Reviewerly/exHarmony huggingface dataset:
OpenBrand: Open Brand Value Extraction from Product Descriptions

Extracting attribute-value information from unstructured product descriptions continue to be of a vital importance in e-commerce applications.One of the most important product attributes is the brand which highly influences customers' purchasing behaviour.Thus, it is crucial to accurately extract brand information dealing with the main challenge of discovering new brand names.Under the open world assumption, several approaches have adopted deep learning models to extract attribute-values using sequence tagging paradigm.However, they did not employ finer grained data representations such as character level embeddings which improve generalizability.In this paper, we introduce OpenBrand, a novel approach for discovering brand names.OpenBrand is a BiLSTM-CRF-Attention model with embeddings at different granularities.Such embeddings are learned using CNN and LSTM architectures to provide more accurate representations.We further propose a new dataset for brand value extraction, with a very challenging task on zero-shot extraction.We have tested our approach, through extensive experiments, and shown that it outperforms state-of-the-art models in brand name discovery.
Improving Multi-label Malevolence Detection in Dialogues through Multi-faceted Label Correlation Enhancement

A dialogue response is malevolent if it is grounded in negative emotions, inappropriate behavior, or an unethical value basis in terms of content and dialogue acts.The detection of malevolent dialogue responses is attracting growing interest.Current research on detecting dialogue malevolence has limitations in terms of datasets and methods.First, available dialogue datasets related to malevolence are labeled with a single category, but in practice assigning a single category to each utterance may not be appropriate as some malevolent utterances belong to multiple labels.Second, current methods for detecting dialogue malevolence neglect label correlation.Therefore, we propose the task of multi-label dialogue malevolence detection and crowdsource a multilabel dataset, multi-label dialogue malevolence detection (MDMD) for evaluation.We also propose a multi-label malevolence detection model, multi-faceted label correlation enhanced CRF (MCRF), with two label correlation mechanisms, label correlation in taxonomy (LCT) and label correlation in context (LCC).Experiments on MDMD show that our method outperforms the best performing baseline by a large margin, i.e., 16.1%, 11.9%, 12.0% and 6.1% on precision, recall, F1, and Jaccard score, respectively.
On-Demand Virtualization for Post-Copy OS Migration in Bare-Metal Cloud

The demand for bare-metal cloud services has increased rapidly because bare-metal cloud is cost-effective for various types of cloud workloads. However, as the bare-metal cloud does not utilize the abstraction of the virtualization layer, it misses the benefits of virtualization. One important benefits absent in the bare-metal cloud is the live migration of guest operating systems. Migrating an OS and applications in the OS as a single unit provides a convenient way to manage cloud services such as load balancing, fault management, and system maintenance. To enable live migration for bare-metal cloud, several approaches have been proposed but they have limitations; they require OS modifications or impose additional overheads for workloads. This paper suggests an on-demand virtualization technique for post-copy OS migration to improve manageability of the bare-metal cloud services. When live migration is requested, a lightweight virtualization layer is enabled in the host on the fly. After completion of the live migration, the virtualization layer is removed from the host. Therefore, the host returns to a bare-metal system for performance. To implement on-demand virtualization, we modify BitVisor to perform the post-copy migration on the x86 architecture. The elapsed time of on-demand virtualization is negligible. It takes only 20 ms to insert the virtualization layer and 30 ms to remove the one. The downtime of migration is reduced because of the post-copy migration.
Tone in Runyankore Verb Stem Reduplication

In this paper I describe the surprisingly extensive range of choices Runyankore speakers have in “devaluative-frequentative” verb stem reduplication (“to sort of do X, to do X here and there, to do X a lot”). Analyzed as stem-compounding, both single (stem1-stem2) and multiple (stem1-stem2-stem3...) reduplication are possible of a stem such as furumuka “dash out” (furumuka-furumuka(-furumuka...)), with the possibility of left-aligned truncation (furu-furumuka, furumu-furumuka), right-aligned truncation (furumuka-muka, furumuka-rumuka) and both (furu-furumuka-muka). In addition, prefinal stems can alternatively end in “replacive [a]” (fura-furumuka, furuma-furumuka). Complementing these variants is a “mixed” system where both stems are truncated (furumu-rumuka), to which additional reduplicated stems can also be added (furu-furumu-rumuka-muka). While each reduplicated stem is free to choose its shape independently of the others (e.g. furu-furuma-furumuka, furuma-furu-furumuka etc.), the same three possible H(igh) stem-tone patterns are observed in different inflections, predictable from the input tones: /H/ on the first stem, /H/ on the last stem, /H/ on the second mora of any stem (au choix). I show that these facts require each stem to be independently derived from the same (complete) morphological and phonological input with tone assigned prior to truncation, thereby directly supporting both reduplication as compounding (Downing 2003) and morphological doubling theory (MDT) (Inkelas &amp; Zoll 2005).
Ranking-Constrained Learning with Rationales for Text Classification

