Reviewerly/exHarmony huggingface dataset:
A new media content trusted dissemination architecture based on AV-blockchain and ChinaDRM

The diffusion of all-media content plays a vital role in guiding public opinion and ideology.However, at present, most of the media content exists on all kinds of mainstream media platforms, which poses great challenges to the effective supervision of relevant departments and society.This has led to arbitrary charges, chaotic media content, difficulties in supervision and evidence collection, and infringements of the rights and interests of original content creators.To address these problems, this paper constructs a trustworthy propagation architecture that supports multiplatform media content sharing.This architecture collaboratively builds an audio-visual blockchain through public and consortium blockchains, coupled with an improved ChinaDRM to provide digital rights management and content encryption.Simultaneously, we employ an enhanced Diffie-Hellman key agreement protocol to offer distributed encryption and decryption for media content.Within this model, various media platforms and national regulatory authorities are responsible for content storage and distribution as consortium nodes and public blockchain nodes, respectively.At the same time, users, as light nodes of public chain or service consumers of consortium blockchain, can consume and comment on content.Analysis shows that the trusted communication framework of media content based on the audio-visual blockchain has certain expansibility and practicability.It can facilitate the supervision of mainstream media platforms by national authorities and society through inter-blockchain technology, offering a novel solution for multi-platform trustworthy cooperative information sharing.
History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System

With the evolution of pre-trained language models, current open-domain dialogue systems have achieved great progress in conducting one-session conversations.In contrast, Multi-Session Conversation (MSC), which consists of multiple sessions over a long term with the same user, is under-investigated.In this paper, we propose History-Aware Hierarchical Transformer (HAHT) for multi-session opendomain dialogue.HAHT maintains a long-term memory of history conversations and utilizes history information to understand current conversation context and generate well-informed and context-relevant responses.Specifically, HAHT first encodes history conversation sessions hierarchically into a history memory.Then, HAHT leverages historical information to facilitate the understanding of the current conversation context by encoding the history memory together with the current context with attention-based mechanisms.Finally, to explicitly utilize historical information, HAHT uses a history-aware response generator that switches between a generic vocabulary and a historyaware vocabulary.Experimental results on a large-scale MSC dataset suggest that the proposed HAHT model consistently outperforms baseline models.Human evaluation results support that HAHT generates more human-like, context-relevant and history-relevant responses than baseline models.Session 1 Conversation User My cat Sonny is my favorite.He likes watching TV with me.
Are Abstractive Summarization Models truly ‘Abstractive’? An Empirical Study to Compare the two Forms of Summarization

Automatic Text Summarization has seen a large paradigm shift from extractive methods to abstractive (or generation-based) methods in the last few years.This can be attributed to the availability of large autoregressive language models (Lewis et al., 2019; Zhang et al., 2019a) that have been shown to outperform extractive methods.In this work, we revisit extractive methods and study their performance against state of the art(SOTA) abstractive models.Through extensive studies, we notice that abstractive methods are not yet completely abstractive in their generated summaries.In addition to this finding, we propose an evaluation metric that could benefit the summarization research community to measure the degree of abstractiveness of a summary in comparison to their extractive counterparts.To confirm the generalizability of our findings, we conduct experiments on two summarization datasets using five powerful techniques in extractive and abstractive summarization and study their levels of abstraction.
UOA at the FinNLP-2022 ERAI Task: Leveraging the Class Label Description for Financial Opinion Mining

Evaluating the Rationales of Amateur Investors (ERAI) is a task about mining expert-like viewpoints from social media.This paper summarizes our solutions to the ERAI shared task, which is co-located with the FinNLP workshop at EMNLP 2022.There are 2 sub-tasks in ERAI.Sub-task 1 is a pair-wised comparison task, where we propose a BERT-based pre-trained model projecting opinion pairs in a common space for classification.Sub-task 2 is an unsupervised learning task ranking the opinions' maximal potential profit (MPP) and maximal loss (ML), where our model leverages the regression method and multi-layer perceptron to rank the MPP and ML values.The proposed approaches achieve competitive accuracy of 54.02% on ML Accuracy and 51.72% on MPP Accuracy for pairwise tasks, also 12.35% and -9.39% regression unsupervised ranking task for MPP and ML.
Data Augmentation for Low-Resource Keyphrase Generation

