Reviewerly/exHarmony huggingface dataset:
Joint Extraction of Multiple Relations and Entities from Building Code Clauses

The extraction of regulatory information is a prerequisite for automated code compliance checking. Although a number of machine learning models have been explored for extracting computer-understandable engineering constraints from code clauses written in natural language, most are inadequate to address the complexity of the semantic relations between named entities. In particular, the existence of two or more overlapping relations involving the same entity greatly exacerbates the difficulty of information extraction. In this paper, a joint extraction model is proposed to extract the relations among entities in the form of triplets. In the proposed model, a hybrid deep learning algorithm combined with a decomposition strategy is applied. First, all candidate subject entities are identified, and then, the associated object entities and predicate relations are simultaneously detected. In this way, multiple relations, especially overlapping relations, can be extracted. Furthermore, nonrelated pairs are excluded through the judicious recognition of subject entities. Moreover, a collection of domain-specific entity and relation types is investigated for model implementation. The experimental results indicate that the proposed model is promising for extracting multiple relations and entities from building codes.
An Intelligent Question Answering System of the Liao Dynasty Based on Knowledge Graph

Abstract The Liao Dynasty was a minority regime established by the Khitan on the grasslands of northern China. To promote and spread the cultural knowledge of the Liao Dynasty, an intelligent question-and-answer system is constructed based on the knowledge graph in the historical and cultural field of the Liao Dynasty. In the traditional question answering system, the quality of answers was not high due to incomplete data and distinctive vocabulary. To solve this problem, a combination method of Liao Dynasty question-and-answer database and KB is proposed to realize knowledge graph question answering, and a joint model of Siamese LSTM and fusion MatchPyramid is proposed for semantic matching between questions in the question-and-answer database. With the joint model, it is easy to perform semantic matching by fusing sentence-level and word-level interactive features through LSTM and MatchPyramid. Furthermore, the question sentence with the same semantics as the user input question sentence is retrieved in the question-and-answer database, and the answer corresponding to the question sentence is returned as the result. The experimental results show that our proposed method has achieved relatively good performance in the historical domain of the Liao Dynasty and the open-domain knowledge graph, and improved the accuracy of question and answer.
Enhanced Language Representation with Label Knowledge for Span Extraction

Span extraction, aiming to extract text spans (such as words or phrases) from plain texts, is a fundamental process in Information Extraction.Recent works introduce the label knowledge to enhance the text representation by formalizing the span extraction task into a question answering problem (QA Formalization), which achieves state-of-the-art performance.However, QA Formalization does not fully exploit the label knowledge and suffers from low efficiency in training/inference.To address those problems, we introduce a new paradigm to integrate label knowledge and further propose a novel model to explicitly and efficiently integrate label knowledge into text representations.Specifically, it encodes texts and label annotations independently and then integrates label knowledge into text representation with an elaborate-designed semantics fusion module.We conduct extensive experiments on three typical span extraction tasks: flat NER, nested NER, and event detection.The empirical results show that 1) our method achieves state-of-the-art performance on four benchmarks, and 2) reduces training time and inference time by 76% and 77% on average, respectively, compared with the QA Formalization paradigm.
One bound to rule them all: from Adiabatic to Zeno

We derive a universal nonperturbative bound on the distance between unitary evolutions generated by time-dependent Hamiltonians in terms of the difference of their integral actions. We apply our result to provide explicit error bounds for the rotating-wave approximation and generalize it beyond the qubit case. We discuss the error of the rotating-wave approximation over long time and in the presence of time-dependent amplitude modulation. We also show how our universal bound can be used to derive and to generalize other known theorems such as the strong-coupling limit, the adiabatic theorem, and product formulas, which are relevant to quantum-control strategies including the Zeno control and the dynamical decoupling. Finally, we prove generalized versions of the Trotter product formula, extending its validity beyond the standard scaling assumption.
Fidelity of time-bin-entangled multiphoton states from a quantum emitter

We devise a mathematical framework for assessing the fidelity of multi-photon entangled states generated by a single solid-state quantum emitter, such as a quantum dot or a nitrogen-vacancy center. Within this formalism, we theoretically study the role of imperfections present in real systems on the generation of time-bin encoded Greenberger-Horne-Zeilinger and one-dimensional cluster states. We consider both fundamental limitations, such as the effect of phonon-induced dephasing, interaction with the nuclear spin bath, and second-order emissions, as well as technological imperfections, such as branching effects, non-perfect filtering, and photon losses. In a companion paper, we consider a particular physical implementation based on a quantum dot emitter embedded in a photonic crystal waveguide and apply our theoretical formalism to assess the fidelities achievable with current technologies.
Semi-supervised Intent Discovery with Contrastive Learning

