Reviewerly/exHarmony huggingface dataset:
A scientometric study of three decades of machine translation research: Trending issues, hotspot research, and co-citation analysis

This study aims to examine machine translation research in journals indexed in the Web of Science to find out the research trending issue, hotspot areas of research, and document co-citation analysis. To this end, 541 documents published between 1992 and 2022 were retrieved and analyzed using CiteSpace, and Bibexcel. Many metrics were analyzed such as document co-citation analysis, sources co-citation analyses, authors’ keywords analysis, and Hirsch index. Data were coded and filtered to include research related to machine translation from the perspectives of language and translation studies. We identified 11 clusters that represented the hotspot research during the period of almost three decades of research. We also discovered that a significant focus of research in machine translation centered around enhancing the translation process through the implementation of neural networks integrated with artificial intelligence. Additionally, we observed the incorporation of human post-editing as a means to refine and improve machine-translated outputs. We found that translation studies journals were the most highly co-cited journals and Google translate was the most highly used machine translation. This study highlights the trending issues and hotspots in machine translation research within language and translation studies. The integration of neural networks with artificial intelligence and human post-editing emerged as prominent areas of focus for enhancing translation quality. The findings of the current study inform future research and technological advancements in machine translation, guiding efforts to improve translation processes and outcomes.
The Energy Consumption of the Ethereum-Ecosystem

Decentralized Finance (DeFi) and blockchain technology are potential game changers but come with a snag. Indeed, the cloven hoof becomes apparent in the network of cryptocurrencies: the energy consumption of cryptocurrencies presents, without any doubt, an environmental challenge. Traditional methodologies for quantifying this consumption, broadly classified into top-down and bottom-up approaches, have merits and limitations. However, discrepancies between these methods indicate an opportunity for more nuanced and precise analysis. Based on the Ethereum ecosystem, this study first presents a detailed analysis of the split into Ethereum and Ethereum Classic, and seconds the energy consumption consequences of the merge. Leveraging domain knowledge, and profitability thresholds, we estimate Ethereum's pre-Merge energy demand and demonstrate the potential of PoS consensus mechanisms to significantly reduce the energy demands of cryptocurrency networks, contributing to the broader discourse on the environmental sustainability of blockchain technologies.
Time Series Enhanced Graph Neural Networks for Session-based Recommendation

In recent years, Graph Neural Networks (GNNs) have received much attention in many fields. Many researchers have also tried applying GNNs to session-based recommendation(SBR), which predicts the next click action of anonymous users based on historical behavior sequences. However, most existing approaches suffer from two problems. Firstly, they cannot capture the temporal order of information propagation within a session well. The second is that users' interests may differ in different sessions, and most methods focus on extracting information from a single session without considering the relationships between sessions. For the first problem, we propose a Time Series Enhanced Graph Neural Networks(TSGNN), which assigns different labels to the edges of the session graph and learns intra-session item representations according to the order of information propagation. For the second problem, we construct multiple sessions as global graphs to achieve inter-session item representation learning that explicitly captures dynamic user interests. Finally, the learned item representations at both levels are combined with position vectors using a soft attention mechanism. We have conducted various experiments on two benchmark datasets showing that the TSGNN model outperforms state-of-the-art methods.
A sequential neural recommendation system exploiting BERT and LSTM on social media posts

Abstract Tourists share opinions about Points of Interest (POIs) through online posts and social media platforms. Opinion mining is a popular technique for extracting feedback from tourists who visited various places hidden in reviews, which are used in several tourist applications that generally reflect their preference towards POI. On the other hand, a trip schema is difficult for tourists because they must pick up sequential POIs in unknown areas that meet their limitations and preferences. However, most prior trip suggestion methods are suboptimal for several reasons, including that they do not consider valuable user reviews and rely exclusively on left-to-right unidirectional discovery sequence models. This study proposes a Neural Network-Long Short-Term Memory (LSTM) POI recommendation system for calculating user similarity based on opinions and preferences. In addition, it presents a method for discovering sequential trip recommendations with Bidirectional Encoder Representations from Transformer (BERT) using a deep learning method. Furthermore, this neural hybrid framework identifies a list of optimal trip candidates by combining personalized POIs with multifaceted context. Furthermore, this method employs the valuable information contained in user posts and their demographic information on social media to mitigate the well-known cold start issue. In the experimental evaluation based on two datasets, Tripadvisor and Yelp, this hybrid method outperforms other state-of-the-art methods when considering F-Score, nDCG, RMSE, and MAP.
Spatial Clustering Regression of Count Value Data via Bayesian Mixture of Finite Mixtures

Investigating relationships between response variables and covariates in areas such as environmental science, geoscience, and public health is an important endeavor. Based on a Bayesian mixture of finite mixtures model, we present a novel spatially clustered coefficients regression model for count value data. The proposed method detects the spatial homogeneity of the Poisson regression coefficients. A Markov random field constrained mixture of finite mixtures prior provides a regularized estimator of the number of clusters of regression coefficients with geographical neighborhood information. As a by-product, we also provide the theoretical properties of our proposed method when the Markov random field is exchangeable. An efficient Markov chain Monte Carlo algorithm is developed by using the multivariate log gamma distribution as a base distribution. Simulation studies are carried out to examine the empirical performance of the proposed method. Additionally, we analyze Georgia's premature death data as an illustration of the effectiveness of our approach. The supplementary materials are provided on GitHub at https://github.com/pengzhaostat/MLG_MFM.
DePlot: One-shot visual language reasoning by plot-to-table translation

