Reviewerly/exHarmony huggingface dataset:
Knowledge Base Index Compression via Dimensionality and Precision Reduction

Recently neural network based approaches to knowledge-intensive NLP tasks, such as question answering, started to rely heavily on the combination of neural retrievers and readers.Retrieval is typically performed over a large textual knowledge base (KB) which requires significant memory and compute resources, especially when scaled up.On HotpotQA we systematically investigate reducing the size of the KB index by means of dimensionality (sparse random projections, PCA, autoencoders) and numerical precision reduction.Our results show that PCA is an easy solution that requires very little data and is only slightly worse than autoencoders, which are less stable.All methods are sensitive to preand post-processing and data should always be centered and normalized both before and after dimension reduction.Finally, we show that it is possible to combine PCA with using 1bit per dimension.Overall we achieve (1) 100× compression with 75%, and (2) 24× compression with 92% original retrieval performance.
The Impact of Security and Payment Method On Consumers’ Perception of Marketplace in Saudi Arabia

Digital transformation has been accelerated in recent years, and COVID-19 has resulted in a rise in overall internet spending.Businesses must take measures in order to ensure that customers have a safe and enjoyable online purchasing experience.In this paper, customers' security perceptions regarding the most popular e-commerce applications in Saudi Arabia are explored.Surveys were distributed online via Google Form to 200 participants in total as part of a crosssectional research design using quantitative methodology.The main findings were related to confirming eight main hypotheses of the research that were related to testing if some factors were important to forming perceived trust by customers.Five factors (trust, security, reputation, benefits, and convenience) were found to have a positive effect, and the remaining three were not (familiarity, size, and usefulness).Finally, this study recommends various actions for practitioners and policymakers to take in order to improve customer perceptions of payment methods and security in Saudi Arabia.
KP-USE: An Unsupervised Approach for Key-Phrases Extraction from Documents

Automatic key-phrase extraction (AKE) is one of the most popular research topics in the field of natural language processing (NLP).Several techniques were used to extract the key-phrases: statistical, graph-based, classification algorithms, deep learning, and embedding techniques.AKE approaches that use embedding techniques are based on calculating the semantic similarity between a vector representing the document and the vectors representing the candidate phrases.However, most of these methods only give acceptable results in short texts such as abstracts paper, but on the other hand, their performance remains weak in long documents because it is represented by a single vector.Generally, the key phrases of a document are often expressed in certain parts of the document as, the title, the summary, and to a lesser extent in the introduction and the conclusion, and not of the entire document.For this reason, we propose in this paper KP-USE.A method extracts key-phrases from long documents based on the semantic similarity of candidate phrases to parts of the document containing keyphrases.KP-USE makes use of the Universal Sentence Encoder (USE) as an embedding method for text representation.We evaluated the performance of the proposed method on three datasets containing long papers, namely, NUS, Krapivin2009, and SemEval2010, where the results showed its performance outperforms recent AKE methods which are based on embedding techniques.
Loss-tolerant measurement device independent quantum key distribution with reference frame misalignment

Reference frame independent and measurement device independent quantum key distribution (RFI-MDI-QKD) has the advantages of being immune to detector side loopholes and misalignment of the reference frame. However, several former related research works are based on the unrealistic assumption of perfect source preparation. In this paper, we merge a loss-tolerant method into RFI-MDI-QKD to consider source flaws into key rate estimation and compare it with quantum coin method. Based on a reliable experimental scheme, the joint influence of both source flaws and reference frame misalignment is discussed with consideration of the finite-key effect. The results show that the loss-tolerant RFI-MDI-QKD protocol can reach longer key rate performance while considering the existence of source flaws in a real-world implementation.
Opinion-based Relational Pivoting for Cross-domain Aspect Term Extraction

Domain adaptation methods often exploit domain-transferable input features, a.k.a.pivots.The task of Aspect and Opinion Term Extraction presents a special challenge for domain transfer: while opinion terms largely transfer across domains, aspects change drastically from one domain to another (e.g. from restaurants to laptops).In this paper, we investigate and establish empirically a prior conjecture, which suggests that the linguistic relations connecting opinion terms to their aspects transfer well across domains and therefore can be leveraged for cross-domain aspect term extraction.We present several analyses supporting this conjecture, via experiments with four linguistic dependency formalisms to represent relation patterns.Subsequently, we present an aspect term extraction method that drives models to consider opinion-aspect relations via explicit multitask objectives.This method provides significant performance gains, even on top of a prior state-of-the-art linguistically-informed model, which are shown in analysis to stem from the relational pivoting signal.
BERT 4EVER@LT-EDI-ACL2022-Detecting signs of Depression from Social Media:Detecting Depression in Social Media using Prompt-Learning and Word-Emotion Cluster

