Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing Social License to Operate from the Public Discourse on Social Media

Organisations are monitoring their Social License to Operate (SLO) with increasing regularity. SLO, the level of support organisations gain from the public, is typically assessed through surveys or focus groups, which require expensive manual efforts and yield quickly-outdated results. In this paper, we present SIRTA (Social Insight via Real-Time Text Analytics), a novel real-time text analytics system for assessing and monitoring organisations’ SLO levels by analysing the public discourse from social posts. To assess SLO levels, our insight is to extract and transform peoples’ stances towards an organisation into SLO levels. SIRTA achieves this by performing a chain of three text classification tasks, where it identifies task-relevant social posts, discovers key SLO risks discussed in the posts, and infers stances specific to the SLO risks. We leverage recent language understanding techniques (e.g., BERT) for building our classifiers. To monitor SLO levels over time, SIRTA employs quality control mechanisms to reliably identify SLO trends and variations of multiple organisations in a market. These are derived from the smoothed time series of their SLO levels based on exponentially-weighted moving average (EWMA) calculation. Our experimental results show that SIRTA is highly effective in distilling stances from social posts for SLO level assessment, and that the continuous monitoring of SLO levels afforded by SIRTA enables the early detection of critical SLO changes.
DaN+: Danish Nested Named Entities and Lexical Normalization

This paper introduces DAN+, a new multi-domain corpus and annotation guidelines for Dan-ish nested named entities (NEs) and lexical normalization to support research on cross-lingualcross-domain learning for a less-resourced language. We empirically assess three strategies tomodel the two-layer Named Entity Recognition (NER) task. We compare transfer capabilitiesfrom German versus in-language annotation from scratch. We examine language-specific versusmultilingual BERT, and study the effect of lexical normalization on NER. Our results show that 1) the most robust strategy is multi-task learning which is rivaled by multi-label decoding, 2) BERT-based NER models are sensitive to domain shifts, and 3) in-language BERT and lexicalnormalization are the most beneficial on the least canonical data. Our results also show that anout-of-domain setup remains challenging, while performance on news plateaus quickly. Thishighlights the importance of cross-domain evaluation of cross-lingual transfer.
Inferring Neuroticism of Twitter Users by Utilizing their Following Interests

Twitter is a medium where, when used adequately, users’ interests can be derived from what he follows. This characteristic can make it attractive for a source of personality derivation. We set out to test the hypothesis that, analogous to the Lexical hypothesis, which posits that word use should reveal personality, following behavior on social media should reveal personality aspects. We used a two-step approach, wherein the first stage, we selected accounts for whom it was possible to infer personality profiles to some extent using available literature on personality and interests. On these accounts, we trained a regression model and segmented the derived features using hierarchical cluster analysis. In the second stage, we obtained a small sample of users’ personalities via a questionnaire and tested whether the model from stage 1 correlated with the users from step 2. The the explained variance for the neurotic and neutral neuroticism groups indicated significant results (R2 = .131, p = .0205; R2 = .22, p = .0044). Confirming the hypothesis that following behavior should be correlated with one’s interests and that interests are correlated with the neuroticism personality dimension.
Discussion Tracker: Supporting Teacher Learning about Students’ Collaborative Argumentation in High School Classrooms

Teaching collaborative argumentation is an advanced skill that many K-12 teachers struggle to develop. To address this, we have developed Discussion Tracker, a classroom discussion analytics system based on novel algorithms for classifying argument moves, specificity, and collaboration. Results from a classroom deployment indicate that teachers found the analytics useful, and that the underlying classifiers perform with moderate to substantial agreement with humans.
Go Simple and Pre-Train on Domain-Specific Corpora: On the Role of Training Data for Text Classification