Keyphrase generation is the task of summarizing the contents of any given article into a few salient phrases (or keyphrases). Existing works for the task mostly rely on large-scale annotated datasets, which are not easy to acquire. Very few works address the problem of keyphrase generation in low-resource settings, but they still rely on a lot of additional unlabeled data for pretraining and on automatic methods for pseudo-annotations. In this paper, we present data augmentation strategies specifically to address keyphrase generation in purely resource-constrained domains. We design techniques that use the full text of the articles to improve both present and absent keyphrase generation. We test our approach comprehensively on three datasets and show that the data augmentation strategies consistently improve the state-of-the-art performance. We release our source code at https://github.com/kgarg8/kpgen-lowres-data-aug.
Collaborative Reasoning on Multi-Modal Semantic Graphs for Video-Grounded Dialogue Generation

We study video-grounded dialogue generation, where a response is generated based on the dialogue context and the associated video.The primary challenges of this task lie in (1) the difficulty of integrating video data into pre-trained language models (PLMs) which presents obstacles to exploiting the power of large-scale pre-training; and (2) the necessity of taking into account the complementarity of various modalities throughout the reasoning process.Although having made remarkable progress in video-grounded dialogue generation, existing methods still fall short when it comes to integrating with PLMs in a way that allows information from different modalities to complement each other.To alleviate these issues, we first propose extracting pertinent information from videos and turning it into reasoning paths that are acceptable to PLMs.Additionally, we propose a multi-agent reinforcement learning method to collaboratively perform reasoning on different modalities (i.e., video and dialogue context).Empirical experiment results on two public datasets indicate that the proposed model can significantly outperform state-of-the-art models by large margins on both automatic and human evaluations.
Semantic Dependency Parsing with Edge GNNs

Second-order neural parsers have obtained high accuracy in semantic dependency parsing.Inspired by the factor graph representation of second-order parsing, we propose edge graph neural networks (E-GNNs).In an E-GNN, each node corresponds to a dependency edge, and the neighbors are defined in terms of sibling, co-parent, and grandparent relationships.We conduct experiments on SemEval 2015 Task 18 English datasets, showing the superior performance of E-GNNs 1 .
Improved Knowledge Distillation for Pre-trained Language Models via Knowledge Selection

Knowledge distillation addresses the problem of transferring knowledge from a teacher model to a student model.In this process, we typically have multiple types of knowledge extracted from the teacher model.The problem is to make full use of them to train the student model.Our preliminary study shows that: (1) not all of the knowledge is necessary for learning a good student model, and (2) knowledge distillation can benefit from certain knowledge at different training steps.In response to these, we propose an actor-critic approach to selecting appropriate knowledge to transfer during the process of knowledge distillation.In addition, we offer a refinement of the training algorithm to ease the computational burden.Experimental results on the GLUE datasets show that our method outperforms several strong knowledge distillation baselines significantly.
Transfer learning for multilingual vacancy text generation

Writing job vacancies can be a repetitive and expensive task for humans.This research focuses on automatically generating parts of vacancy texts, i.e., the benefits section, given structured job attributes as input using mT5, the multilingual version of the state-of-the-art T5 transformer model.While transformers are accurate at generating coherent text, they can struggle with correctly including structured (input) data in the generated text.Including this input data correctly is crucial for vacancy text generation; otherwise, job seekers may be misled.To evaluate how the model includes the different types of structured input, we propose a novel domain-specific metric: 'input generation accuracy'.Our metric aims to address the shortcomings of Relation Generation, a commonly used evaluation metric for data-to-text generation that relies on string matching, as our task includes evaluating generated texts based on binary and categorical inputs.Using our novel evaluation method, we measure how well the input is included in the generated text separately for different types of inputs (binary, categorical, numeric), offering another contribution to the field.In addition, we evaluate how accurately the mT5 model generates texts in the requested languages.Our experiments show that mT5 is highly accurate at generating texts in the correct (requested) languages, and at handling seen categorical and binary inputs correctly.However, mT5 performed worse when generating text from unseen city names or working with numeric inputs.
LogicNMR: Probing the Non-monotonic Reasoning Ability of Pre-trained Language Models

The logical reasoning capabilities of pretrained language models have recently received much attention.As one of the vital reasoning paradigms, non-monotonic reasoning refers to the fact that conclusions may be invalidated with new information.Existing work has constructed a nonmonotonic inference dataset δ-NLI and explored the performance of language models on it.However, the δ-NLI dataset is entangled with commonsense reasoning.In this paper, we explore the pure non-monotonic reasoning ability of pre-trained language models.We build a non-monotonic reasoning benchmark, named LogicNMR, with explicit default rules and iterative updates.In the experimental part, the performance of popular language models on LogicNMR is explored from the perspectives of accuracy, generalization, proof-based traceability and robustness.The experimental results show that even though the fine-tuned language models achieve an accuracy of more than 94.4% on LogicNMR, they perform unsatisfactorily, with a significant drop, in generalization and proof-based traceability.
Keyphrase Generation via Soft and Hard Semantic Corrections

