Miracle-dz/UniFAR-FTUs huggingface dataset:
Are Neural Networks Extracting Linguistic Properties or Memorizing Training Data? An Observation with a Multilingual Probe for Predicting Tense

We evaluate the ability of Bert embeddings to represent tense information, taking French and Chinese as a case study. In French, the tense information is expressed by verb morphology and can be captured by simple surface information. On the contrary, tense interpretation in Chinese is driven by abstract, lexical, syntactic and even pragmatic information. We show that while French tenses can easily be predicted from sentence representations, results drop sharply for Chinese, which suggests that Bert is more likely to memorize shallow patterns from the training data rather than uncover abstract properties.
DISK-CSV: Distilling Interpretable Semantic Knowledge with a Class Semantic Vector

Neural networks (NN) applied to natural language processing (NLP) are becoming deeper and more complex, making them increasingly difficult to understand and interpret. Even in applications of limited scope on fixed data, the creation of these complex “black-boxes” creates substantial challenges for debugging, understanding, and generalization. But rapid development in this field has now lead to building more straightforward and interpretable models. We propose a new technique (DISK-CSV) to distill knowledge concurrently from any neural network architecture for text classification, captured as a lightweight interpretable/explainable classifier. Across multiple datasets, our approach achieves better performance than the target black-box. In addition, our approach provides better explanations than existing techniques.
Probing into the Root: A Dataset for Reason Extraction of Structural Events from Financial Documents

This paper proposes a new task regarding event reason extraction from document-level texts. Unlike the previous causality detection task, we do not assign target events in the text, but only provide structural event descriptions, and such settings accord more with practice scenarios. Moreover, we annotate a large dataset FinReason for evaluation, which provides Reasons annotation for Financial events in company announcements. This task is challenging because the cases of multiple-events, multiple-reasons, and implicit-reasons are included. In total, FinReason contains 8,794 documents, 12,861 financial events and 11,006 reason spans. We also provide the performance of existing canonical methods in event extraction and machine reading comprehension on this task. The results show a 7 percentage point F1 score gap between the best model and human performance, and existing methods are far from resolving this problem.
Fine-Grained Event Trigger Detection

Most of the previous work on Event Detection (ED) has only considered the datasets with a small number of event types (i.e., up to 38 types). In this work, we present the first study on fine-grained ED (FED) where the evaluation dataset involves much more fine-grained event types (i.e., 449 types). We propose a novel method to transform the Semcor dataset for Word Sense Disambiguation into a large and high-quality dataset for FED. Extensive evaluation of the current ED methods is conducted to demonstrate the challenges of the generated datasets for FED, calling for more research effort in this area.
Generating Weather Comments from Meteorological Simulations

The task of generating weather-forecast comments from meteorological simulations has the following requirements: (i) the changes in numerical values for various physical quantities need to be considered, (ii) the weather comments should be dependent on delivery time and area information, and (iii) the comments should provide useful information for users. To meet these requirements, we propose a data-to-text model that incorporates three types of encoders for numerical forecast maps, observation data, and meta-data. We also introduce weather labels representing weather information, such as sunny and rain, for our model to explicitly describe useful information. We conducted automatic and human evaluations. The results indicate that our model performed best against baselines in terms of informativeness. We make our code and data publicly available.
Neural-Driven Search-Based Paraphrase Generation

We study a search-based paraphrase generation scheme where candidate paraphrases are generated by iterated transformations from the original sentence and evaluated in terms of syntax quality, semantic distance, and lexical distance. The semantic distance is derived from BERT, and the lexical quality is based on GPT2 perplexity. To solve this multi-objective search problem, we propose two algorithms: Monte-Carlo Tree Search For Paraphrase Generation (MCPG) and Pareto Tree Search (PTS). We provide an extensive set of experiments on 5 datasets with a rigorous reproduction and validation for several state-of-the-art paraphrase generation algorithms. These experiments show that, although being non explicitly supervised, our algorithms perform well against these baselines.
Project-then-Transfer: Effective Two-stage Cross-lingual Transfer for Semantic Dependency Parsing

This paper describes the first report on cross-lingual transfer for semantic dependency parsing. We present the insight that there are twodifferent kinds of cross-linguality, namely sur-face level and mantic level, and try to cap-ture both kinds of cross-linguality by combin-ing annotation projection and model transferof pre-trained language models. Our exper-iments showed that the performance of our graph-based semantic dependency parser almost achieved the approximated upper bound.
Attention-based Relational Graph Convolutional Network for Target-Oriented Opinion Words Extraction

