Reviewerly/exHarmony huggingface dataset:
Using Character-Level Sequence-to-Sequence Model for Word Level Text Generation to Enhance Arabic Speech Recognition

Owing to the linguistic richness of the Arabic language, which contains more than 6000 roots, building a reliable Arabic language model for Arabic speech recognition systems faces many challenges.This paper introduces a language model free Arabic automatic speech recognition system for Modern Standard Arabic based on an end-to-end-based Deep Speech architecture developed by Mozilla.The proposed model uses a character-level sequence-to-sequence model to map the character alignment produced by the recognizer model onto the corresponding words.The developed system outperformed recent studies on single-speaker and multi-speaker Arabic speech recognition using two different state-ofthe-art datasets.The first was the Arabic Multi-Genre Broadcast (MGB2) corpus with 1200 h of audio data from multiple speakers.The system achieved a new milestone in the MGB2 challenge with a word error rate (WER) of 3.2, outperforming related work using the same corpus with a word error reduction of 17%.An additional experiment with a 7-hour Saudi Accent Single Speaker Corpus (SASSC) was used to build an additional model for single male speaker-based Arabic speech recognition using the same proposed network architecture.The single-speaker model outperformed related experiments with a WER of 4.25 with a relative improvement of 33.8%.
Distance Information Improves Heterogeneous Graph Neural Networks

Heterogeneous graph neural network (HGNN) has shown superior performance and attracted considerable research interest. However, HGNN inherits the limitation of expressive power from GNN via learning <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$individual$</tex-math></inline-formula> node embeddings based on their structural neighbors, largely ignoring the potential correlations between nodes and leading to sub-optimal performance. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">How to establish correlations among multiple node embeddings and improve the expressive power of HGNN is still an open problem.</i> To solve the above problem, we propose a simple and effective technique called heterogeneous distance encoding (HDE) to fundamentally improve the expressive power of HGNN. Specifically, we define heterogeneous shortest path distance to describe the relative distance between nodes, and then jointly encode such distances for multiple nodes of interest to establish their correlation. By simply injecting the encoded correlation into the neighbor aggregating process, we can learn more expressive heterogeneous graph representations for downstream tasks. More importantly, the proposed HDE relies only on the graph structure and ensures the inductive ability of HGNN. We also propose an efficient HDE algorithm that can significantly reduce the computational overhead. Significant improvements on both transductive and inductive tasks over four real-world graphs demonstrate the effectiveness of HDE in improving the expressive power of HGNN.
CoMod: An Abstractive Approach to Discourse Context Identification

Generative text summarization can condense large volumes of information into a concise summary. It helps users quickly grasp the main points of a text without having to read the entire document. Machine learning (ML) plays a pivotal role in this domain, offering significant advantages in information processing and comprehension. In this paper we present CoMod, an abstractive method for generating the context of a document, from its content and that of the referenced documents, if any. CoMod analyzes the intricate patterns and relationships within a document’s content, thereby extracting and inferring the underlying context. The context generation process involves using a word linearization process as well as a Markov model, specifically a Bigram model, to predict the likelihood of word sequences. The Markov model is trained on a corpus of text and used to generate coherent sentences based on the probabilities of transitioning from one word to another. The word linearization model has been trained on general datasets. Markov tables allows to adapt the generated context to a specific domain and can be built on the fly in CoMod. The approach was compared to other methods and demonstrated very encouraging capabilities by outperforming other approaches tested on the same datasets. It thus confirms the potential of generative methods in the field of automatic text summarization and their ability of leveraging the power of machine learning for context generation to revolutionize information management, boosting productivity, scalability and knowledge discovery in various domains.
Finite Dimensional Estimation Algebra for Time-Varying Filtering System and Optimal Transport Particle Filter: A Tangent Flow Point of View

Ever since the technique of the Kalman filter was popularized, there has been a lot of research interest in finding more classes of finite-dimensional recursive filters. In past research, the estimation algebra method could only be used for time-invariant systems. In this article, we extend the estimation algebra method so that it applies to a general class of time-varying filtering systems. Then, the Wei-Norman method can be used to derive the explicit solution of the posterior distribution of state estimation. As a special control law, tangent flow is derived for the nonlinear filtering system based on the Monge-Amp$\grave{\text{e}}$re equation in optimal transport. As a result, we propose an optimal transportation filter by applying stochastic tangent flow to Yau filtering systems. The numerical experiments demonstrate the higher efficacy and accuracy of the proposed optimal transportation filter compared to common traditional algorithms such as the extended Kalman filter and feedback particle filter.
Harnessing Web3 on Carbon Offset Market for Sustainability: Framework and a Case Study