In this paper, we report the solution of the team BERT 4EVER for the LT-EDI-2022 shared task2: Homophobia/Transphobia Detection in social media comments in ACL 2022, which aims to classify Youtube comments into one of the following categories: no, moderate, or severe depression.We model the problem as a text classification task and a text generation task and respectively propose two different models for the tasks.To combine the knowledge learned from these two different models, we softly fuse the predicted probabilities of the models above and then select the label with the highest probability as the final output.In addition, multiple augmentation strategies are leveraged to improve the model generalization capability, such as back translation and adversarial training.Experimental results demonstrate the effectiveness of the proposed models and two augmented strategies.
Explicit Object Relation Alignment for Vision and Language Navigation

In this paper, we investigate the problem of vision and language navigation.To solve this problem, grounding the landmarks and spatial relations in the textual instructions into visual modality is important.We propose a neural agent named Explicit Object Relation Alignment Agent (EXOR), to explicitly align the spatial information in both instruction and the visual environment, including landmarks and spatial relationships between the agent and landmarks.Empirically, our proposed method surpasses the baseline by a large margin on the R2R dataset.We provide a comprehensive analysis to show our model's spatial reasoning ability and explainability.
Pseudo Ambiguous and Clarifying Questions Based on Sentence Structures Toward Clarifying Question Answering System

Question answering (QA) with disambiguation questions is essential for practical QA systems because user questions often do not contain information enough to find their answers.We call this task clarifying question answering, a task to find answers to ambiguous user questions by disambiguating their intents through interactions.There are two major problems in building a clarifying question answering system: data preparation of possible ambiguous questions and the generation of clarifying questions.In this paper, we tackle these problems by sentence generation methods using sentence structures.Ambiguous questions are generated by eliminating a part of a sentence considering the sentence structure.Clarifying the question generation method based on case frame dictionary and sentence structure is also proposed.Our experimental results verify that our pseudo ambiguous question generation successfully adds ambiguity to questions.Moreover, the proposed clarifying question generation recovers the performance drop by asking the user for missing information.
EPT-X: An Expression-Pointer Transformer model that generates eXplanations for numbers

In this paper, we propose a neural model EPT-X (Expression-Pointer Transformer with Explanations), which utilizes natural language explanations to solve an algebraic word problem.To enhance the explainability of the encoding process of a neural model, EPT-X adopts the concepts of plausibility and faithfulness which are drawn from math word problem solving strategies by humans.A plausible explanation is one that includes contextual information for the numbers and variables that appear in a given math word problem.A faithful explanation is one that accurately represents the reasoning process behind the model's solution equation.The EPT-X model yields an average baseline performance of 69.59% on our PEN dataset and produces explanations with quality that is comparable to human output.The contribution of this work is two-fold.(1) EPT-X model: An explainable neural model that sets a baseline for algebraic word problem solving task, in terms of model's correctness, plausibility, and faithfulness.(2) New dataset: We release a novel dataset PEN (Problems with Explanations for Numbers), which expands the existing datasets by attaching explanations to each number/variable.
Communicative resources of socio-economic development and pedagogical activity in the context of globalization challenges

From the point of view of the theory of social systems, culture is rather a wide field of possibilities that are revealed in human communication. This is our main initial methodological position, which in the future we recommend for use in socio-economic and humanitarian studies not so much in the regime of a rigid method that formats research, but as a cross-cutting value and ideological setting. Communication, as a message, can always give a different meaning to a report, but this is immediately apparent in social relationships. What does not work in such cases is the principle of communication, namely the difference between information and a message, which gives the message itself the character of an event that requires a reaction. It is at the level of intimate social relations, where the fundamental dependence of the realization of social freedom in the external social instance is manifested – even if it is a loved person. While multifunctional itself, morality can limit the scope of a functional specification. In this case, social interpenetration cannot be distinguished without taking into account interhuman relations. In addition, morality imposes its own limitations on the sphere of intimate relationships. After all, it is also impossible to deepen between people if it is associated with public morality. The model of a unified and standardized world (single and uniform) is being replaced by new concepts of globalization, containing the idea of preserving cultural diversity in people’s lives.
Does BERT Know that the IS-A Relation Is Transitive?