Target-oriented opinion words extraction (TOWE) is a subtask of aspect-based sentiment analysis (ABSA). It aims to extract the corresponding opinion words for a given opinion target in a review sentence. Intuitively, the relation between an opinion target and an opinion word mostly relies on syntactics. In this study, we design a directed syntactic dependency graph based on a dependency tree to establish a path from the target to candidate opinions. Subsequently, we propose a novel attention-based relational graph convolutional neural network (ARGCN) to exploit syntactic information over dependency graphs. Moreover, to explicitly extract the corresponding opinion words toward the given opinion target, we effectively encode target information in our model with the target-aware representation. Empirical results demonstrate that our model significantly outperforms all of the existing models on four benchmark datasets. Extensive analysis also demonstrates the effectiveness of each component of our models. Our code is available at https://github.com/wcwowwwww/towe-eacl.
Probing for idiomaticity in vector space models

Contextualised word representation models have been successfully used for capturing different word usages and they may be an attractive alternative for representing idiomaticity in language. In this paper, we propose probing measures to assess if some of the expected linguistic properties of noun compounds, especially those related to idiomatic meanings, and their dependence on context and sensitivity to lexical choice, are readily available in some standard and widely used representations. For that, we constructed the Noun Compound Senses Dataset, which contains noun compounds and their paraphrases, in context neutral and context informative naturalistic sentences, in two languages: English and Portuguese. Results obtained using four types of probing measures with models like ELMo, BERT and some of its variants, indicate that idiomaticity is not yet accurately represented by contextualised models
How Good (really) are Grammatical Error Correction Systems?

Standard evaluations of Grammatical Error Correction (GEC) systems make use of a fixed reference text generated relative to the original text; they show, even when using multiple references, that we have a long way to go. This analysis paper studies the performance of GEC systems relative to closest-gold – a gold reference text created relative to the output of a system. Surprisingly, we show that the real performance is 20-40 points better than standard evaluations show. Moreover, the performance remains high even when considering any of the top-10 hypotheses produced by a system. Importantly, the type of mistakes corrected by lower-ranked hypotheses differs in interesting ways from the top one, providing an opportunity to focus on a range of errors – local spelling and grammar edits vs. more complex lexical improvements. Our study shows these results in English and Russian, and thus provides a preliminary proposal for a more realistic evaluation of GEC systems.
Multiple Tasks Integration: Tagging, Syntactic and Semantic Parsing as a Single Task

Departing from both sequential pipelines and monotask systems, we propose Multiple Tasks Integration (MTI), a multitask paradigm orthogonal to weight sharing. The essence of MTI is to process the input iteratively but concurrently at multiple levels of analysis, where each decision is based on all of the structures that are already inferred and free from usual ordering constraints. We illustrate MTI with a system that performs part-of-speech tagging, syntactic dependency parsing and semantic dependency parsing. We observe that both the use of reinforcement learning and the release from sequential constraints are beneficial to the quality of the syntactic and semantic parses. We also observe that our model adopts an easy-first strategy that consists, on average, of predicting shorter dependencies before longer ones, but that syntax is not always tackled before semantics.
FAST: Financial News and Tweet Based Time Aware Network for Stock Trading

Designing profitable trading strategies is complex as stock movements are highly stochastic; the market is influenced by large volumes of noisy data across diverse information sources like news and social media. Prior work mostly treats stock movement prediction as a regression or classification task and is not directly optimized towards profit-making. Further, they do not model the fine-grain temporal irregularities in the release of vast volumes of text that the market responds to quickly. Building on these limitations, we propose a novel hierarchical, learning to rank approach that uses textual data to make time-aware predictions for ranking stocks based on expected profit. Our approach outperforms state-of-the-art methods by over 8% in terms of cumulative profit and risk-adjusted returns in trading simulations on two benchmarks: English tweets and Chinese financial news spanning two major stock indexes and four global markets. Through ablative and qualitative analyses, we build the case for our method as a tool for daily stock trading.
Retrieval, Re-ranking and Multi-task Learning for Knowledge-Base Question Answering