Blockchain, pivotal in shaping the metaverse and Web3, often draws criticism for high energy consumption and carbon emission. The rise of sustainability-focused blockchains, especially when intersecting with innovative wireless technologies, revises this predicament. To understand blockchain's role in sustainability, we propose a three-layers structure encapsulating four green utilities: Recording and Tracking, Wide Verification, Value Trading, and Concept Disseminating. Nori, a decentralized voluntary carbon offset project, serves as our case, illuminating these utilities. Our research unveils unique insights into the on-chain carbon market participants, affect factors of the market, value propositions of NFT-based carbon credits, and the role of social media to spread the concept of carbon offset. We argue that blockchain's contribution to sustainability is significant, with carbon offsetting potentially evolving as a new standard within the blockchain sector.
Vehicle Micro-Trajectory Automatic Acquisition Method Based on Multisensor Fusion

A vehicle micro-trajectory automatic acquisition method based on multisensor fusion is proposed to enhance the accuracy of trajectory sensed by traffic monitoring equipment while minimizing equipment expenditures. First, the YOLOv5 + DeepSORT vehicle recognition and tracking model is applied to obtain the video’s pixel trajectories of moving vehicles. Second, calibrate the camera’s internal parameters by calibration method with a checkerboard grid and obtain the conversion formula between pixel and camera coordinate system. Third, calibrate the camera’s external parameters by matching the feature point information of the diffuse calibration plate both in light detection and ranging (LiDAR) and camera, and determine the positional relationship between the LiDAR and camera coordinate system. Then, to convert pixel trajectory to real trajectory, we construct the conversion formula from the pixel coordinate system to the LiDAR coordinate system by combining the internal and external parameters. Finally, the kernel regression method based on the Gaussian function reduces inaccuracies caused by camera vibration and coordinate transformation. Also, a trajectory fusion method based on Kalman filtering with dynamically assigned weight coefficients is proposed. It uses multiple cameras to collect data simultaneously and effectively fuse them to improve the trajectory integrity and continuity further while improving the accuracy and reliability of vehicle tracking. Experiments are conducted on three scenarios to validate the proposed method: a straight road section, a curving road section, and an intersection. The experimental results were compared with the vehicle trajectories retrieved from LiDAR data. The results indicate that the average absolute error of the optimized trajectory under monocular vision is reduced by about 40%, and the average absolute error of the velocity is reduced by about 45% compared with that before the optimization. Compared with the real trajectory, the error of the calibrated trajectory is within 0.2 m. The fused trajectory under binocular vision can fill in more than 85% of the missing trajectory points. Compared with monocular vision, the average absolute error of the trajectory is reduced by more than 0.03 m, and the velocity error index is further improved. This method effectively improves the accuracy of acquiring vehicle trajectories through traffic monitoring videos.
Similarity-weighted Construction of Contextualized Commonsense Knowledge Graphs for Knowledge-intense Argumentation Tasks

Arguments often do not make explicit how a conclusion follows from its premises.To compensate for this lack, we enrich arguments with structured background knowledge to support knowledge-intense argumentation tasks.We present a new unsupervised method for constructing Contextualized Commonsense Knowledge Graphs (CCKGs) that selects contextually relevant knowledge from large knowledge graphs (KGs) efficiently and at high quality.Our work goes beyond context-insensitive knowledge extraction heuristics by computing semantic similarity between KG triplets and textual arguments.Using these triplet similarities as weights, we extract contextualized knowledge paths that connect a conclusion to its premise, while maximizing similarity to the argument.We combine multiple paths into a CCKG that we optionally prune to reduce noise and raise precision.Intrinsic evaluation of the quality of our graphs shows that our method is effective for (re)constructing human explanation graphs.Manual evaluations in a large-scale knowledge selection setup confirm high recall and precision of implicit CSK in the CCKGs.Finally, we demonstrate the effectiveness of CCKGs in a knowledge-insensitive argument quality rating task, outperforming strong baselines and rivaling a GPT-3 based system. 1
Privacy-Preserving Domain Adaptation of Semantic Parsers

Task-oriented dialogue systems often assist users with personal or confidential matters.For this reason, the developers of such a system are generally prohibited from observing actual usage.So how can they know where the system is failing and needs more training data or new functionality?In this work, we study ways in which realistic user utterances can be generated synthetically, to help increase the linguistic and functional coverage of the system, without compromising the privacy of actual users.To this end, we propose a two-stage Differentially Private (DP) generation method which first generates latent semantic parses, and then generates utterances based on the parses.Our proposed approach improves MAUVE by 2.5× and parse tree function type overlap by 1.3× relative to current approaches for private synthetic data generation, improving both on fluency and semantic coverage.We further validate our approach on a realistic domain adaptation task of adding new functionality from private user data to a semantic parser, and show overall gains of 8.5% points in accuracy with the new feature.
AQE: Argument Quadruplet Extraction via a Quad-Tagging Augmented Generative Approach

