Reviewerly/exHarmony huggingface dataset:
Automatic Gloss Dictionary for Sign Language Learners

A multi-language dictionary is a fundamental tool for language learning, allowing the learner to look up unfamiliar words.Searching an unrecognized word in the dictionary does not usually require deep knowledge of the target language.However, this is not true for sign language, where gestural elements preclude this type of easy lookup.This paper introduces GlossFinder, an online tool supporting 2, 000 signs to assist language learners in determining the meaning of given signs.Unlike alternative systems of complex inputs, our system requires only that learners imitate the sign in front of a standard webcam.A user study conducted among sign language speakers of varying ability compared our system against existing alternatives and the interviews indicated a clear preference for our new system.This implies that GlossFinder can lower the barrier in sign language learning by addressing the common problem of sign finding and make it accessible to the wider community.
QiuNiu: A Chinese Lyrics Generation System with Passage-Level Input

Lyrics generation has been a very popular application of natural language generation.Previous works mainly focused on generating lyrics based on a couple of attributes or keywords, rendering very limited control over the content of the lyrics.In this paper, we demonstrate the QiuNiu, a Chinese lyrics generation system which is conditioned on passage-level text rather than a few attributes or keywords.By using the passage-level text as input, the content of generated lyrics is expected to reflect the nuances of users' needs.The QiuNiu system supports various forms of passage-level input, such as short stories, essays, poetry.The training of it is conducted under the framework of unsupervised machine translation, due to the lack of aligned passage-level text-to-lyrics corpus.We initialize the parameters of Qiu-Niu with a custom pretrained Chinese GPT-2 model and adopt a two-step process to finetune the model for better alignment between passage-level text and lyrics.Additionally, a postprocess module is used to filter and rerank the generated lyrics to select the ones of highest quality.The demo video of the system is available at https://youtu.be/OCQNzahqWgM.
Onto4MAT: A Swarm Shepherding Ontology for Generalized Multiagent Teaming

Research in multi-agent teaming has increased substantially over recent years, with knowledge-based systems to support teaming processes typically focused on delivering functional (communicative) solutions for a team to act meaningfully in response to direction. Enabling humans to effectively interact and team with a swarm of autonomous cognitive agents is an open research challenge in Human-Swarm Teaming research, partially due to the focus on developing the enabling architectures to support these systems. Typically, bi-directional transparency and shared semantic understanding between agents has not prioritised a designed mechanism in Human-Swarm Teaming, potentially limiting how a human and a swarm team can share understanding and information\textemdash data through concepts and contexts\textemdash to achieve a goal. To address this, we provide a formal knowledge representation design that enables the swarm Artificial Intelligence to reason about its environment and system, ultimately achieving a shared goal. We propose the Ontology for Generalised Multi-Agent Teaming, Onto4MAT, to enable more effective teaming between humans and teams through the biologically-inspired approach of shepherding.
Seasonal Clustering Forecasting Technique for Intelligent Hourly Solar Irradiance Systems

The main purpose of the study is to reach a forecasting accuracy level through layering and stacking clusters of weather data to reduce seasonality-related uncertainty. To enhance forecasting accuracy, high-dimensional heterogeneous weather data should be added to training datasets; yet, weather data are deemed to have seasonality-related uncertainty. Since utilizing the long short-term memory (LSTM) model does not achieve the required results, the LSTM forecasting model performance deteriorates as a result of the change from univariate to multivariate analyses. Therefore, a primary investigation was conducted to analyze seasonality-based hourly predictions for global horizontal irradiance. The investigation found that seasonality affects accuracy of predictions due to high levels of Autumn- and Winter-related weather phenomena and climate uncertainty. Accordingly, seasonal clustering forecasting technique (SCFT) based on an LSTM hybrid strategy and stacked layers of weather clusters was proposed. In order to demonstrate the validity of this model, the proposed SCFT is compared to a variety of forecasting approaches in the literature. The comparison shows the SCFT’s superiority, especially during seasons that are predominantly rainy and overcast. Finally, in accordance with previous studies, suggesting the benefit of applying model testing in other parts of the world, SCFT is employed using data from other Köppen climate classifications. Furthermore, the performance of the proposed forecasting model, SCFT, shows significant stability and reliability after measuring it against Diebold–Mariano and Granger–Newbold tests.
A Meta-framework for Spatiotemporal Quantity Extraction from Text