Question answering over knowledge bases (KBQA) usually involves three sub-tasks, namely topic entity detection, entity linking and relation detection. Due to the large number of entities and relations inside knowledge bases (KB), previous work usually utilized sophisticated rules to narrow down the search space and managed only a subset of KBs in memory. In this work, we leverage a retrieve-and-rerank framework to access KBs via traditional information retrieval (IR) method, and re-rank retrieved candidates with more powerful neural networks such as the pre-trained BERT model. Considering the fact that directly assigning a different BERT model for each sub-task may incur prohibitive costs, we propose to share a BERT encoder across all three sub-tasks and define task-specific layers on top of the shared layer. The unified model is then trained under a multi-task learning framework. Experiments show that: (1) Our IR-based retrieval method is able to collect high-quality candidates efficiently, thus enables our method adapt to large-scale KBs easily; (2) the BERT model improves the accuracy across all three sub-tasks; and (3) benefiting from multi-task learning, the unified model obtains further improvements with only 1/3 of the original parameters. Our final model achieves competitive results on the SimpleQuestions dataset and superior performance on the FreebaseQA dataset.
Graph-based Fake News Detection using a Summarization Technique

Nowadays, fake news is spreading in various ways, and this fake information is causing a lot of social damages. Thus the need to detect fake information is increasing to prevent the damages caused by fake news. In this paper, we propose a novel graph-based fake news detection method using a summarization technique that uses only the document internal information. Our proposed method represents the relationship between all sentences using a graph and the reflection rate of contextual information among sentences is computed by using an attention mechanism. In addition, we improve the performance of fake news detection by utilizing summary information as an important subject of the document.The experimental results demonstrate that our method achieves high accuracy, 91.04%, that is 8.85%p better than the previous method.
ChEMU-Ref: A Corpus for Modeling Anaphora Resolution in the Chemical Domain

Chemical patents contain rich coreference and bridging links, which are the target of this research. Specially, we introduce a novel annotation scheme, based on which we create the ChEMU-Ref dataset from reaction description snippets in English-language chemical patents. We propose a neural approach to anaphora resolution, which we show to achieve strong results, especially when jointly trained over coreference and bridging links.
Cross-Topic Rumor Detection using Topic-Mixtures

There has been much interest in rumor detection using deep learning models in recent years. A well-known limitation of deep learning models is that they tend to learn superficial patterns, which restricts their generalization ability. We find that this is also true for cross-topic rumor detection. In this paper, we propose a method inspired by the “mixture of experts” paradigm. We assume that the prediction of the rumor class label given an instance is dependent on the topic distribution of the instance. After deriving a vector representation for each topic, given an instance, we derive a “topic mixture” vector for the instance based on its topic distribution. This topic mixture is combined with the vector representation of the instance itself to make rumor predictions. Our experiments show that our proposed method can outperform two baseline debiasing methods in a cross-topic setting. In a synthetic setting when we removed topic-specific words, our method also works better than the baselines, showing that our method does not rely on superficial features.
PHASE: Learning Emotional Phase-aware Representations for Suicide Ideation Detection on Social Media

Recent psychological studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners. Contextualizing the build-up of such ideation is critical for the identification of users at risk. In this work, we focus on identifying suicidal intent in tweets by augmenting linguistic models with emotional phases modeled from users’ historical context. We propose PHASE, a time-and phase-aware framework that adaptively learns features from a user’s historical emotional spectrum on Twitter for preliminary screening of suicidal risk. Building on clinical studies, PHASE learns phase-like progressions in users’ historical Plutchik-wheel-based emotions to contextualize suicidal intent. While outperforming state-of-the-art methods, we show the utility of temporal and phase-based emotional contextual cues for suicide ideation detection. We further discuss practical and ethical considerations.
Alignment verification to improve NMT translation towards highly inflectional languages with limited resources

The present article discusses how to improve translation quality when using limited training data to translate towards morphologically rich languages. The starting point is a neural MT system, used to train translation models, using solely publicly available parallel data. An initial analysis of the translation output has shown that quality is sub-optimal, due mainly to an insufficient amount of training data. To improve translation quality, a hybridized solution is proposed, using an ensemble of relatively simple NMT systems trained with different metrics, combined with an open source module, designed for a low-resource MT system. Experimental results of the proposed hybridized method with multiple independent test sets achieve improvements over (i) both the best individual NMT and (ii) the standard ensemble system provided in the Marian-NMT system. Improvements over Marian-NMT are in many cases statistically significant. Finally, a qualitative analysis of translation results indicates a greater robustness for the hybridized method.
Why Is MBTI Personality Detection from Texts a Difficult Task?