Pre-trained language models provide the foundations for state-of-the-art performance across a wide range of natural language processing tasks, including text classification. However, most classification datasets assume a large amount labeled data, which is commonly not the case in practical settings. In particular, in this paper we compare the performance of a light-weight linear classifier based on word embeddings, i.e., fastText (Joulin et al., 2017), versus a pre-trained language model, i.e., BERT (Devlin et al., 2019), across a wide range of datasets and classification tasks. In general, results show the importance of domain-specific unlabeled data, both in the form of word embeddings or language models. As for the comparison, BERT outperforms all baselines in standard datasets with large training sets. However, in settings with small training datasets a simple method like fastText coupled with domain-specific word embeddings performs equally well or better than BERT, even when pre-trained on domain-specific data.
BMEAUT at SemEval-2020 Task 2: Lexical Entailment with Semantic Graphs

In this paper we present a novel rule-based, language independent method for determining lexical entailment relations using semantic representations built from Wiktionary definitions. Combined with a simple WordNet-based method our system achieves top scores on the English and Italian datasets of the Semeval-2020 task “Predicting Multilingual and Cross-lingual (graded) Lexical Entailment” (Glavaš et al., 2020). A detailed error analysis of our output uncovers future di- rections for improving both the semantic parsing method and the inference process on semantic graphs.
Adsorption of Azo Dyes by a Novel Bio-Nanocomposite Based on Whey Protein Nanofibrils and Nano-clay: Equilibrium Isotherm and Kinetic Modeling

Excessive discharge of hazardous azo dyes into aquatic ecosystem is a global environmental concern. Here, we develop a green approach to remediate dye pollutions in water by fabricating an easy separable bio-nanocomposite, based on whey protein concentrate, its nanofibrils and montmorillonite nano-clay. Nanofibrils lead to a uniform dispersion of montmorillonite in the whey protein matrix and also reinforce the nanocomposite. The adsorption efficacy was monitored in a batch system, using cationic dyes (Chrysoidine-G, Bismarck brown-R), reactive dyes (reactive black-5, reactive orange-16), acid dyes (acid red-88, acid red-114), and direct dyes (direct violet-51, Congo red). This nanocomposite adsorbed different dye classes, cationic dyes quicker (>82%, after 4 h) and reactive dyes slower. Then, the effect of dye concentration, contact time and adsorbent dose on Chrysoidine-G adsorption was explored. The adsorbent showed a high removal (>93%) for a wide concentration range of Chrysoidine-G. Equilibrium adsorption parameters were reasonably fitted with a linear (Nernst) isotherm model, while kinetic data were fitted with pseudo-second-order and intra-particle diffusion models. To characterize the nanocomposite, we used SEM, FT-IR, XRD and BET techniques. We conclude that this nanocomposite is a green adsorbent with potential use for wastewater treatment and related purposes. Highlights We produced a novel bio-nanocomposite using whey nanofibrils and MMT Nanofibrils help disperse MMT particles uniformly in the WP matrix The adsorbent’s performance was compared to the adsorbents in absence of MMT and nanofibrils This composite adsorbs cationic, anionic, direct and reactive azo dyes with different kinetics Adsorption isotherms and kinetics are studied in detail
Visual-Textual Alignment for Graph Inference in Visual Dialog

As a conversational intelligence task, visual dialog entails answering a series of questions grounded in an image, using the dialog history as context. To generate correct answers, the comprehension of the semantic dependencies among implicit visual and textual contents is critical. Prior works usually ignored the underlying relation and failed to infer it reasonably. In this paper, we propose a Visual-Textual Alignment for Graph Inference (VTAGI) network. Compared with other approaches, it makes up the lack of structural inference in visual dialog. The whole system consists of two modules, Visual and Textual Alignment (VTA) and Visual Graph Attended by Text (VGAT). Specially, the VTA module aims at representing an image with a set of integrated visual regions and corresponding textual concepts, reflecting certain semantics. The VGAT module views the visual features with semantic information as observed nodes and each node learns the relationship with others in visual graph. We also qualitatively and quantitatively evaluate the model on VisDial v1.0 dataset, showing our VTAGI outperforms previous state-of-the-art models.
FPAI at SemEval-2020 Task 10: A Query Enhanced Model with RoBERTa for Emphasis Selection

