Reviewerly/exHarmony huggingface dataset:
ConvGQR: Generative Query Reformulation for Conversational Search

In conversational search, the user's real search intent for the current conversation turn is dependent on the previous conversation history.It is challenging to determine a good search query from the whole conversation context.To avoid the expensive re-training of the query encoder, most existing methods try to learn a rewriting model to de-contextualize the current query by mimicking the manual query rewriting.However, manually rewritten queries are not always the best search queries.Thus, training a rewriting model on them would lead to sub-optimal queries.Another useful information to enhance the search query is the potential answer to the question.In this paper, we propose ConvGQR, a new framework to reformulate conversational queries based on generative pre-trained language models (PLMs), one for query rewriting and another for generating potential answers.By combining both, ConvGQR can produce better search queries.In addition, to relate query reformulation to the retrieval task, we propose a knowledge infusion mechanism to optimize both query reformulation and retrieval.Extensive experiments on four conversational search datasets demonstrate the effectiveness of ConvGQR.
FiDO: Fusion-in-Decoder optimized for stronger performance and faster inference

Fusion-in-Decoder (FiD) is a powerful retrieval-augmented language model that sets the state-of-the-art on many knowledgeintensive NLP tasks.However, the architecture used for FiD was chosen by making minimal modifications to a standard T5 model, which our analysis shows to be highly suboptimal for a retrieval-augmented model.In particular, FiD allocates the bulk of FLOPs to the encoder, while the majority of inference time results from memory bandwidth constraints in the decoder.We propose two simple changes to the FiD architecture to alleviate memory bandwidth constraints, and speed up inference by 7x.This allows us to use a much larger decoder at modest cost.We denote FiD with the above modifications as FiDO, and show that it strongly improves performance over existing FiD models for a wide range of inference budgets.For example, FiDO-Large-XXL performs faster inference than FiD-Base and achieves better performance than FiD-Large.
Language Agnostic Multilingual Information Retrieval with Contrastive Learning

Multilingual information retrieval (IR) is challenging since annotated training data is costly to obtain in many languages.We present an effective method to train multilingual IR systems when only English IR training data and some parallel corpora between English and other languages are available.We leverage parallel and non-parallel corpora to improve the pretrained multilingual language models' cross-lingual transfer ability.We design a semantic contrastive loss to align representations of parallel sentences that share the same semantics in different languages, and a new language contrastive loss to leverage parallel sentence pairs to remove language-specific information in sentence representations from non-parallel corpora.When trained on English IR data with these losses and evaluated zero-shot on non-English data, our model demonstrates significant improvement to prior work on retrieval performance, while it requires much less computational effort.We also demonstrate the value of our model for a practical setting when a parallel corpus is only available for a few languages, but a lack of parallel corpora resources persists for many other low-resource languages.Our model can work well even with a small number of parallel sentences, and be used as an add-on module to any backbones and other tasks.
RuCoLA: Russian Corpus of Linguistic Acceptability

Linguistic acceptability (LA) attracts the attention of the research community due to its many uses, such as testing the grammatical knowledge of language models and filtering implausible texts with acceptability classifiers.However, the application scope of LA in languages other than English is limited due to the lack of high-quality resources.To this end, we introduce the Russian Corpus of Linguistic Acceptability (RuCoLA), built from the ground up under the well-established binary LA approach.RuCoLA consists of 9.8k indomain sentences from linguistic publications and 3.6k out-of-domain sentences produced by generative models.The out-of-domain set is created to facilitate the practical use of acceptability for improving language generation.Our paper describes the data collection protocol and presents a fine-grained analysis of acceptability classification experiments with a range of baseline approaches.In particular, we demonstrate that the most widely used language models still fall behind humans by a large margin, especially when detecting morphological and semantic errors.We release RuCoLA, the code of experiments, and a public leaderboard 1 to assess the linguistic competence of language models for Russian.
ConsistTL: Modeling Consistency in Transfer Learning for Low-Resource Neural Machine Translation

Transfer learning is a simple and powerful method that can be used to boost model performance of low-resource neural machine translation (NMT).Existing transfer learning methods for NMT are static, which simply transfer knowledge from a parent model to a child model once via parameter initialization.In this paper, we propose a novel transfer learning method for NMT, namely ConsistTL, which can continuously transfer knowledge from the parent model during the training of the child model.Specifically, for each training instance of the child model, ConsistTL constructs the semantically-equivalent instance for the parent model and encourages prediction consistency between the parent and child for this instance, which is equivalent to the child model learning each instance under the guidance of the parent model.Experimental results on five low-resource NMT tasks demonstrate that ConsistTL results in significant improvements over strong transfer learning baselines, with a gain up to 1.7 BLEU over the existing backtranslation model on the widely-used WMT17 Turkish-English benchmark.Further analysis reveals that ConsistTL can improve the inference calibration of the child model.Code and scripts are freely available at https://github.com/NLP2CT/ConsistTL.
Assist Non-native Viewers: Multimodal Cross-Lingual Summarization for How2 Videos