Automatic detection of the four MBTI personality dimensions from texts has recently attracted noticeable attention from the natural language processing and computational linguistic communities. Despite the large collections of Twitter data for training, the best systems rarely even outperform the majority-class baseline. In this paper, we discuss the theoretical reasons for such low results and present the insights from an annotation study that further shed the light on this issue.
GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition

Instead of using expensive manual annotations, researchers have proposed to train named entity recognition (NER) systems using heuristic labeling rules. However, devising labeling rules is challenging because it often requires a considerable amount of manual effort and domain expertise. To alleviate this problem, we propose GLARA, a graph-based labeling rule augmentation framework, to learn new labeling rules from unlabeled data. We first create a graph with nodes representing candidate rules extracted from unlabeled data. Then, we design a new graph neural network to augment labeling rules by exploring the semantic relations between rules. We finally apply the augmented rules on unlabeled data to generate weak labels and train a NER model using the weakly labeled data. We evaluate our method on three NER datasets and find that we can achieve an average improvement of +20% F1 score over the best baseline when given a small set of seed rules.
Is the Understanding of Explicit Discourse Relations Required in Machine Reading Comprehension?

An in-depth analysis of the level of language understanding required by existing Machine Reading Comprehension (MRC) benchmarks can provide insight into the reading capabilities of machines. In this paper, we propose an ablation-based methodology to assess the extent to which MRC datasets evaluate the understanding of explicit discourse relations. We define seven MRC skills which require the understanding of different discourse relations. We then introduce ablation methods that verify whether these skills are required to succeed on a dataset. By observing the drop in performance of neural MRC models evaluated on the original and the modified dataset, we can measure to what degree the dataset requires these skills, in order to be understood correctly. Experiments on three large-scale datasets with the BERT-base and ALBERT-xxlarge model show that the relative changes for all skills are small (less than 6%). These results imply that most of the answered questions in the examined datasets do not require understanding the discourse structure of the text. To specifically probe for natural language understanding, there is a need to design more challenging benchmarks that can correctly evaluate the intended skills.
Companionship for women using English maternity services during COVID-19: National and organisational perspectives

Objectives: To explore the impact of COVID-19 on companionship for women using maternity services in England, as part of the Achieving Safe and Personalised maternity care In Response to Epidemics (ASPIRE COVID-19 UK) study. Setting: Maternity care provision in England. Participants: Interviews were held with 26 national governmental, professional, and service-user organisation leads including representatives from the Royal College of Midwives, NHS England, Birthrights and AIMS (July-Dec). Other data included public-facing outputs logged from 25 maternity Trusts (Sept/Oct) and data extracted from 78 documents from 8 key governmental, professional and service-user organisations that informed national maternity care guidance and policy (Feb-Dec). Results: Six themes emerged: Postcode lottery of care highlights variations in companionship practices, Confusion and stress around rules relates to a lack of and variable information concerning companionship, Unintended consequences concerns the negative impacts of restricted companionship on service-users and staff, Need for flexibility highlights concerns about applying companionship policies irrespective of need, Acceptable time for support highlights variations in when and if companionship was allowed antenatally and intrapartum; and Loss of human rights for gain in infection control emphasizes how a predominant focus on infection control was at a cost to psychological safety and womens human rights. Conclusions: Policies concerning companionship have been inconsistently applied within English maternity services during the COVID-19 pandemic. In some cases, policies were not justified by the level of risk, and were applied indiscriminately regardless of need. This was associated with psychological harms for some women and staff. There is an urgent need to determine how to balance risks and benefits sensitively and flexibly and to optimise outcomes during the current and future crisis situations.
Chitosan/PVA/Doxycycline Film and Nanofiber Accelerate Diabetic Wound Healing in a Rat Model

In this study, we evaluated the effects of nanofiber and film polymers with doxycycline for treating a wound in a diabetic rat model. 108 male rats were divided into six groups, the control group, the diabetic control, and the groups were diabetic rats receiving different wound dressing. At the 3rd, 7th, and 14th days, macroscopic/histologic imaging and tissue sampling were performed. Tissues were analyzed for IL-1β, TNF-α, IL-10, TIMP-1, and MMP-2 by using ELISA. Dressings of chitosan, polyvinyl alcohol, and doxycycline increased the rate of wound closure, the volume of collagen, dermal, and epidermis. In addition, it increased the number of fibroblasts and basal cell epidermis cells, vascular length, and decreased the number of neutrophil cells. Inflammatory cytokines and MMP-2 were decreased, and anti-inflammatory IL-10 and TIMP-1 were increased. It was ultimately attained that the combination of chitosan/ polyvinyl alcohol /doxycycline could be a useful dressing for the healing of diabetic wounds.