User intent discovery is a key step in developing a Natural Language Understanding (NLU) module at the core of any modern Conversational AI system.Typically, human experts review a representative sample of user input data to discover new intents, which is subjective, costly, and error-prone.In this work, we aim to assist the NLU developers by presenting a novel method for discovering new intents at scale given a corpus of utterances.Our method utilizes supervised contrastive learning to leverage information from a domainrelevant, already labeled dataset and identifies new intents in the corpus at hand using unsupervised K-means clustering.Our method outperforms the state-of-the-art by a large margin up to 2% and 13% on two benchmark datasets, measured by clustering accuracy.Furthermore, we apply our method on a large dataset from the travel domain to demonstrate its effectiveness on a real-world use case.
Twitter Sentiment Analysis using Machine Learning Techniques

Nowadays people share their views and opinions in twitter and other social media platforms, the way of recognizing sentiments and speculation in tweets is Twitter Sentiment Analysis. Determining the contradiction or sentiment of the tweets and then listing them into positive, negative and neutral tweets is the main classifying step in this process. The issue related to sentiment analysis is the naming of the correct congruous sentiment classifier algorithm to list the tweets. The foundation classifier techniques like Logistic regression, Naive Bayes classifier, Random Forest and SVMs are normally used. In this paper, the Naïve Bayes classifier and Logistic Regression has been used to perform sentiment analysis and classify based on the better accuracy of catagorizing Technique. The outcome shows that Naive Bayes classifier works better for this approach. Data pre-processing and feature extraction is realized as a portion of task.
Improving Stance Detection with Multi-Dataset Learning and Knowledge Distillation

Stance detection determines whether the author of a text is in favor of, against or neutral to a specific target and provides valuable insights into important events such as legalization of abortion.Despite significant progress on this task, one of the remaining challenges is the scarcity of annotations.Besides, most previous works focused on a hardlabel training in which meaningful similarities among categories are discarded during training.To address these challenges, first, we evaluate a multi-target and a multi-dataset training settings by training one model on each dataset and datasets of different domains, respectively.We show that models can learn more universal representations with respect to targets in these settings.Second, we investigate the knowledge distillation in stance detection and observe that transferring knowledge from a teacher model to a student model can be beneficial in our proposed training settings.Moreover, we propose an Adaptive Knowledge Distillation (AKD) method that applies instance-specific temperature scaling to the teacher and student predictions.Results show that the multi-dataset model performs best on all datasets and it can be further improved by the proposed AKD, outperforming the state-of-the-art by a large margin.We publicly release our code. 1
KERS: A Knowledge-Enhanced Framework for Recommendation Dialog Systems with Multiple Subgoals

Recommendation dialogs require the system to build a social bond with users to gain trust and develop affinity in order to increase the chance of a successful recommendation.It is beneficial to divide up, such conversations with multiple subgoals (such as social chat, question answering, recommendation, etc.), so that the system can retrieve appropriate knowledge with better accuracy under different subgoals.In this paper, we propose a unified framework for common knowledge-based multi-subgoal dialog: knowledge-enhanced multi-subgoal driven recommender system (KERS).We first predict a sequence of subgoals and use them to guide the dialog model to select knowledge from a sub-set of existing knowledge graph.We then propose three new mechanisms to filter noisy knowledge and to enhance the inclusion of cleaned knowledge in the dialog response generation process.Experiments show that our method obtains stateof-the-art results on DuRecDial dataset in both automatic and human evaluation.
APIRecX: Cross-Library API Recommendation via Pre-Trained Language Model

For programmers, learning the usage of APIs (Application Programming Interfaces) of a software library is important yet difficult.API recommendation tools can help developers use APIs by recommending which APIs to be used next given the APIs that have been written.Traditionally, language models such as N-gram are applied to API recommendation.However, because the software libraries keep changing and new libraries keep emerging, new APIs are common.These new APIs can be seen as OOV (out of vocabulary) words and cannot be handled well by existing API recommendation approaches due to the lack of training data.In this paper, we propose APIRecX, the first cross-library API recommendation approach, which uses BPE to split each API call in each API sequence and pre-trains a GPTbased language model.It then recommends APIs by fine-tuning the pre-trained model.APIRecX can migrate the knowledge of existing libraries to a new library, and can recommend APIs that are previously regarded as OOV.We evaluate APIRecX on six libraries and the results confirm its effectiveness by comparing with two typical API recommendation approaches.
Crosslingual Transfer Learning for Relation and Event Extraction via Word Category and Class Alignments