Argument mining involves multiple sub-tasks that automatically identify argumentative elements, such as claim detection, evidence extraction, stance classification, etc.However, each subtask alone is insufficient for a thorough understanding of the argumentative structure and reasoning process.To learn a complete view of an argument essay and capture the interdependence among argumentative components, we need to know what opinions people hold (i.e., claims), why those opinions are valid (i.e., supporting evidence), which source the evidence comes from (i.e., evidence type), and how those claims react to the debating topic (i.e., stance).In this work, we for the first time propose a challenging argument quadruplet extraction task (AQE), which can provide an allin-one extraction of four argumentative components, i.e., claims, evidence, evidence types, and stances.To support this task, we construct a large-scale and challenging dataset.However, there is no existing method that can solve the argument quadruplet extraction.To fill this gap, we propose a novel quad-tagging augmented generative approach, which leverages a quadruplet tagging module to augment the training of the generative framework.The experimental results on our dataset demonstrate the empirical superiority of our proposed approach over several strong baselines.
Rethinking Document-Level Relation Extraction: A Reality Check

Recently, numerous efforts have continued to push up performance boundaries of documentlevel relation extraction (DocRE) and have claimed significant progress in DocRE.In this paper, we do not aim at proposing a novel model for DocRE.Instead, we take a closer look at the field to see if these performance gains are actually true.By taking a comprehensive literature review and a thorough examination of popular DocRE datasets, we find that these performance gains are achieved upon a strong or even untenable assumption in common: all named entities are perfectly localized, normalized, and typed in advance.Next, we construct four types of entity mention attacks to examine the robustness of typical DocRE models by behavioral probing.We also have a close check on model usability in a more realistic setting.Our findings reveal that most of current DocRE models are vulnerable to entity mention attacks and difficult to be deployed in real-world end-user NLP applications.Our study calls more attentions for future research to stop simplifying problem setups, and to model DocRE in the wild rather than in an unrealistic Utopian world.
It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance

Progress in NLP is increasingly measured through benchmarks; hence, contextualizing progress requires understanding when and why practitioners may disagree about the validity of benchmarks.We develop a taxonomy of disagreement, drawing on tools from measurement modeling, and distinguish between two types of disagreement: 1) how tasks are conceptualized and 2) how measurements of model performance are operationalized.To provide evidence for our taxonomy, we conduct a metaanalysis of relevant literature to understand how NLP tasks are conceptualized, as well as a survey of practitioners about their impressions of different factors that affect benchmark validity.Our meta-analysis and survey across eight tasks, ranging from coreference resolution to question answering, uncover that tasks are generally not clearly and consistently conceptualized and benchmarks suffer from operationalization disagreements.These findings support our proposed taxonomy of disagreement.Finally, based on our taxonomy, we present a framework for constructing benchmarks and documenting their limitations.Benchmark Disagreements
AD-KD: Attribution-Driven Knowledge Distillation for Language Model Compression

Knowledge distillation has attracted a great deal of interest recently to compress pre-trained language models.However, existing knowledge distillation methods suffer from two limitations.First, the student model simply imitates the teacher's behavior while ignoring the underlying reasoning.Second, these methods usually focus on the transfer of sophisticated model-specific knowledge but overlook dataspecific knowledge.In this paper, we present a novel attribution-driven knowledge distillation approach, which explores the token-level rationale behind the teacher model based on Integrated Gradients (IG) and transfers attribution knowledge to the student model.To enhance the knowledge transfer of model reasoning and generalization, we further explore multi-view attribution distillation on all potential decisions of the teacher.Comprehensive experiments are conducted with BERT on the GLUE benchmark.The experimental results demonstrate the superior performance of our approach to several state-of-the-art methods.
Scalable and Explainable Automated Scoring for Open-Ended Constructed Response Math Word Problems

Open-ended constructed response math word problems ("math plus text", or MPT) are a powerful tool in the assessment of students' abilities to engage in mathematical reasoning and creative thinking.Such problems ask the student to compute a value or construct an expression and then explain, potentially in prose, what steps they took and why they took them.MPT items can be scored against highly structured rubrics, and we develop a novel technique for the automated scoring of MPT items that leverages these rubrics to provide explainable scoring.We show that our approach can be trained automatically and performs well on a large dataset of 34,417 responses across 14 MPT items.
A Multi-task Learning Framework for Quality Estimation