Multimodal summarization for videos aims to generate summaries from multi-source information (videos, audio transcripts), which has achieved promising progress.However, existing works are restricted to monolingual video scenarios, ignoring the demands of non-native video viewers to understand the cross-language videos in practical applications.It stimulates us to propose a new task, named Multimodal Cross-Lingual Summarization for videos (MCLS), which aims to generate cross-lingual summaries from multimodal inputs of videos.First, to make it applicable to MCLS scenarios, we conduct a Video-guided Dual Fusion network (VDF) that integrates multimodal and cross-lingual information via diverse fusion strategies at both encoder and decoder.Moreover, to alleviate the problem of high annotation costs and limited resources in MCLS, we propose a triple-stage training framework to assist MCLS by transferring the knowledge from monolingual multimodal summarization data, which includes: 1) multimodal summarization on sufficient prevalent language videos with a VDF model; 2) knowledge distillation (KD) guided adjustment on bilingual transcripts; 3) multimodal summarization for cross-lingual videos with a KD induced VDF model.Experiment results on the reorganized How2 dataset show that the VDF model alone outperforms previous methods for multimodal summarization, and the performance further improves by a large margin via the proposed triple-stage training framework.
AraBART: a Pretrained Arabic Sequence-to-Sequence Model for Abstractive Summarization

Like most natural language understanding and generation tasks, state-of-the-art models for summarization are transformer-based sequenceto-sequence architectures that are pretrained on large corpora.While most existing models focus on English, Arabic remains understudied.In this paper we propose AraBART, the first Arabic model in which the encoder and the decoder are pretrained end-to-end, based on BART (Lewis et al., 2020).We show that AraBART achieves the best performance on multiple abstractive summarization datasets, outperforming strong baselines including a pretrained Arabic BERT-based model, multilingual BART, Arabic T5, and a multilingual T5 model.AraBART is publicly available on github 1 and the Hugging Face model hub 2 .
Detect-Localize-Repair: A Unified Framework for Learning to Debug with CodeT5

Automated software debugging is a crucial task for improving the productivity of software developers.Many neural-based techniques have been proven effective for debugging-related tasks such as bug localization and program repair (or bug fixing).However, these techniques often focus only on either one of them or approach them in a stage-wise manner, ignoring the mutual benefits between them.In this work, we propose a novel unified Detect-Localize-Repair framework based on a pretrained programming language model CodeT5 to seamlessly address these tasks, named CodeT5-DLR.Specifically, we propose three objectives to adapt the generic CodeT5 for debugging: a bug detection objective to determine whether a given code snippet is buggy or not, a bug localization objective to identify the buggy lines, and a program repair objective to translate the buggy code to its fixed version.We evaluate it on each of these tasks and their combined setting on two newly collected line-level debugging datasets in Java and Python.Extensive results show that our model significantly outperforms existing baselines from both NLP and software engineering domains.
Are Neural Topic Models Broken?

Recently, the relationship between automated and human evaluation of topic models has been called into question.Method developers have staked the efficacy of new topic model variants on automated measures, and their failure to approximate human preferences places these models on uncertain ground.Moreover, existing evaluation paradigms are often divorced from real-world use.Motivated by content analysis as a dominant real-world use case for topic modeling, we analyze two related aspects of topic models that affect their effectiveness and trustworthiness in practice for that purpose: the stability of their estimates and the extent to which the model's discovered categories align with human-determined categories in the data.We find that neural topic models fare worse in both respects compared to an established classical method.We take a step toward addressing both issues in tandem by demonstrating that a straightforward ensembling method can reliably outperform the members of the ensemble.
Learning Unified Hyper-Network for Multi-Modal MR Image Synthesis and Tumor Segmentation With Missing Modalities

