deriving a large training dataset. We evaluate three methods on this task, based on fine-tuning pre-trained models. We show that while auto-regressive, unidirectional Language Models such as GPT2 produce better fluency, they struggle to adhere to the requested facts. We propose a plan-and-cloze model (using fine-tuned XLNet) which produces competitive fluency while adhering to the requested content.
Data Selection for Bilingual Lexicon Induction from Specialized Comparable Corpora

Narrow specialized comparable corpora are often small in size. This particularity makes it difficult to build efficient models to acquire translation equivalents, especially for less frequent and rare words. One way to overcome this issue is to enrich the specialized corpora with out-of-domain resources. Although some recent studies have shown improvements using data augmentation, the enrichment method was roughly conducted by adding out-of-domain data with no particular attention given to how to enrich words and how to do it optimally. In this paper, we contrast several data selection techniques to improve bilingual lexicon induction from specialized comparable corpora. We first apply two well-established data selection techniques often used in machine translation that is: Tf-Idf and cross entropy. Then, we propose to exploit BERT for data selection. Overall, all the proposed techniques improve the quality of the extracted bilingual lexicons by a large margin. The best performing model is the cross entropy, obtaining a gain of about 4 points in MAP while decreasing computation time by a factor of 10.
Bubalus bubalis: A Short Story

The domestic buffalo (Bubalus bubalis), also known as water buffalo or Asian buffalo to prevent confusion with the American bison (Bison bison), wrongly named buffalo in North America, comprises two subspecies: the river buffalo (B. bubalis bubalis) and the swamp buffalo (B. bubalis kerebau). The swamp buffalo has a consistent phenotype and is considered as one type, even if many breeds are recognized within it; conversely, the river buffalo subspecies has many breeds. We found limited information available regarding the worldwide distribution of buffaloes. The best estimate is that 208,098,759 buffalo head are distributed in 77 countries in five continents. In this review, we presented the basic aspects of the water buffalo and unraveled the buffalo path followed from the origin of the species to its current global distribution. We reviewed several data sources to provide a better estimate of the world buffalo count and distribution.
Combining Word Embeddings with Bilingual Orthography Embeddings for Bilingual Dictionary Induction

Bilingual dictionary induction (BDI) is the task of accurately translating words to the target language. It is of great importance in many low-resource scenarios where cross-lingual training data is not available. To perform BDI, bilingual word embeddings (BWEs) are often used due to their low bilingual training signal requirements. They achieve high performance, but problematic cases still remain, such as the translation of rare words or named entities, which often need to be transliterated. In this paper, we enrich BWE-based BDI with transliteration information by using Bilingual Orthography Embeddings (BOEs). BOEs represent source and target language transliteration word pairs with similar vectors. A key problem in our BDI setup is to decide which information source – BWEs (or semantics) vs. BOEs (or orthography) – is more reliable for a particular word pair. We propose a novel classification-based BDI system that uses BWEs, BOEs and a number of other features to make this decision. We test our system on English-Russian BDI and show improved performance. In addition, we show the effectiveness of our BOEs by successfully using them for transliteration mining based on cosine similarity.
Specializing Word Vectors by Spectral Decomposition on Heterogeneously Twisted Graphs

Traditional word vectors, such as word2vec and glove, have a well-known inclination to conflate the semantic similarity with other semantic relations. A retrofitting procedure may be needed to solve this issue. In this work, we propose a new retrofitting method called Heterogeneously Retrofitted Spectral Word Embedding. It heterogeneously twists the similarity matrix of word pairs with lexical constraints. A new set of word vectors is generated by a spectral decomposition of the similarity matrix, which has a linear algebraic analytic form. Our method has a competitive performance compared with the state-of-the-art retrofitting method such as AR (CITATION). In addition, since our embedding has a clear linear algebraic relationship with the similarity matrix, we carefully study the contribution of each component in our model. Last but not least, our method is very efficient to execute.
Ca2+ Dependence of Volume-Regulated VRAC/LRRC8 and TMEM16A Cl– Channels

All vertebrate cells activate Cl– currents (ICl,swell) when swollen by hypotonic bath solution. The volume-regulated anion channel VRAC has now been identified as LRRC8/SWELL1. However, apart from VRAC, the Ca2+-activated Cl– channel (CaCC) TMEM16A and the phospholipid scramblase and ion channel TMEM16F were suggested to contribute to cell swelling-activated whole-cell currents. Cell swelling was shown to induce Ca2+ release from the endoplasmic reticulum and to cause subsequent Ca2+ influx. It is suggested that TMEM16A/F support intracellular Ca2+ signaling and thus Ca2+-dependent activation of VRAC. In the present study, we tried to clarify the contribution of TMEM16A to ICl,swell. In HEK293 cells coexpressing LRRC8A and LRRC8C, we found that activation of ICl,swell by hypotonic bath solution (Hypo; 200 mosm/l) was Ca2+ dependent. TMEM16A augmented the activation of LRRC8A/C by enhancing swelling-induced local intracellular Ca2+ concentrations. In HT29 cells, knockdown of endogenous TMEM16A attenuated ICl,swell and changed time-independent swelling-activated currents to VRAC-typical time-dependent currents. Activation of ICl,swell by Hypo was attenuated by blocking receptors for inositol trisphosphate and ryanodine (IP3R; RyR), as well as by inhibiting Ca2+ influx. The data suggest that TMEM16A contributes directly to ICl,swell as it is activated through swelling-induced Ca2+ increase. As activation of VRAC is shown to be Ca2+-dependent, TMEM16A augments VRAC currents by facilitating Hypo-induced Ca2+ increase in submembraneous signaling compartments by means of ER tethering.
RIVA: A Pre-trained Tweet Multimodal Model Based on Text-image Relation for Multimodal NER