News events are often associated with quantities (e.g., the number of COVID-19 patients or the number of arrests in a protest), and it is often important to extract their type, time, and location from unstructured text in order to analyze these quantity events.This paper thus formulates the NLP problem of spatiotemporal quantity extraction, and proposes the first meta-framework for solving it.This meta-framework contains a formalism that decomposes the problem into several information extraction tasks, a shareable crowdsourcing pipeline, and transformer-based baseline models.We demonstrate the meta-framework in three domains-the COVID-19 pandemic, Black Lives Matter protests, and 2020 California wildfires-to show that the formalism is general and extensible, the crowdsourcing pipeline facilitates fast and high-quality data annotation, and the baseline system can handle spatiotemporal quantity extraction well enough to be practically useful.We release all resources for future research on this topic.
Unmet Creativity Support Needs in Computationally Supported Creative Writing

Large language models (LLMs) enabled by the datasets and computing power of the last decade have recently gained popularity for their capacity to generate plausible natural language text from human-provided prompts.This ability makes them appealing to fiction writers as prospective co-creative agents, addressing the common challenge of writer's block, or getting unstuck.However, creative writers face additional challenges, including maintaining narrative consistency, developing plot structure, architecting reader experience, and refining their expressive intent, which are not well-addressed by current LLM-backed tools.In this paper, we define these needs by grounding them in cognitive and theoretical literature, then survey previous computational narrative research that holds promise for supporting each of them in a co-creative setting.
LPS@LT-EDI-ACL2022:An Ensemble Approach about Hope Speech Detection

The task shared by sponsor about Hope Speech Detection for Equality, Diversity, and Inclusion at LT-EDI-ACL-2022.The goal of this task is to identify whether a given comment contains hope speech or not,and hope is considered significant for the well-being, recuperation and restoration of human life.Our work aims to change the prevalent way of thinking by moving away from a preoccupation with discrimination, loneliness or the worst things in life to building the confidence, support and good qualities based on comments by individuals.In response to the need to detect equality, diversity and inclusion of hope speech in a multilingual environment, we built an integration model and achieved well performance on multiple datasets presented by the sponsor and the specific results can be referred to the experimental results section.
Differential Fault Analysis on the Key Schedule of the LBlock Algorithm

In 2011, Wu and Zhang proposed a lightweight block cipher named LBlock, whose plaintext size and key size are 64 bits and 80 bits, respectively.As they said, LBlock can be efficiently implemented in constrained hardware environments, such as wireless sensor network.In this paper, we study the security of LBlock against the differential fault analysis (DFA) on its key scheduling.As far as we know, this is the first time the DFA on key schedule is used to analyze LBlock.More concretely, our DFA attack adopts the random nibble fault model.When the fault is injected in the subkeys in round 30 and 31, most bits of the subkeys in round 31 and 32 can be recovered, which leads to the leakage of the bits of master secret key according to the key scheduling.A quantitative analysis of the input-output differentials of the S-boxes in LBlock shows that our attack reduces the searching space of master key from 2 80 to 2 23 .Then the exhaustive search of all 2 23 possible keys can uniquely determine the true master key.Finally, we also implement LBlock and simulate the DFA on its key scheduling.The experiment results show that our attack is effective.
MuCoT: Multilingual Contrastive Training for Question-Answering in Low-resource Languages

Accuracy of English-language Question Answering (QA) systems has improved significantly in recent years with the advent of Transformer-based models (e.g., BERT).These models are pre-trained in a self-supervised fashion with a large English text corpus and further fine-tuned with a massive English QA dataset (e.g., SQuAD).However, QA datasets on such a scale are not available for most of the other languages.Multi-lingual BERT-based models (mBERT) are often used to transfer knowledge from high-resource languages to low-resource languages.Since these models are pre-trained with huge text corpora containing multiple languages, they typically learn language-agnostic embeddings for tokens from different languages.However, directly training an mBERT-based QA system for low-resource languages is challenging due to the paucity of training data.In this work, we augment the QA samples of the target language using translation and transliteration into other languages and use the augmented data to finetune an mBERT-based QA model, which is already pre-trained in English.Experiments on the Google ChAII dataset show that finetuning the mBERT model with translations from the same language family boosts the question-answering performance, whereas the performance degrades in the case of crosslanguage families.We further show that introducing a contrastive loss between the translated question-context feature pairs during the finetuning process, prevents such degradation with cross-lingual family translations and leads to marginal improvement.The code for this work is available at https://github.com/gokulkarthik/mucot.
Towards Collaborative Neural-Symbolic Graph Semantic Parsing via Uncertainty