Keyphrase generation aims to generate a set of condensed phrases given a source document.Although maximum likelihood estimation (MLE) based keyphrase generation methods have shown impressive performance, they suffer from the bias on the source-prediction pair and the bias on the prediction-target pair.To tackle the above biases, we propose a novel correction model CorrKG on top of the MLE pipeline, where the biases are corrected via the optimal transport (OT) and a frequency-based filtering-and-sorting (FreqFS) strategy.Specifically, OT is introduced as the soft correction to facilitate the alignment of salient information and rectify the semantic bias on the source document and predicted keyphrases pair.An adaptive semantic mass learning scheme is conducted on the vanilla OT to achieve a proper pair-wise optimal transport procedure, which promotes the OT calculation brought by rectifying semantic masses dynamically.Besides, the FreqFS strategy is designed as the hard correction to reduce the bias of predicted and target keyphrases, and thus generate accurate and sufficient keyphrases.Extensive experiments over multiple benchmark datasets show that our model achieves superior keyphrase generation as compared with the state-of-the-arts. IntroductionKeyphrase generation is an important and meaningful task that converts the main semantic information of the document into multiple keyphrases.Keyphrases can further be divided into present keyphrases and absent keyphrases, with the former appearing in the document whereas the latter do not.High-quality keyphrases are beneficial for many downstream tasks, such as text summarization (Wang and Cardie, 2013), document clustering (Hammouda et al., 2005), translation (Tang et al., 2016), and so forth.Despite the promising suc-* The first two authors contribute equally to this work.
A Taxonomical NLP Blueprint to Support Financial Decision Making through Information-Centred Interactions

Investment management professionals (IMPs) often make decisions after manual analysis of text transcripts of central banks' conferences or companies' earning calls.Their current software tools, while interactive, largely leave users unassisted in using these transcripts.A key component to designing speech and NLP techniques for this community is to qualitatively characterize their perceptions of AI as well as their legitimate needs so as to (1) better apply existing NLP methods, (2) direct future research and (3) correct IMPs' perceptions of what AI is capable of.This paper presents such a study, through a contextual inquiry with eleven IMPs, uncovering their information practices when using such transcripts.We then propose a taxonomy of user requirements and usability criteria to support IMP decision making, and validate the taxonomy through participatory design workshops with four IMPs.Our investigation suggests that: (1) IMPs view visualization methods and natural language processing algorithms primarily as time-saving tools that are incapable of enhancing either discovery or interpretation and (2) their existing software falls well short of the state of the art in both visualization and NLP.
Bloom Library: Multimodal Datasets in 300+ Languages for a Variety of Downstream Tasks

We present Bloom Library, a linguistically diverse set of multimodal and multilingual datasets for language modeling, image captioning, visual storytelling, and speech synthesis/recognition.These datasets represent either the most, or among the most, multilingual datasets for each of the included downstream tasks.In total, the initial release of the Bloom Library datasets covers 363 languages across 32 language families.We train downstream task models for various languages represented in the data, showing the viability of the data for future work in low-resource, multimodal NLP and establishing the first known baselines for these downstream tasks in certain languages (e.g., Bisu [bzi], with an estimated population of 700 users).Some of these first-of-their-kind baselines are comparable to state-of-the-art performance for higher-resourced languages.The Bloom Library datasets are released under Creative Commons licenses on the Hugging Face datasets hub to catalyze more linguistically diverse research in the included downstream tasks.
RoChBert: Towards Robust BERT Fine-tuning for Chinese

Despite of the superb performance on a wide range of tasks, pre-trained language models (e.g., BERT) have been proved vulnerable to adversarial texts.In this paper, we present RoChBert, a framework to build more Robust BERT-based models by utilizing a more comprehensive adversarial graph to fuse Chinese phonetic and glyph features into pretrained representations during fine-tuning.Inspired by curriculum learning, we further propose to augment the training dataset with adversarial texts in combination with intermediate samples.Extensive experiments demonstrate that RoChBert outperforms previous methods in significant ways: (i) robust -RoChBert greatly improves the model robustness without sacrificing accuracy on benign texts.Specifically, the defense lowers the success rates of unlimited and limited attacks by 59.43% and 39.33% respectively, while remaining accuracy of 93.30%; (ii) flexible -RoChBert can easily extend to various language models to solve different downstream tasks with excellent performance; and (iii) efficient -RoChBert can be directly applied to the fine-tuning stage without pre-training language model from scratch, and the proposed data augmentation method is also low-cost.
KiL 2023 : 3rd International Workshop on Knowledge-infused Learning