This paper describes the model we apply in the SemEval-2020 Task 10. We formalize the task of emphasis selection as a simplified query-based machine reading comprehension (MRC) task, i.e. answering a fixed question of “Find candidates for emphasis”. We propose our subword puzzle encoding mechanism and subword fusion layer to align and fuse subwords. By introducing the semantic prior knowledge of the informative query and some other techniques, we attain the 7th place during the evaluation phase and the first place during train phase.
Fair Evaluation in Concept Normalization: a Large-scale Comparative Analysis for BERT-based Models

Linking of biomedical entity mentions to various terminologies of chemicals, diseases, genes, adverse drug reactions is a challenging task, often requiring non-syntactic interpretation. A large number of biomedical corpora and state-of-the-art models have been introduced in the past five years. However, there are no general guidelines regarding the evaluation of models on these corpora in single- and cross-terminology settings. In this work, we perform a comparative evaluation of various benchmarks and study the efficiency of state-of-the-art neural architectures based on Bidirectional Encoder Representations from Transformers (BERT) for linking of three entity types across three domains: research abstracts, drug labels, and user-generated texts on drug therapy in English. We have made the source code and results available at https://github.com/insilicomedicine/Fair-Evaluation-BERT.
Integrating knowledge graph embeddings to improve mention representation for bridging anaphora resolution

Lexical semantics and world knowledge are crucial for interpreting bridging anaphora. Yet, existing computational methods for acquiring and injecting this type of information into bridging resolution systems suffer important limitations. Based on explicit querying of external knowledge bases, earlier approaches are computationally expensive (hence, hardly scalable) and they map the data to be processed into high-dimensional spaces (careful handling of the curse of dimensionality and overfitting has to be in order). In this work, we take a different and principled approach which naturally addresses these issues. Specifically, we convert the external knowledge source (in this case, WordNet) into a graph, and learn embeddings of the graph nodes of low dimension to capture the crucial features of the graph topology and, at the same time, rich semantic information. Once properly identified from the mention text spans, these low dimensional graph node embeddings are combined with distributional text-based embeddings to provide enhanced mention representations. We illustrate the effectiveness of our approach by evaluating it on commonly used datasets, namely ISNotes and BASHI. Our enhanced mention representations yield significant accuracy improvements on both datasets when compared to different standalone text-based mention representations.
Team Solomon at SemEval-2020 Task 4: Be Reasonable: Exploiting Large-scale Language Models for Commonsense Reasoning

In this paper, we present our submission for SemEval 2020 Task 4 - Commonsense Validation and Explanation (ComVE). The objective of this task was to develop a system that can differentiate statements that make sense from the ones that don’t. ComVE comprises of three subtasks to challenge and test a system’s capability in understanding commonsense knowledge from various dimensions. Commonsense reasoning is a challenging task in the domain of natural language understanding and systems augmented with it can improve performance in various other tasks such as reading comprehension, and inferencing. We have developed a system that leverages commonsense knowledge from pretrained language models trained on huge corpus such as RoBERTa, GPT2, etc. Our proposed system validates the reasonability of a given statement against the backdrop of commonsense knowledge acquired by these models and generates a logical reason to support its decision. Our system ranked 2nd in subtask C with a BLEU score of 19.3, which by far is the most challenging subtask as it required systems to generate the rationale behind the choice of an unreasonable statement. In subtask A and B, we achieved 96% and 94% accuracy respectively standing at 4th position in both the subtasks.
Hitachi at SemEval-2020 Task 3: Exploring the Representation Spaces of Transformers for Human Sense Word Similarity