Recent work in task-independent graph semantic parsing has shifted from grammar-based symbolic approaches to neural models, showing strong performance on different types of meaning representations.However, it is still unclear that what are the limitations of these neural parsers, and whether these limitations can be compensated by incorporating symbolic knowledge into model inference.In this paper, we address these questions by taking English Resource Grammar (ERG) parsing as a case study.Specifically, we first develop a state-of-the-art, T5-based neural ERG parser, and conduct detail analyses of parser performance within fine-grained linguistic categories.The neural parser attains superior performance on in-distribution test set, but degrades significantly on long-tail situations, while the symbolic parser performs more robustly.To address this, we further propose a simple yet principled collaborative framework for neural-symbolic semantic parsing, by designing a decision criterion for beam search that incorporates the prior knowledge from a symbolic parser and accounts for model uncertainty.Experimental results show that the proposed framework yields comprehensive improvement over neural baseline across long-tail categories, yielding the best known SMATCH score (97.01) on the well-studied DeepBank benchmark.
Measuring Fairness of Text Classifiers via Prediction Sensitivity

With the rapid growth in language processing applications, fairness has emerged as an important consideration in data-driven solutions.Although various fairness definitions have been explored in the recent literature, there is lack of consensus on which metrics most accurately reflect the fairness of a system.In this work, we propose a new formulation -ACCU-MULATED PREDICTION SENSITIVITY, which measures fairness in machine learning models based on the model's prediction sensitivity to perturbations in input features.The metric attempts to quantify the extent to which a single prediction depends on a protected attribute, where the protected attribute encodes the membership status of an individual in a protected group.We show that the metric can be theoretically linked with a specific notion of group fairness (statistical parity) and individual fairness.It also correlates well with humans' perception of fairness.We conduct experiments on two text classification datasets -JIGSAW TOXICITY, and BIAS IN BIOS, and evaluate the correlations between metrics and manual annotations on whether the model produced a fair outcome.We observe that the proposed fairness metric based on prediction sensitivity is statistically significantly more correlated with human annotation than the existing counterfactual fairness metric.
Simultaneous Neural Machine Translation with Prefix Alignment

Simultaneous translation is a task that requires starting translation before the speaker has finished speaking, so we face a trade-off between latency and accuracy.In this work, we focus on prefix-to-prefix translation and propose a method to extract alignment between bilingual prefix pairs.We use the alignment to segment a streaming input and fine-tune a translation model.The proposed method demonstrated higher BLEU than those of baselines in low latency ranges in our experiments on the IWSLT simultaneous translation benchmark.
Memory-aligned Knowledge Graph for Clinically Accurate Radiology Image Report Generation

Automatic generating the clinically accurate radiology report from X-ray images is important but challenging.The identification of multigrained abnormal regions in image and corresponding abnormalities is difficult for datadriven neural models.In this work, we introduce a Memory-aligned Knowledge Graph (MaKG) of clinical abnormalities to better learn the visual patterns of abnormalities and their relationships by integrating it into a deep model architecture for the report generation.We carry out extensive experiments and show that the proposed MaKG deep model can improve the clinical accuracy of the generated reports.
On Isotropy Calibration of Transformer Models

Different studies of the embedding space of transformer models suggest that the distribution of contextual representations is highly anisotropic -the embeddings are distributed in a narrow cone.Meanwhile, static word representations (e.g., Word2Vec or GloVe) have been shown to benefit from isotropic spaces.Therefore, previous work has developed methods to calibrate the embedding space of transformers in order to ensure isotropy.However, a recent study (Cai et al., 2021) shows that the embedding space of transformers is locally isotropic, which suggests that these models are already capable of exploiting the expressive capacity of their embedding space.In this work, we conduct an empirical evaluation of state-of-the-art methods for isotropy calibration on transformers and find that they do not provide consistent improvements across models and tasks.These results support the thesis that, given the local isotropy, transformers do not benefit from additional isotropy calibration.
Exploring Cross-lingual Text Detoxification with Large Multilingual Language Models.

Detoxification is a task of generating text in polite style while preserving meaning and fluency of the original toxic text.Existing detoxification methods are designed to work in one exact language.This work investigates multilingual and cross-lingual detoxification and the behavior of large multilingual models like in this setting.Unlike previous works we aim to make large language models able to perform detoxification without direct fine-tuning in given language.Experiments show that multilingual models are capable of performing multilingual style transfer.However, models are not able to perform cross-lingual detoxification and direct fine-tuning on exact language is inevitable.
CAISA at WASSA 2022: Adapter-Tuning for Empathy Prediction

We build a system that leverages adapters, a light weight and efficient method for leveraging large language models to perform the task Empathy and Distress prediction tasks for WASSA 2022.In our experiments, we find that stacking our empathy and distress adapters on a pretrained emotion classification adapter performs best compared to full fine-tuning approaches and emotion feature concatenation.We make our experimental code publicly available. 1
RoMe: A Robust Metric for Evaluating Natural Language Generation