We propose a novel approach that jointly utilizes the labels and elicited rationales for text classification to speed up the training of deep learning models with limited training data.We define and optimize a ranking-constrained loss function that combines cross-entropy loss with ranking losses as rationale constraints.We evaluate our proposed rationale-augmented learning approach on three human-annotated datasets, and show that our approach provides significant improvements over classification approaches that do not utilize rationales as well as other state-of-the-art rationaleaugmented baselines.
Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style Transfer

We exploit the pre-trained seq2seq model mBART for multilingual text style transfer.Using machine translated data as well as gold aligned English sentences yields state-of-theart results in the three target languages we consider.Besides, in view of the general scarcity of parallel data, we propose a modular approach for multilingual formality transfer, which consists of two training strategies that target adaptation to both language and task.Our approach achieves competitive performance without monolingual task-specific parallel data and can be applied to other style transfer tasks as well as to other languages.
Clue Propagation Based on Non-Adjective Opinion Words for Handling Disconnected Propagation in Product Reviews

Recently, much research has focused on explicit aspect extraction.User reviews in the textual form are unstructured data, creating a very high complexity when processed for sentiment analysis.Previous propagation approaches proposed in this area mainly focused only on adjectiveand-noun-based relations.By using only a small seed of opinion words, there is a possibility that the propagation can be disconnected, resulting in several aspects and opinions remaining unextracted.To overcome the aforementioned problem, we propose clue propagation method by using several clues to successfully identify aspects and opinions to extract explicit aspects, including noun-and-verb-based relations.The utilization of more than one clue in the propagation prevented premature termination, extending the scope of the extraction.We evaluated our proposed method by comparing it with several state-of-the-art methods.Empirically, the experimental results showed that the added more clues in clue propagation method could extract aspects and opinions that were not extracted in previous studies.Clue propagation can overcome the possibility of disconnected properties in the propagation method.
UMUTeam@TamilNLP-ACL2022: Abusive Detection in Tamil using Linguistic Features and Transformers

Social media has become a dangerous place as bullies take advantage of the anonymity the Internet provides to target and intimidate vulnerable individuals and groups.In the past few years, the research community has focused on developing automatic classification tools for detecting hate-speech, its variants, and other types of abusive behaviour.However, these methods are still at an early stage in low-resource languages.With the aim of reducing this barrier, the TamilNLP shared task has proposed a multi-classification challenge for Tamil written in Tamil script and code-mixed to detect abusive comments and hope-speech.Our participation consists of a knowledge integration strategy that combines sentence embeddings from BERT, RoBERTa, FastText and a subset of language-independent linguistic features.We achieved our best result in code-mixed, reaching 3rd position with a macro-average f1-score of 35%.
Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning

Pre-trained sequence-to-sequence language models have led to widespread success in many natural language generation tasks.However, there has been relatively less work on analyzing their ability to generate structured outputs such as graphs.Unlike natural language, graphs have distinct structural and semantic properties in the context of a downstream NLP task, e.g., generating a graph that is connected and acyclic can be attributed to its structural constraints, while the semantics of a graph can refer to how meaningfully an edge represents the relation between two node concepts.In this work, we study pre-trained language models that generate explanation graphs in an end-to-end manner and analyze their ability to learn the structural constraints and semantics of such graphs.We first show that with limited supervision, pre-trained language models often generate graphs that either violate these constraints or are semantically incoherent.Since curating large amount of humanannotated graphs is expensive and tedious, we propose simple yet effective ways of graph perturbations via node and edge edit operations that lead to structurally and semantically positive and negative graphs.Next, we leverage these graphs in different contrastive learning models with Max-Margin and InfoNCE losses.Our methods lead to significant improvements in both structural and semantic accuracy of explanation graphs and also generalize to other similar graph generation tasks.Lastly, we show that human errors are the best negatives for contrastive learning and also that automatically generating more such human-like negative graphs can lead to further improvements. 1
Designing a New Deep Convolutional Neural Network for White Blood Cells Classification