Accurate segmentation of brain tumors is of critical importance in clinical assessment and treatment planning, which requires multiple MR modalities providing complementary information. However, due to practical limits, one or more modalities may be missing in real scenarios. To tackle this problem, existing methods need to train multiple networks or a unified but fixed network for various possible missing modality cases, which leads to high computational burdens or sub-optimal performance. In this paper, we propose a unified and adaptive multi-modal MR image synthesis method, and further apply it to tumor segmentation with missing modalities. Based on the decomposition of multi-modal MR images into common and modality-specific features, we design a shared hyper-encoder for embedding each available modality into the feature space, a graph-attention-based fusion block to aggregate the features of available modalities to the fused features, and a shared hyper-decoder for image reconstruction. We also propose an adversarial common feature constraint to enforce the fused features to be in a common space. As for missing modality segmentation, we first conduct the feature-level and image-level completion using our synthesis method and then segment the tumors based on the completed MR images together with the extracted common features. Moreover, we design a hypernet-based modulation module to adaptively utilize the real and synthetic modalities. Experimental results suggest that our method can not only synthesize reasonable multi-modal MR images, but also achieve state-of-the-art performance on brain tumor segmentation with missing modalities.
A new role for translators and trainers: MT literacy consultants

Recent developments in machine translation (MT) might have led some people to believe that soon professional translation will not be needed, but most translator trainers are aware of the high demand for the quality that MT systems cannot deliver without human intervention. It is thus important that professional translators, trainers and their students appreciate when and how MT can best be deployed, even if they do not use it much themselves. This can be accomplished by enhancing their MT literacy, which encompasses an understanding of the basics, risks and benefits of the technology. Trainers can prepare their students to provide advice to clients who might be interested in using MT for their multilingual content but do not have the expertise to judge when it would be enough to meet their needs. Drawing on the example of knowledge dissemination in higher education, this article presents survey results that suggest MT is being used far more widely than previously assumed. We highlight some of the risks associated with uninformed use of this technology, discuss how they can be mitigated by translation professionals with consulting competence, and outline some training scenarios which could contribute to developing societal AI literacy in general.
Integration of Deep Learning for Automatic Recognition of 2D Engineering Drawings

In an environment where manufacturing precision requirements are increasing, complete project plans can consist of hundreds of engineering drawings. The presentation of these drawings often varies based on personal preferences, leading to inconsistencies in format and symbols. The lack of standardization in these aspects can result in inconsistent interpretations during subsequent analysis. Therefore, proper annotation of engineering drawings is crucial as it determines product quality, subsequent inspections, and processing costs. To reduce the time and cost associated with interpreting and analyzing drawings, as well as to minimize human errors in judgment, we developed an engineering drawing recognition system. This study employs geometric dimensioning and tolerancing (GD&amp;T) in accordance with the ASME (American Society of Mechanical Engineers) Y14.5 2018 specification to describe the language of engineering drawings. Additionally, PyTorch, OpenCV, and You Only Look Once (YOLO) are utilized for training. Existing 2D engineering drawings serve as the training data, and image segmentation is performed to identify objects such as dimensions, tolerances, functional frames, and geometric symbols in the drawings using the network model. By reading the coordinates corresponding to each object, the correct values are displayed. Real-world cases are utilized to train the model with multiple engineering drawings containing mixed features, resulting in recognition capabilities surpassing those of single-feature identification. This approach improves the recognition accuracy of deep learning models and makes engineering drawing and image recognition more practical. The recognition results are directly stored in a database, reducing product verification time and preventing errors that may occur due to manual data entry, thereby avoiding subsequent quality control issues. The accuracy rates achieved are as follows: 85% accuracy in detecting views in 2D engineering drawings, 70% accuracy in detecting annotation groups and annotations, and 80% accuracy in text and symbol recognition.
Intrinsic randomness under general quantum measurements

Quantum measurements can produce unpredictable randomness arising from the uncertainty principle. When measuring a state with von Neumann measurements, the intrinsic randomness can be quantified by the quantum coherence of the state on the measurement basis. Unlike projection measurements, there are additional and possibly hidden degrees of freedom in an apparatus for generic measurements. We propose an adversary scenario to characterize the intrinsic randomness of general measurements with arbitrary input states. Interestingly, we discover that certain measurements, including symmetric and information-complete ones, generate nonzero randomness for all states, which suggests a new approach for designing source-independent random number generators without state characterization. Furthermore, our results reveal that intrinsic randomness can quantify coherence under general measurements, which generalizes the result in the standard resource theory of state coherence.
An analysis of the use of augmented reality and virtual reality as educational resources

Abstract In recent years, the utilization of augmented reality (AR) and virtual reality (VR) has emerged as a transformative approach in education, revolutionizing traditional teaching methods. This study seeks to explore the efficacy of AR and VR as pedagogical resources for enhancing the teaching of the solar system. The research process involved the development of an application comprising two modules, AR and VR, which were evaluated to assess their impact on the teaching process. Furthermore, a comparative study was conducted to evaluate the immersiveness, interactivity, and ease of use offered by these technologies. The findings demonstrate that both AR and VR demonstrate promise in supporting the teaching process, with the VR module garnering particularly positive evaluations. However, it is crucial to acknowledge existing barriers in underprivileged communities, where public schools face limited investments in technology infrastructure. These limitations hinder the widespread implementation of such immersive experiences and their potential to foster new knowledge acquisition.
Inshore Ship Detection Based on Multi-Modality Saliency for Synthetic Aperture Radar Images