Previous work on crosslingual Relation and Event Extraction (REE) suffers from the monolingual bias issue due to the training of models on only the source language data.An approach to overcome this issue is to use unlabeled data in the target language to aid the alignment of crosslingual representations, i.e., via fooling a language discriminator.However, as this approach does not condition on class information, a target language example of a class could be incorrectly aligned to a source language example of a different class.To address this issue, we propose a novel crosslingual alignment method that leverages class information of REE tasks for representation learning.In particular, we propose to learn two versions of representation vectors for each class in an REE task based on either source or target language examples.Representation vectors for corresponding classes will then be aligned to achieve class-aware alignment for crosslingual representations.In addition, we propose to further align representation vectors for languageuniversal word categories (i.e., parts of speech and dependency relations).As such, a novel filtering mechanism is presented to facilitate the learning of word category representations from contextualized representations on input texts based on adversarial learning.We conduct extensive crosslingual experiments with English, Chinese, and Arabic over REE tasks.The results demonstrate the benefits of the proposed method that significantly advances the state-of-the-art performance in these settings.
Hierarchical fusion and divergent activation based weakly supervised learning for object detection from remote sensing images

Object detection and location from remote sensing (RS) images is challenging, computationally expensive, and labor intense. Benefiting from research on convolutional neural networks (CNNs), the performance in this field has improved in the recent years. However, object detection methods based on CNNs require a large number of images with annotation information for training. For object location, these annotations must contain bounding boxes. Furthermore, objects in RS images are usually small and densely co-located, leading to a high cost of manual annotation. We tackle the problem of weakly supervised object detection under such conditions, aiming to learn detectors with only image-level annotations, i.e., without bounding box annotations. Based on the fact that the feature maps of a CNN are localizable, we hierarchically fuse the location information from the shallow feature map with the class activation map to obtain accurate object locations. In order to mitigate the loss of small or densely distributed objects, we introduce a divergent activation module and a similarity module into the network. The divergent activation module is used to improve the response strength of the low-response areas in the shallow feature map. Densely distributed objects in RS images, such as aircraft in an airport, often exhibit a certain similarity. The similarity module is used to improve the feature distribution of the shallow feature map and to suppress background noise. Comprehensive experiments on a public dataset and a self-assembled dataset (which we made publicly available) show the superior performance of our method compared to state-of-the-art object detectors.
Experimental error mitigation using linear rescaling for variational quantum eigensolving with up to 20 qubits

Abstract Quantum computers have the potential to help solve a range of physics and chemistry problems, but noise in quantum hardware currently limits our ability to obtain accurate results from the execution of quantum-simulation algorithms. Various methods have been proposed to mitigate the impact of noise on variational algorithms, including several that model the noise as damping expectation values of observables. In this work, we benchmark various methods, including a new method proposed here. We compare their performance in estimating the ground-state energies of several instances of the 1D mixed-field Ising model using the variational-quantum-eigensolver algorithm with up to 20 qubits on two of IBM’s quantum computers. We find that several error-mitigation techniques allow us to recover energies to within 10% of the true values for circuits containing up to about 25 ansatz layers, where each layer consists of CNOT gates between all neighboring qubits and Y -rotations on all qubits.
Triggerless Backdoor Attack for NLP Tasks with Clean Labels

Backdoor attacks pose a new threat to NLP models.A standard strategy to construct poisoned data in backdoor attacks is to insert triggers (e.g., rare words) into selected sentences and alter the original label to a target label.This strategy comes with a severe flaw of being easily detected from both the trigger and the label perspectives: the trigger injected, which is usually a rare word, leads to an abnormal natural language expression, and thus can be easily detected by a defense model; the changed target label leads the example to be mistakenly labeled, and thus can be easily detected by manual inspections.To deal with this issue, in this paper, we propose a new strategy to perform textual backdoor attack which does not require an external trigger and the poisoned samples are correctly labeled.The core idea of the proposed strategy is to construct clean-labeled examples, whose labels are correct but can lead to test label changes when fused with the training set.To generate poisoned clean-labeled examples, we propose a sentence generation model based on the genetic algorithm to cater to the non-differentiable characteristic of text data.Extensive experiments demonstrate that the proposed attacking strategy is not only effective, but more importantly, hard to defend due to its triggerless and clean-labeled nature.Our work marks the first step towards developing triggerless attacking strategies in NLP 1 .
Application of the Extended Reality Technology for Teaching New Languages: A Systematic Review