Evaluating Natural Language Generation (NLG) systems is a challenging task.Firstly, the metric should ensure that the generated hypothesis reflects the reference's semantics.Secondly, it should consider the grammatical quality of the generated sentence.Thirdly, it should be robust enough to handle various surface forms of the generated sentence.Thus, an effective evaluation metric has to be multifaceted.In this paper, we propose an automatic evaluation metric incorporating several core aspects of natural language understanding (language competence, syntactic and semantic variation).Our proposed metric, RoMe, is trained on language features such as semantic similarity combined with tree edit distance and grammatical acceptability, using a self-supervised neural network to assess the overall quality of the generated sentence.Moreover, we perform an extensive robustness analysis of the state-of-the-art methods and RoMe.Empirical results suggest that RoMe has a stronger correlation to human judgment over state-of-the-art metrics in evaluating system-generated sentences across several NLG tasks.
On the Impact of Data Augmentation on Downstream Performance in Natural Language Processing

With in the broader scope of machine learning, data augmentation is a common strategy to improve generalization and robustness of machine learning models.While data augmentation has been widely used within computer vision, its use in the NLP has been comparably rather limited.The reason for this is that within NLP, the impact of proposed data augmentation methods on performance has not been evaluated in a unified manner, and effective data augmentation methods are unclear.In this paper, we look to tackle this by evaluating the impact of 12 data augmentation methods on multiple datasets when finetuning pre-trained language models.We find minimal improvements when data sizes are constrained to a few thousand, with performance degradation when data size is increased.We also use various methods to quantify the strength of data augmentations, and find that these values, though weakly correlate with downstream performance, correlate negatively or positively depending on the task.Furthermore, we find a glaring lack of consistently performant data augmentations.This all alludes to the difficulty of data augmentations for NLP tasks and we are inclined to believe that static data augmentations are not broadly applicable given these properties.
The Roles of Cloud-Based Systems on the Cancer-Related Studies: A Systematic Literature Review

The advances in Wireless-based technologies and intelligent diagnostics and forecasting such as cloud computing have significantly affected our lifestyle, observed in many fields, especially healthcare.Also, since the number of new cases of cancer has become very high, there is a need to investigate this matter deeply.Still, there is no systematic review on the application or implementation of the cloud in cancer-care services.Hence, this paper has introduced a comprehensive review of a cloud-centered healthcare system that emphasizes treatment ways in different types of cancer until Sep 2021.The results have shown that the largest study was about the relationship between cancer and the cloud associated with breast cancer.Also, the results have shown that cloud computing facilitates data protection, privacy, and medical record access.Using cloud computing in hospitals, physicians will use advanced programs and tools, and nurses will quickly access patients' information with new Wireless-based technologies.A strong understanding of the practical aspects of cloud computing will help researchers effectively navigate the vast data ecosystems in cancer research.So, by highlighting the advantages and drawbacks of analyzed articles, this study provides a comprehensive and up-to-date report on the field of cloud-based cancer studies to fill the previous gaps.
On Continual Model Refinement in Out-of-Distribution Data Streams

Real-world natural language processing (NLP) models need to be continually updated to fix the prediction errors in out-of-distribution (OOD) data streams while overcoming catastrophic forgetting.However, existing continual learning (CL) problem setups cannot cover such a realistic and complex scenario.In response to this, we propose a new CL problem formulation dubbed continual model refinement (CMR).Compared to prior CL settings, CMR is more practical and introduces unique challenges (boundary-agnostic and non-stationary distribution shift, diverse mixtures of multiple OOD data clusters, error-centric streams, etc.).We extend several existing CL approaches to the CMR setting and evaluate them extensively.For benchmarking and analysis, we propose a general sampling algorithm to obtain dynamic OOD data streams with controllable nonstationarity, as well as a suite of metrics measuring various aspects of online performance.Our experiments and detailed analysis reveal the promise and challenges of the CMR problem, supporting that studying CMR in dynamic OOD streams can benefit the longevity of deployed NLP models in production.
White light interferometry with spectral-temporal demodulation for large-range thickness measurement

Film thickness measurement can be realized using white light interferometry, but it is challenging to guarantee high precision in a large range of thicknesses. Based on scanning white light interferometry, we propose a spectral-temporal demodulation scheme for large-range thickness measurement. The demodulation process remains unchanged for either coatings or substrate-free films, while some adjustments are made according to the estimated optical thickness. Experiments show that the single-point repeatabilities for 500 nm SiO2 coating and 68 µm substrate-free Si film are no more than 0.70 nm and 1.22 nm, respectively. This method can be further developed for simultaneous measurement of surface profile and film thickness.