In this paper, we present our system for SemEval-2020 task 3, Predicting the (Graded) Effect of Context in Word Similarity. Due to the unsupervised nature of the task, we concentrated on inquiring about the similarity measures induced by different layers of different pre-trained Transformer-based language models, which can be good approximations of the human sense of word similarity. Interestingly, our experiments reveal a language-independent characteristic: the middle to upper layers of Transformer-based language models can induce good approximate similarity measures. Finally, our system was ranked 1st on the Slovenian part of Subtask1 and 2nd on the Croatian part of both Subtask1 and Subtask2.
UI at SemEval-2020 Task 4: Commonsense Validation and Explanation by Exploiting Contradiction

This paper describes our submissions into the ComVe challenge, the SemEval 2020 Task 4. This evaluation task consists of three sub-tasks that test commonsense comprehension by identifying sentences that do not make sense and explain why they do not. In subtask A, we use Roberta to find which sentence does not make sense. In subtask B, besides using BERT, we also experiment with replacing the dataset with MNLI when selecting the best explanation from the provided options why the given sentence does not make sense. In subtask C, we utilize the MNLI model from subtask B to evaluate the explanation generated by Roberta and GPT-2 by exploiting the contradiction of the sentence and their explanation. Our system submission records a performance of 88.2%, 80.5%, and BLEU 5.5 for those three subtasks, respectively.
Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction

Document-level Relation Extraction (RE) is particularly challenging due to complex semantic interactions among multiple entities in a document. Among exiting approaches, Graph Convolutional Networks (GCN) is one of the most effective approaches for document-level RE. However, traditional GCN simply takes word nodes and adjacency matrix to represent graphs, which is difficult to establish direct connections between distant entity pairs. In this paper, we propose Global Context-enhanced Graph Convolutional Networks (GCGCN), a novel model which is composed of entities as nodes and context of entity pairs as edges between nodes to capture rich global context information of entities in a document. Two hierarchical blocks, Context-aware Attention Guided Graph Convolution (CAGGC) for partially connected graphs and Multi-head Attention Guided Graph Convolution (MAGGC) for fully connected graphs, could take progressively more global context into account. Meantime, we leverage a large-scale distantly supervised dataset to pre-train a GCGCN model with curriculum learning, which is then fine-tuned on the human-annotated dataset for further improving document-level RE performance. The experimental results on DocRED show that our model could effectively capture rich global context information in the document, leading to a state-of-the-art result. Our code is available at https://github.com/Huiweizhou/GCGCN.
YNU_oxz at SemEval-2020 Task 12: Bidirectional GRU with Capsule for Identifying Multilingual Offensive Language

This article describes the system submitted to SemEval-2020 Task 12 OffensEval 2: Multilingual Offensive Language Recognition in Social Media. The task is to classify offensive language in social media. The shared task contains five languages (English, Greek, Arabic, Danish, and Turkish) and three subtasks. We only participated in subtask A of English to identify offensive language. To solve this task, we proposed a system based on a Bidirectional Gated Recurrent Unit (Bi-GRU) with a Capsule model. Finally, we used the K-fold approach for ensemble. Our model achieved a Macro-average F1 score of 0.90969 (ranked 27/85) in subtask A.
Generationary or: “How We Went beyond Sense Inventories and Learnedto Gloss”

In this talk I present Generationary, an approach that goes beyond the mainstream assumption that word senses can be represented as discrete items of a predefined inventory, and put forward a unified model which produces contextualized definitions for arbitrary lexical items, from words to phrases and even sentences. Generationary employs a novel span-based encoding scheme to fine-tune an English pre-trained Encoder-Decoder system and generate new definitions. Our model outperforms previous approaches in the generative task of Definition Modeling in many settings, but it also matches or surpasses the state of the art in discriminative tasks such as Word Sense Disambiguation and Word-in-Context. Finally, we show that Generationary benefits from training on definitions from multiple inventories, with strong gains across benchmarks, including a novel dataset of definitions for free adjective-noun phrases.
Effect of Increasing Glucose Concentration on Estimation of Electrolytes by ion selective electrode