White Blood Cells (WBC) are one of the most important factors affecting the human immune system. Abnormalities in this type of blood cells are the causes of a wide variety of deadliest illnesses. Consequently, WBC classification plays a very important role in disease diagnosis and type of treatment required. Usually, conventional methods of white blood cells classification at first segment the suspected images and then extract features from the segmented images. But, achieving a high accuracy using these methods requires a very accurate segmentation. Hence, in this paper, we propose a new architecture for a Convolutional Neural Network (CNN) with 56 layers for white blood cell classification. The experimental results illustrate that the proposed method performs better compared to the state-of-the-art in the field. Our method reached to 99.2% accuracy on the famous BCCD benchmark dataset.
What Makes Reading Comprehension Questions Difficult?

For a natural language understanding benchmark to be useful in research, it has to consist of examples that are diverse and difficult enough to discriminate among current and near-future state-of-the-art systems.However, we do not yet know how best to select text sources to collect a variety of challenging examples.In this study, we crowdsource multiple-choice reading comprehension questions for passages taken from seven qualitatively distinct sources, analyzing what attributes of passages contribute to the difficulty and question types of the collected examples.To our surprise, we find that passage source, length, and readability measures do not significantly affect question difficulty.Through our manual annotation of seven reasoning types, we observe several trends between passage sources and reasoning types, e.g., logical reasoning is more often required in questions written for technical passages.These results suggest that when creating a new benchmark dataset, selecting a diverse set of passages can help ensure a diverse range of question types, but that passage difficulty need not be a priority.
Password Challenges for Older People in China and the United Kingdom

As vital services are moving increasingly online, it is important that everyone, including older people, can access them. Both high risk and many low risk online services require password authentication. Although there has been some research proposing easier to use and remember password systems, there is surprisingly little research on what problems older people have with real world authentication systems. This research conducted an online survey with 61 Chinese older people and 75 British older people about what problems they have in creating and managing passwords. There were differences between the samples of older people in the two countries in creating passwords, but few differences between the two samples in managing passwords. There were many problems in creating passwords, but not as many in terms of managing them. In particular, remembering passwords was not a particular concern, which may surprise many researchers who assume older people would have memory problems. The results have implications for where future research on password authentication for older users should be focussed.
Constructing Ensembles of Dispatching Rules for Multi-objective Problems

Scheduling represents an important aspect of many real-world processes, which is why such problems have been well studied in the literature. Such problems are often dynamic and require that multiple criteria be optimised simultaneously. Dispatching rules (DRs) are the method of choice for solving dynamic problems. However, existing DRs are usually implemented for the optimisation of only a single criterion. Since manual design of DRs is difficult, genetic programming (GP) has been used to automatically design new DRs for single and multiple objectives. However, the performance of a single rule is limited, and it may not work well in all situations. Therefore, ensembles have been used to create rule sets that outperform single DRs. The goal of this study is to adapt ensemble learning methods to create ensembles that optimise multiple criteria simultaneously. The method creates ensembles of DRs with multiple objectives previously evolved by GP to improve their performance. The results show that ensembles are suitable for the considered multi-objective problem.
Plug-and-Play Controller for Story Completion: A Pilot Study toward Emotion-aware Story Writing Assistance

Emotions are essential for storytelling and narrative generation, and as such, the relationship between stories and emotions has been extensively studied.The authors of this paper, including a professional novelist, have examined the use of natural language processing to address the problems of novelists from the perspective of practical creative writing.In particular, the story completion task, which requires understanding the existing unfinished context, was studied from the perspective of creative support for human writers, to generate appropriate content to complete the unfinished parts.It was found that unsupervised pre-trained large neural models of the sequence-to-sequence type are useful for this task.Furthermore, based on the plug-and-play module for controllable text generation using GPT-2, an additional module was implemented to consider emotions.Although this is a preliminary study, and the results leave room for improvement before incorporating the model into a practical system, this effort is an important step in complementing the emotional trajectory of the story.
Twitter account classification using account metadata: organizationvs. individual

Organizations present their existence on social media to gain followers and reach out to the crowds.Social media-related tasks and applications, such as social media graph construction, sentiment analysis, and bot detection, are required to identify the entities' account types.Some applications focus on personal accounts, whereas others only need nonpersonal accounts.This paper addresses the account classification problem using only minimum amount of data, which is the metadata of the account's profile.The proposed approach classifies accounts either as organization or individual, in a language-independent manner, without collecting the accounts' tweet content.The model uses a long short term memory (LSTM) network for processing the textual properties and a fully-connected neural network for processing the numerical features.We apply our solution to a collection of Twitter accounts, as it is one of the most widely used social networks.Our classifier, based solely on the account metadata, achieves an average of 97.4% accuracy under 7-fold cross-validation.The experiments show that the account metadata is a qualified resource for accurately estimating the account types.
Super-Resolution of GF-1 Multispectral Wide Field of View Images via a Very Deep Residual Coordinate Attention Network