The success of a natural language processing (NLP) system on a task does not amount to fully understanding the complexity of the task, typified by many deep learning models.One such question is: can a black-box model make logically consistent predictions for transitive relations?Recent studies suggest that pre-trained BERT can capture lexico-semantic clues from words in the context.However, to what extent BERT captures the transitive nature of some lexical relations is unclear.From a probing perspective, we examine WordNet word senses and the IS-A relation, which is a transitive relation.That is, for senses A, B, and C, A is-a B and B is-a C entail A is-a C. We aim to quantify how much BERT agrees with the transitive property of IS-A relations, via a minimalist probing setting.Our investigation reveals that BERT's predictions do not fully obey the transitivity property of the IS-A relation. 1
ChatMatch: Evaluating Chatbots by Autonomous Chat Tournaments

Existing automatic evaluation systems of chatbots mostly rely on static chat scripts as ground truth, which is hard to obtain, and requires access to the models of the bots as a form of "white-box testing".Interactive evaluation mitigates this problem but requires human involvement.In our work, we propose an interactive chatbot evaluation framework in which chatbots compete with each other like in a sports tournament, using flexible scoring metrics.This framework can efficiently rank chatbots independently from their model architectures and the domains for which they are trained.
Skipout: An Adaptive Layer-Level Regularization Framework for Deep Neural Networks

Regularization methods can surprisingly improve the generalization ability of deep neural networks.Among numerous methods, the branch of Dropout regularization is very popular in practice.However, Dropout-like regularization variants have some deficiencies: blindness of regularization, dependency of regularization on layer types, and how to determine the parameters of regularization.In this paper, we propose Skipout framework to tackle these drawbacks by breaking the chain of co-adapted units in consecutive layers.Skipout is a layer-level regularization method that categorizes the layers of a deep network adaptively during training.It divides them into robust and critical layers based on the network architecture and the given task.Instead of turning off some units as in Dropout methods, Skipout recognizes the robust layers and skips them out untrained while sustaining to train the critical layers.The trick of Skipout is in backpropagating the cumulated residual errors via the robust layers, where the activation functions of their units are temporarily set to identity.The units of robust layers are dually activated: by their own functions in the forward pass and by identity in the backward pass.Therefore, Skipout takes advantage of vanishing gradient solutions via robust layers.Moreover, its implementation is simple and applicable to both convolutional and fully-connected layers.Experiments on diverse benchmark datasets and different deep models confirm that Skipout framework can improve the generalization performance of deep neural networks.
RELiC: Retrieving Evidence for Literary Claims

Humanities scholars commonly provide evidence for claims that they make about a work of literature (e.g., a novel) in the form of quotations from the work.We collect a large-scale dataset (RELiC) of 78K literary quotations and surrounding critical analysis and use it to formulate the novel task of literary evidence retrieval, in which models are given an excerpt of literary analysis surrounding a masked quotation and asked to retrieve the quoted passage from the set of all passages in the work.Solving this retrieval task requires a deep understanding of complex literary and linguistic phenomena, which proves challenging to methods that overwhelmingly rely on lexical and semantic similarity matching.We implement a RoBERTa-based dense passage retriever for this task that outperforms existing pretrained information retrieval baselines; however, experiments and analysis by human domain experts indicate that there is substantial room for improvement over our dense retriever.
Fast Few-shot Debugging for NLU Test Suites

We study few-shot debugging of transformer based natural language understanding models, using recently popularized test suites to not just diagnose but correct a problem.Given a few debugging examples of a certain phenomenon, and a held-out test set of the same phenomenon, we aim to maximize accuracy on the phenomenon at a minimal cost of accuracy on the original test set.We examine several methods that are faster than full epoch retraining.We introduce a new fast method, which samples a few in-danger examples from the original training set.Compared to fast methods using parameter distance constraints or Kullback-Leibler divergence, we achieve superior original accuracy for comparable debugging accuracy.
CLUES: A Benchmark for Learning Classifiers using Natural Language Explanations