Background The measurement of serum electrolytes like sodium (Na+), potassium (K+) is routinely performed in clinical biochemistry laboratories using ion-selective electrodes (ISE). Aim To minimize error and test the effect of increasing glucose concentration on estimation of sodium and potassium. Materials and Methods Randomly selected sera samples submitted for routine biochemical estimations were pooled for the study. A stock solution of glucose with concentration of 20 g/ dL was prepared by dissolving anhydrous glucose in normal saline (0.9 % NaCl). Next the pooled sera was divided into several aliquots with 1.0 ml sera each and increasing amounts of the stock solution were added to the different aliquots so as to generate glucose concentrations ranging from around 100 mg/dL to about 3000 mg/dL. Results the glucose concentration of different serum aliquots obtained on cobas c311(roche system) were found to rang from 110 to 2577 mg/dl. The mean of sodium by direct ion selective 142±0.52 mmol/l, values showed in significant difference (p=0.320). The mean of sodium by indirect ion selective 143 ± 0.72 mmol/l, values showed significant difference (p< 0.05). And the mean of potassium by direct ion selective 3.54 ±0.29 mmol/l, values showed significant difference (p< 0.05). The mean of potassium by indirect ion selective 3.53±0.26 mmol/l, values showed significant difference (p< 0.05). Conclusions Increasing blood gluose interferance in electrlytes mainly potasssium by both direct and indirect ion selective electrode, whenincreasing glucose potassium decreae significantly No different significant between direct and indirect ion selective electrods in potassium when used cobas c311 and easy lyte.Significant interferance in sodium by indirect ion selective electrodewhile no significant interferance in sodium direct ion selective electrode.Glucose concentration interfer the electrolyte specially potassium in concentration more than 1700 mg/dl by direct and indirect methods.
Discovery Team at SemEval-2020 Task 1: Context-sensitive Embeddings Not Always Better than Static for Semantic Change Detection

This paper describes the approaches used by the Discovery Team to solve SemEval-2020 Task 1 - Unsupervised Lexical Semantic Change Detection. The proposed method is based on clustering of BERT contextual embeddings, followed by a comparison of cluster distributions across time. The best results were obtained by an ensemble of this method and static Word2Vec embeddings. According to the official results, our approach proved the best for Latin in Subtask 2.
Combined inhibition of KDM6A/B and HDACs exacerbates integrated stress response and mediates therapeutic effects in IDH1-mutant glioma

IDH1/2-mutant gliomas are primary brain tumors for which curative treatments are lacking. Using a chemical screen targeting chromatin modifiers, we identified the histone H3 K27me3 demethylase inhibitor GSK-J4 and class I histone deacetylase inhibitors such as Belinostat as potent, genotype-selective agents against IDH1-mutant glioma. RNA-sequencing on paired wild-type and IDH1R132H cells revealed induction of stress-related pathways in IDH1R132H cells, which was dependent on the onco-metabolite 2-hydroxyglutarate (2-HG). GSK-J4 and Belinostat combination activated an ATF4-mediated integrated stress response, cell cycle arrest, and DDIT3/CHOP-dependent apoptosis in IDH1-mutant cells. We show that genetic ablation of GSK-J4 target histone demethylases, KDM6A and KDM6B phenocopied the pharmacological effects of the compound. Combination treatment of GSK-J4 and Belinostat extended animal survival in an IDH1-mutant orthotopic model in vivo. These results provide a possible therapeutic approach that exploits epigenetic vulnerabilities of IDH-mutant gliomas.IDH1/2 genes are frequently mutated in low grade glioma and secondary glioblastoma. These tumors exhibit a distinct epigenome with increased DNA and histone methylation; therefore, identifying and exploiting their epigenetic vulnerabilities may lead to effective therapies. We discover targeting of KDM6A/6B together with HDACs provides a promising genotype-specific therapeutic approach.