GF-1 multispectral wide field of view (WFV) images, with a spatial resolution of 16 m, have been widely used in earth monitoring. However, the spatial details provided by WFV images are not sufficient for many applications. Thus, this letter proposes a novel WFV image super-resolution (SR) algorithm called GFRCAN based on a very deep residual coordinate attention network. To form a very deep network, the residual-in-residual (RIR) structure consisting of several residual groups (RG) with long skip connections is used. Meanwhile, the residual coordinate attention block (RCOAB) and adaptive multi-scale spatial attention module (AMSA) are incorporated to focus on the high-frequency information and multi-scale features adaptive weighted fusion. Besides, the spectral and spatial details of SR images are improved by incorporating peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) into the loss function. Both subjective and objective evaluation results show that the proposed model outperforms the state-of-the-art methods.
Psycholinguistic Diagnosis of Language Models’ Commonsense Reasoning

Neural language models have attracted a lot of attention in the past few years. More and more researchers are getting intrigued by how language models encode commonsense, specifically what kind of commonsense they understand, and why they do. This paper analyzed neural language models’ understanding of commonsense pragmatics (i.e., implied meanings) through human behavioral and neurophysiological data. These psycholinguistic tests are designed to draw conclusions based on predictive responses in context, making them very well suited to test word-prediction models such as BERT in natural settings. They can provide the appropriate prompts and tasks to answer questions about linguistic mechanisms underlying predictive responses. This paper adopted psycholinguistic datasets to probe language models’ commonsense reasoning. Findings suggest that GPT-3’s performance was mostly at chance in the psycholinguistic tasks. We also showed that DistillBERT had some understanding of the (implied) intent that’s shared among most people. Such intent is implicitly reflected in the usage of conversational implicatures and presuppositions. Whether or not fine-tuning improved its performance to human-level depends on the type of commonsense reasoning.
Estimate of 4-adic complexity of unified quaternary sequences of length $ 2p $

<p style='text-indent:20px;'>We derive the 4-adic complexity of unified quaternary sequences with period <inline-formula><tex-math id="M2">\begin{document}$ 2p $\end{document}</tex-math></inline-formula>. These sequences with good autocorrelation properties are proposed by Ke et al. in 2020. We estimate the 4-adic complexity of aforementioned sequences and show that any of them has high 4-adic complexity, which is good enough to resist the attack of the rational approximation algorithm.
Finding the Dominant Winning Ticket in Pre-Trained Language Models

The Lottery Ticket Hypothesis suggests that for any over-parameterized model, a small subnetwork exists to achieve competitive performance compared to the backbone architecture.In this paper, we study whether there is a winning lottery ticket for pre-trained language models, which allow the practitioners to fine-tune the parameters in the ticket but achieve good downstream performance.To achieve this, we regularize the fine-tuning process with L1 distance and explore the subnetwork structure (what we refer to as the "dominant winning ticket").Empirically, we show that (a) the dominant winning ticket can achieve performance that is comparable with that of the full-parameter model, (b) the dominant winning ticket is transferable across different tasks, (c) and the dominant winning ticket has a natural structure within each parameter matrix.Strikingly, we find that a dominant winning ticket that takes up 0.05% of the parameters can already achieve satisfactory performance, indicating that the PLM is significantly reducible during fine-tuning.
Factual Consistency of Multilingual Pretrained Language Models

Pretrained language models can be queried for factual knowledge, with potential applications in knowledge base acquisition and tasks that require inference.However, for that, we need to know how reliable this knowledge is, and recent work has shown that monolingual English language models lack consistency when predicting factual knowledge, that is, they fill-in-theblank differently for paraphrases describing the same fact.In this paper, we extend the analysis of consistency to a multilingual setting.We introduce a resource, MPARAREL 1 , and investigate (i) whether multilingual language models such as mBERT and XLM-R are more consistent than their monolingual counterparts; and (ii) if such models are equally consistent across languages.We find that mBERT is as inconsistent as English BERT in English paraphrases, but that both mBERT and XLM-R exhibit a high degree of inconsistency in English and even more so for all the other 45 languages.