Synthetic aperture radar (SAR) ship detection is of significant importance in military and commercial applications. However, a high similarity in intensity and spatial distribution of scattering characteristics between the ship target and harbor facilities, along with a fuzzy sea-land boundary due to the strong speckle noise, result in a low detection accuracy and high false alarm rate for SAR ship detection with complex inshore scenes. In this paper, a new inshore ship detection method based on multi-modality saliency is proposed to overcome these challenges. Four saliency maps are established from different perspectives: an ocean-buffer saliency map (OBSM) outlining more accurate coastline under speckle noises; a local stability saliency map (LSSM) addressing pixel spatial distribution; a super-pixel saliency map (SPSM) extracting critical region-based features for inshore ship detection; and an intensity saliency map (ISM) to highlight target pixels with intensity distribution. By combining these saliency maps, ship targets in complex inshore scenes can be successfully detected. The method provides a novel interdisciplinary perspective (surface metrology) for SAR image segmentation, discovers the difference in spatial characteristics of SAR image elements, and proposes a novel robust CFAR procedure for background clutter fitting. Experiments on a public SAR ship detection dataset (SSDD) shows that our method achieves excellent detection performance, with a low false alarm rate, in offshore scenes, inshore scenes, inshore scenes with confusing metallic port facilities, and large-scale scenes. The results outperform several widely used methods, such as CFAR-based methods and super-pixel methods.
Identifying underlying individuality across running, walking, and handwriting patterns with conditional cycle–consistent generative adversarial networks

In recent years, the analysis of movement patterns has increasingly focused on the individuality of movements. After long speculations about weak individuality, strong individuality is now accepted, and the first situation–dependent fine structures within it are already identified. Methodologically, however, only signals of the same movements have been compared so far. The goal of this work is to detect cross-movement commonalities of individual walking, running, and handwriting patterns using data augmentation. A total of 17 healthy adults (35.8 ± 11.1 years, eight women and nine men) each performed 627.9 ± 129.0 walking strides, 962.9 ± 182.0 running strides, and 59.25 ± 1.8 handwritings. Using the conditional cycle-consistent generative adversarial network (CycleGAN), conditioned on the participant’s class, a pairwise transformation between the vertical ground reaction force during walking and running and the vertical pen pressure during handwriting was learned in the first step. In the second step, the original data of the respective movements were used to artificially generate the other movement data. In the third step, whether the artificially generated data could be correctly assigned to a person via classification using a support vector machine trained with original data of the movement was tested. The classification F1–score ranged from 46.8% for handwriting data generated from walking data to 98.9% for walking data generated from running data. Thus, cross–movement individual patterns could be identified. Therefore, the methodology presented in this study may help to enable cross–movement analysis and the artificial generation of larger amounts of data.
Effects of #coronavirus content moderation on misinformation and anti-Asian hate on Instagram

This study evaluated the intended and unintended effects of Instagram’s content moderation on #coronavirus for both the short- and long-term effects on misinformation and anti-Asian sentiment. We performed manual coding of images ( N = 9648), and a series of supervised machine learning methods to classify three waves of comments ( N = 22,676) published in 2020 on Instagram. Welch’s F tests were used to compare misinformation, emotions, toxicity, and identity attack across three time periods. The results showed that hashtag moderation had an intended effect in reducing misinformation, and an unintended effect in reducing anger, fear, toxicity, and identity attack. Images with people of East Asian descent were associated with more anger, fear, toxicity, and identity attack than images with people of other races. Prior to content moderation, misinformation was associated with identity attack. Stigmatization on social media, and content moderation of misinformation and hate speech are discussed.
Density kernel depth for outlier detection in functional data

Abstract In this paper, we propose a novel approach to address the problem of functional outlier detection. Our method leverages a low-dimensional and stable representation of functions using Reproducing Kernel Hilbert Spaces (RKHS). We define a depth measure based on density kernels that satisfy desirable properties. We also address the challenges associated with estimating the density kernel depth. Throughout a Monte Carlo simulation we assess the performance of our functional depth measure in the outlier detection task under different scenarios. To illustrate the effectiveness of our method, we showcase the proposed method in action studying outliers in mortality rate curves.