Recent prolific advances in artificial intelligence through the incorporation of domain knowledge have constituted a new paradigm for AI and data mining communities. For example, the human feedback-based language generation in ChatGPT (a large language model (LLM)), the use of Protein Bank in DeepMind's AlphaFold, and the use of 23 rules of safety in DeepMind's Sparrow have demonstrated the success of teaming human knowledge and AI. In addition, the knowledge retrieval-guided language modeling methods have strengthened the association between knowledge and AI. However, translating research methods and resources into practice presents a new challenge for the machine learning and data/knowledge mining communities. For example, in DARPA's Explainable AI seminar, the need for explainable contextual adaptation is seen as the 3rd phase of AI, facilitating the interplay between data and knowledge for explainability, safety, and, eventually, trust. However, policymakers and practitioners assert serious usability and privacy concerns that constrain adoption, notably in high-consequence domains, such as cybersecurity, healthcare, and other social good domains. In addition, limitations in output quality, measurement, and interactive ability, including both the provision of explanations and the acceptance of user preferences, result in low adoption rates in such domains. This workshop aims to accelerate our pace towards creating innovative methods for integrating knowledge into contemporary AI and data science methods and develop metrics for assessing performance in various applications.
CONSISTENT: Open-Ended Question Generation From News Articles

Recent work on question generation has largely focused on factoid questions such as who, what, where, when about basic facts.Generating open-ended why, how, what, etc. questions that require long-form answers have proven more difficult.To facilitate the generation of openended questions, we propose CONSISTENT, a new end-to-end system for generating openended questions that are answerable from and faithful to the input text.Using news articles as a trustworthy foundation for experimentation, we demonstrate our model's strength over several baselines using both automatic and humanbased evaluations.We contribute an evaluation dataset of expert-generated open-ended questions.We discuss potential downstream applications for news media organizations.
Improved YOLOv5 with Backbone Replacement to MobileNet V3s for School Attribute Detection

School attributes are a series of clothes and accessories that must be worn by students in the school environment. The implementation of this rule aims to create discipline in students. However, in practice, not all rules can be implemented properly because there are still students who violate these rules. One of the rules applied at school is the use of complete attributes. Currently, attribute checks in schools are done manually or through teacher supervision. However, this takes more time, is prone to errors, and is inefficient due to the large number of students being checked. This study proposes an improved YOLOv5 architecture with the replacement of the backbone to MobileNetV3s to detect school attributes. This method uses deep learning and the YOLOv5 algorithm to detect in real time the use of school attributes by students. In this study, the experimental results show that the enhanced YOLOv5 with MobileNetV3s has higher accuracy compared to the original YOLOv5. In addition, the improved model is more efficient in memory usage and weight file size. With an accuracy result of 0.912 on mAP50 and a weight size of about 90 MB and a memory usage of &lt;7 GB, it shows the potential of replacing the backbone in this technology in overcoming attribute detection challenges in schools and can be applied in other cases. However, further research is needed to generalize these results to other problems. This research also shows that backbone replacement in YOLOv5 can affect the accuracy of the model.
Chicken Disease Classification Based on Inception V3 Algorithm for Data Imbalance

In order to supply the world's protein needs, one of the most crucial industries is the poultry business. The problem that often occurs in chicken farms is disease, and this can have a significant impact on the farm. The availability of large enough amounts of data makes it possible to carry out the process of monitoring chicken diseases using deep learning technology for the classification of chicken diseases. With the availability of large enough data, the dataset has a variety of features that cause problems with data clutter. To overcome the problem of data conflict, an oversampling technique is used to increase the sample data from the minority class so that it has the same value as the other majority classes, and the Inception-V3 algorithm is used to classify chicken diseases based on fecal images. The total number of data used was 8067, which were broken down into the following four categories: Healthy, Salmonella, Coccidiosis, and Newcastle disease. Data balancing was done using oversampling to get the total data to 10500 before the evaluation process was started. The data was distributed by splitting it by 80% of the data will be used for training, 10% for data validation, and 10% for testing. The results of the test, which employed Inception V3 without oversampling, produced the highest possible score of 94.05%.