Supervised learning has traditionally focused on inductive learning by observing labeled examples of a task.In contrast, humans have the ability to learn new concepts from language.Here, we explore learning zero-shot classifiers for structured data 1 purely from language from natural language explanations as supervision.For this, we introduce CLUES, a benchmark for Classifier Learning Using natural language ExplanationS, consisting of a range of classification tasks over structured data along with natural language supervision in the form of explanations.CLUES consists of 36 real-world and 144 synthetic classification tasks.It contains crowdsourced explanations describing real-world tasks from multiple teachers and programmatically generated explanations for the synthetic tasks.We also introduce ExEnt, an entailment-based method for training classifiers from language explanations, which explicitly models the influence of individual explanations in making a prediction.ExEnt generalizes up to 18% better (relative) on novel tasks than a baseline that does not use explanations.We identify key challenges in learning from explanations, addressing which can lead to progress on CLUES in the future.Our code and datasets are available at: https: //clues-benchmark.github.io.
MILIE: Modular &amp; Iterative Multilingual Open Information Extraction

Open Information Extraction (OpenIE) is the task of extracting (subject, predicate, object) triples from natural language sentences.Current OpenIE systems extract all triple slots independently.In contrast, we explore the hypothesis that it may be beneficial to extract triple slots iteratively: first extract easy slots, followed by the difficult ones by conditioning on the easy slots, and therefore achieve a better overall extraction.
The effect of service innovation, corporate image, human capital strategy and customer loyalty on performance: Evidence from rice industry

The current study purpose is to investigate the effect of service innovation, corporate image, and human capital strategy and customer loyalty on performance for small and micro community enterprise of rice products in central northeast Thailand. For this objective, researchers applied the quantitative research approach and used the cross sectional research design. To obtain data, the researchers distributed questionnaires directly and online to respondents which were small and micro community enterprise of rice products in central northeast Thailand for 1 month from the beginning of September and obtained valid answers totaling 320 responses, which then we chose to be the sample in this study. The Structural Equation Modeling (SEM) results show that corporate image had a positive and significant relationship with the human capital strategy. In the same vein, the customer loyalty had also a positive and significant relationship with the humane capital strategy. Further findings show that service innovation had also a positive and significant relationship with the human capital strategy. Moreover, service innovation, corporate image, customer loyalty had also a positive and significant relationship with the business performance. On the other hand, human capital strategy had also a positive and significant relationship with the business performance. Based on these findings, the current study could provide researchers and policy makers to know about the importance of all predictors to increase their business performance.
Graph Refinement for Coreference Resolution

The state-of-the-art models for coreference resolution are based on independent mention pairwise decisions.We propose a modelling approach that learns coreference at the documentlevel and takes global decisions.For this purpose, we model coreference links in a graph structure where the nodes are tokens in the text, and the edges represent the relationship between them.Our model predicts the graph in a non-autoregressive manner, then iteratively refines it based on previous predictions, allowing global dependencies between decisions.The experimental results show improvements over various baselines, reinforcing the hypothesis that document-level information improves conference resolution.
Meta-XNLG: A Meta-Learning Approach Based on Language Clustering for Zero-Shot Cross-Lingual Transfer and Generation: A Meta-Learning Approach Based on Language Clustering for Zero-Shot Cross-Lingual Transfer and Generation

Recently, the NLP community has witnessed a rapid advancement in multilingual and crosslingual transfer research where the supervision is transferred from high-resource languages (HRLs) to low-resource languages (LRLs).However, the cross-lingual transfer is not uniform across languages, particularly in the zeroshot setting.Towards this goal, one promising research direction is to learn shareable structures across multiple tasks with limited annotated data.The downstream multilingual applications may benefit from such a learning setup as most of the languages across the globe are low-resource and share some structures with other languages.In this paper, we propose a novel meta-learning framework (called Meta-X NLG ) to learn shareable structures from typologically diverse languages based on metalearning and language clustering.This is a step towards uniform cross-lingual transfer for unseen languages.We first cluster the languages based on language representations and identify the centroid language of each cluster.Then, a meta-learning algorithm is trained with all centroid languages and evaluated on the other languages in the zero-shot setting.We demonstrate the effectiveness of this modeling on two NLG tasks (Abstractive Text Summarization and Question Generation), 5 popular datasets and 30 typologically diverse languages.Consistent improvements over strong baselines demonstrate the efficacy of the proposed framework.The careful design of the model makes this end-to-end NLG setup less vulnerable to the accidental translation problem, which is a prominent concern in zero-shot cross-lingual NLG tasks.