Multimodal named entity recognition (MNER) for tweets has received increasing attention recently. Most of the multimodal methods used attention mechanisms to capture the text-related visual information. However, unrelated or weakly related text-image pairs account for a large proportion in tweets. Visual clues unrelated to the text would incur uncertain or even negative effects for multimodal model learning. In this paper, we propose a novel pre-trained multimodal model based on Relationship Inference and Visual Attention (RIVA) for tweets. The RIVA model controls the attention-based visual clues with a gate regarding the role of image to the semantics of text. We use a teacher-student semi-supervised paradigm to leverage a large unlabeled multimodal tweet corpus with a labeled data set for text-image relation classification. In the multimodal NER task, the experimental results show the significance of text-related visual features for the visual-linguistic model and our approach achieves SOTA performance on the MNER datasets.
RIJP at SemEval-2020 Task 1: Gaussian-based Embeddings for Semantic Change Detection

This paper describes the model proposed and submitted by our RIJP team to SemEval 2020 Task1: Unsupervised Lexical Semantic Change Detection. In the model, words are represented by Gaussian distributions. For Subtask 1, the model achieved average scores of 0.51 and 0.70 in the evaluation and post-evaluation processes, respectively. The higher score in the post-evaluation process than that in the evaluation process was achieved owing to appropriate parameter tuning. The results indicate that the proposed Gaussian-based embedding model is able to express semantic shifts while having a low computational
Fact vs. Opinion: the Role of Argumentation Features in News Classification

A 2018 study led by the Media Insight Project showed that most journalists think that a clearmarking of what is news reporting and what is commentary or opinion (e.g., editorial, op-ed)is essential for gaining public trust. We present an approach to classify news articles into newsstories (i.e., reporting of factual information) and opinion pieces using models that aim to sup-plement the article content representation with argumentation features. Our hypothesis is thatthe nature of argumentative discourse is important in distinguishing between news stories andopinion articles. We show that argumentation features outperform linguistic features used previ-ously and improve on fine-tuned transformer-based models when tested on data from publishersunseen in training. Automatically flagging opinion pieces vs. news stories can aid applicationssuch as fact-checking or event extraction.
What Can We Learn from Noun Substitutions in Revision Histories?

In community-edited resources such as wikiHow, sentences are subject to revisions on a daily basis. Recent work has shown that resulting improvements over time can be modelled computationally, assuming that each revision contributes to the improvement. We take a closer look at a subset of such revisions, for which we attempt to improve a computational model and validate in how far the assumption that ‘revised means better’ actually holds. The subset of revisions considered here are noun substitutions, which often involve interesting semantic relations, including synonymy, antonymy and hypernymy. Despite the high semantic relatedness, we find that a supervised classifier can distinguish the revised version of a sentence from an original version with an accuracy close to 70%, when taking context into account. In a human annotation study, we observe that annotators identify the revised sentence as the ‘better version’ with similar performance. Our analysis reveals a fair agreement among annotators when a revision improves fluency. In contrast, noun substitutions that involve other lexical-semantic relationships are often perceived as being equally good or tend to cause disagreements. While these findings are also reflected in classification scores, a comparison of results shows that our model fails in cases where humans can resort to factual knowledge or intuitions about the required level of specificity.
Measuring Correlation-to-Causation Exaggeration in Press Releases

Press releases have an increasingly strong influence on media coverage of health research; however, they have been found to contain seriously exaggerated claims that can misinform the public and undermine public trust in science. In this study we propose an NLP approach to identify exaggerated causal claims made in health press releases that report on observational studies, which are designed to establish correlational findings, but are often exaggerated as causal. We developed a new corpus and trained models that can identify causal claims in the main statements in a press release. By comparing the claims made in a press release with the corresponding claims in the original research paper, we found that 22% of press releases made exaggerated causal claims from correlational findings in observational studies. Furthermore, universities exaggerated more often than journal publishers by a ratio of 1.5 to 1. Encouragingly, the exaggeration rate has slightly decreased over the past 10 years, despite the increase of the total number of press releases. More research is needed to understand the cause of the decreasing pattern.