Quality Estimation (QE) is the task of evaluating machine translation output in the absence of reference translation.Conventional approaches to QE involve training separate models at different levels of granularity viz., word-level, sentence-level, and document-level, which sometimes lead to inconsistent predictions for the same input.To overcome this limitation, we focus on jointly training a single model for sentence-level and word-level QE tasks in a multi-task learning framework.Using two multi-task learning-based QE approaches, we show that multi-task learning improves the performance of both tasks.We evaluate these approaches by performing experiments in different settings, viz., single-pair, multi-pair, and zero-shot.We compare the multi-task learning-based approach with baseline QE models trained on single tasks and observe an improvement of up to 4.28% in Pearson's correlation (r) at sentence-level and 8.46% in F1-score at word-level, in the singlepair setting.In the multi-pair setting, we observe improvements of up to 3.04% at sentencelevel and 13.74% at word-level; while in the zero-shot setting, we also observe improvements of up to 5.26% and 3.05%, respectively.We make the models proposed in this paper publically available 1 .
RHO: Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding

Dialogue systems can leverage large pretrained language models and knowledge to generate fluent and informative responses.However, these models are still prone to produce hallucinated responses not supported by the input source, which greatly hinders their application.The heterogeneity between external knowledge and dialogue context challenges representation learning and source integration, which further contributes to unfaithfulness.To handle this challenge and generate more faithful responses, this paper presents RHO (ρ) utilizing the representations of linked entities and relation predicates from a knowledge graph (KG).We propose (1) local knowledge grounding to combine textual embeddings with the corresponding KG embeddings; and (2) global knowledge grounding to equip RHO with multi-hop reasoning abilities via the attention mechanism.In addition, we devise a response re-ranking technique based on walks over KG sub-graphs for better conversational reasoning.Experimental results on OpenDialKG (Moon et al., 2019) show that our approach significantly outperforms stateof-the-art methods on both automatic and human evaluation by a large margin, especially in hallucination reduction (17.54% in FeQA (Durmus et al., 2020)). 1
Jack-flood at SemEval-2023 Task 5:Hierarchical Encoding and Reciprocal Rank Fusion-Based System for Spoiler Classification and Generation

The rise of social media has exponentially witnessed the use of clickbait posts that grab users' attention.This paper presents our approach, which use different encoding techniques.We propose hierarchical encoding with count and document length feature-based model for spoiler type classification, which uses Recurrence over Pretrained Encoding.We also propose combining multiple ranking with reciprocal rank fusion for passage spoiler retrieval and question-answering approach for phrase spoiler retrieval.For multipart spoiler retrieval, we combine the above two spoiler retrieval methods.Based on the benchmark, the experimental results indicate that our proposed spoiler retrieval methods are highly effective in retrieving spoilers that are semantically very close to the ground truth spoilers.
Improving Domain Generalization for Prompt-Aware Essay Scoring via Disentangled Representation Learning

Automated Essay Scoring (AES) aims to score essays written in response to specific prompts.Many AES models have been proposed, but most of them are either prompt-specific or prompt-adaptive and cannot generalize well on "unseen" prompts.This work focuses on improving the generalization ability of AES models from the perspective of domain generalization, where the data of target prompts cannot be accessed during training.Specifically, we propose a prompt-aware neural AES model to extract comprehensive representation for essay scoring, including both promptinvariant and prompt-specific features.To improve the generalization of representation, we further propose a novel disentangled representation learning framework.In this framework, a contrastive norm-angular alignment strategy and a counterfactual self-training strategy are designed to disentangle the prompt-invariant information and prompt-specific information in representation.Extensive experimental results on datasets of both ASAP and TOEFL11 demonstrate the effectiveness of our method under the domain generalization setting.
kNN-TL: k-Nearest-Neighbor Transfer Learning for Low-Resource Neural Machine Translation

Transfer learning has been shown to be an effective technique for enhancing the performance of low-resource neural machine translation (NMT).This is typically achieved through either fine-tuning a child model with a pretrained parent model, or by utilizing the output of the parent model during the training of the child model.However, these methods do not make use of the parent knowledge during the child inference, which may limit the translation performance.In this paper, we propose a k-Nearest-Neighbor Transfer Learning (kNN-TL) approach for low-resource NMT, which leverages the parent knowledge throughout the entire developing process of the child model.Our approach includes a parent-child representation alignment method, which ensures consistency in the output representations between the two models, and a child-aware datastore construction method that improves inference efficiency by selectively distilling the parent datastore based on relevance to the child model.Experimental results on four lowresource translation tasks show that kNN-TL outperforms strong baselines.Extensive analyses further demonstrate the effectiveness of our approach.Code and scripts are freely available at https://github.com/NLP2CT/kNN-TL.