Much attention has been given to the use of extended reality (XR) technology in educational institutions due to its flexibility, effectiveness, and attractiveness. However, there is a limited study of the application of XR technology for teaching and learning languages in schools. Thus, this paper presents a systematic review to identify the potential benefits and challenges of using XR technology for teaching new languages. This review provides a basis for adopting XR technology for teaching languages in schools. This research also provides recommendations to successfully implement the XR technology and ways to improve motivation, engagement, and enhanced accessibility of learning and teaching resources on both students and teachers. To fulfil the aims of this research, previous studies from 2011 to 2021 are collected from various academic databases. This study finds that there is still a need to develop appropriate strategies for the development and implementation of XR technology for teaching new languages to school students.
A Garbage Detection and Classification Method Based on Visual Scene Understanding in the Home Environment

Garbage classification is a social issue related to people’s livelihood and sustainable development, so letting service robots autonomously perform intelligent garbage classification has important research significance. Aiming at the problems of complex systems with data source and cloud service center data transmission delay and untimely response, at the same time, in order to realize the perception, storage, and analysis of massive multisource heterogeneous data, a garbage detection and classification method based on visual scene understanding is proposed. This method uses knowledge graphs to store and model items in the scene in the form of images, videos, texts, and other multimodal forms. The ESA attention mechanism is added to the backbone network part of the YOLOv5 network, aiming to improve the feature extraction ability of the network, combining with the built multimodal knowledge graph to form the YOLOv5-Attention-KG model, and deploying it to the service robot to perform real-time perception on the items in the scene. Finally, collaborative training is carried out on the cloud server side and deployed to the edge device side to reason and analyze the data in real time. The test results show that, compared with the original YOLOv5 model, the detection and classification accuracy of the proposed model is higher, and the real-time performance can also meet the actual use requirements. The model proposed in this paper can realize the intelligent decision-making of garbage classification for big data in the scene in a complex system and has certain conditions for promotion and landing.
Multitask Pointer Network for multi-representational parsing

Dependency and constituent trees are widely used by many artificial intelligence applications for representing the syntactic structure of human languages. Typically, these structures are separately produced by either dependency or constituent parsers. In this article, we propose a transition-based approach that, by training a single model, can efficiently parse any input sentence with both constituent and dependency trees, supporting both continuous/projective and discontinuous/non-projective syntactic structures. To that end, we develop a Pointer Network architecture with two separate task-specific decoders and a common encoder, and follow a multitask learning strategy to jointly train them. The resulting quadratic system, not only becomes the first parser that can jointly produce both unrestricted constituent and dependency trees from a single model, but also proves that both syntactic formalisms can benefit from each other during training, achieving state-of-the-art accuracies in several widely-used benchmarks such as the continuous English and Chinese Penn Treebanks, as well as the discontinuous German NEGRA and TIGER datasets.
Generalized measure of quantum Fisher information

In this work, we present a lower bound on the quantum Fisher information (QFI) which is efficiently computable on near-term quantum devices. This bound itself is of interest, as we show that it satisfies the canonical criteria of a QFI measure. Specifically, it is essentially a QFI measure for subnormalized states, and hence it generalizes the standard QFI in this sense. Our bound employs the generalized fidelity applied to a truncated state, which is constructed via the $m$ largest eigenvalues and their corresponding eigenvectors of the probe quantum state ${\ensuremath{\rho}}_{\ensuremath{\theta}}$. Focusing on unitary families of exact states, we analyze the properties of our proposed lower bound, and demonstrate its utility for efficiently estimating the QFI.
Machine translation literacy and undergraduate students in applied languages: Report on an exploratory study

This article reports on an exploratory study conducted on applied languages undergraduate students’ use of machine translation. Starting from the observation that they make extensive use of free tools available online, our aim was to understand whether they are capable of identifying and correcting machine translation errors, and if so, to what extent.
A New Method to Compare the Interpretability of Rule-Based Algorithms

Interpretability is becoming increasingly important for predictive model analysis. Unfortunately, as remarked by many authors, there is still no consensus regarding this notion. The goal of this paper is to propose the definition of a score that allows for quickly comparing interpretable algorithms. This definition consists of three terms, each one being quantitatively measured with a simple formula: predictivity, stability and simplicity. While predictivity has been extensively studied to measure the accuracy of predictive algorithms, stability is based on the Dice-Sorensen index for comparing two rule sets generated by an algorithm using two independent samples. The simplicity is based on the sum of the lengths of the rules derived from the predictive model. The proposed score is a weighted sum of the three terms mentioned above. We use this score to compare the interpretability of a set of rule-based algorithms and tree-based algorithms for the regression case and for the classification case.