Visual language such as charts and plots is ubiquitous in the human world.Comprehending plots and charts requires strong reasoning skills.Prior state-of-the-art (SOTA) models require at least tens of thousands of training examples and their reasoning capabilities are still much limited, especially on complex humanwritten queries.This paper presents the first few(one)-shot solution to visual language reasoning.We decompose the challenge of visual language reasoning into two steps: (1) plot-to-text translation, and (2) reasoning over the translated text.The key in this method is a modality conversion module, named as DE-PLOT, which translates the image of a plot or chart to a linearized table.The output of DE-PLOT can then be directly used to prompt a pretrained large language model (LLM), exploiting the few-shot reasoning capabilities of LLMs.To obtain DEPLOT, we standardize the plot-to-table task by establishing unified task formats and metrics, and train DEPLOT endto-end on this task.DEPLOT can then be used off-the-shelf together with LLMs in a plugand-play fashion.Compared with a SOTA model finetuned on thousands of data points, DEPLOT+LLM with just one-shot prompting achieves a 29.4% improvement over finetuned SOTA on human-written queries from the task of chart QA.
Instance-Guided Prompt Learning for Few-Shot Text Matching

Few-shot text matching is a more practical technique in natural language processing (NLP) to determine whether two texts are semantically identical.Recent studies based on prompt learning have shown remarkable results.These methods primarily employ uniform prompt patterns for all instances.But they fail to take into account the connection between prompts and instances.This paper argues that dynamically strengthening the correlation between particular instances and the prompts is necessary because fixed prompts cannot adequately fit all diverse instances in inference.Thus, we propose IGATE: Instance-Guided prompt leArning for few-shoT tExt matching, a novel pluggable prompt learning method.The gate mechanism between embedding and encoder of the PLM makes use of the semantics of instances to regulate the effects of the gate on the prompt tokens.The experimental findings show that IGATE achieves SOTA performance on MRPC and QQP, outperforming strong baselines.Our codes are available at https://github.com/Du-Jia/IGATE.
ANALOGICAL - A Novel Benchmark for Long Text Analogy Evaluation in Large Language Models

Over the past decade, analogies, in the form of word-level analogies, have played a significant role as an intrinsic measure of evaluating the quality of word embedding methods such as word2vec.Modern large language models (LLMs), however, are primarily evaluated on extrinsic measures based on benchmarks such as GLUE and SuperGLUE, and there are only a few investigations on whether LLMs can draw analogies between long texts.In this paper, we present ANALOGICAL, a new benchmark to intrinsically evaluate LLMs across a taxonomy of analogies of long text with six levels of complexity -(i) word, (ii) word vs. sentence, (iii) syntactic, (iv) negation, (v) entailment, and (vi) metaphor.Using thirteen datasets and three different distance measures, we evaluate the abilities of eight LLMs in identifying analogical pairs in the semantic vector space.Our evaluation finds that it is increasingly challenging for LLMs to identify analogies when going up the analogy taxonomy.
Multi-modal Action Chain Abductive Reasoning

Abductive Reasoning, has long been considered to be at the core ability of humans, which enables us to infer the most plausible explanation of incomplete known phenomena in daily life.However, such critical reasoning capability is rarely investigated for contemporary AI systems under such limited observations.To facilitate this research community, this paper sheds new light on Abductive Reasoning by studying a new vision-language task, Multi-modal Action chain abductive Reasoning (MAR), together with a large-scale Abductive Reasoning dataset: Given an incomplete set of language described events, MAR aims to imagine the most plausible event by spatio-temporal grounding in past video and then infer the hypothesis of subsequent action chain that can best explain the language premise.To solve this task, we propose a strong baseline model that realizes MAR from two perspectives: (i) we first introduce the transformer, which learns to encode the observation to imagine the plausible event with explicitly interpretable event grounding in the video based on the commonsense knowledge recognition ability.(ii) To complete the assumption of a follow-up action chain, we design a novel symbolic module that can complete strict derivation of the progressive action chain layer by layer.We conducted extensive experiments on the proposed dataset, and the experimental study shows that the proposed model significantly outperforms existing video-language models in terms of effectiveness on our newly created MAR dataset.Our dataset is available 1 .
Respectful or Toxic? Using Zero-Shot Learning with Language Models to Detect Hate Speech

Hate speech detection faces two significant challenges: 1) the limited availability of labeled data and 2) the high variability of hate speech across different contexts and languages.Prompting brings a ray of hope to these challenges.It allows injecting a model with taskspecific knowledge without relying on labeled data.This paper explores zero-shot learning with prompting for hate speech detection.We investigate how well zero-shot learning can detect hate speech in 3 languages with limited labeled data.We experiment with various large language models and verbalizers on 8 benchmark datasets.Our findings highlight the impact of prompt selection on the results.They also suggest that prompting, specifically with recent large language models, can achieve performance comparable to and surpass fine-tuned models, making it a promising alternative for under-resourced languages.Our findings highlight the potential of prompting for hate speech detection and show how both the prompt and the model have a significant impact on achieving more accurate predictions in this task.
Focused Prefix Tuning for Controllable Text Generation

In a controllable text generation dataset, there exist unannotated attributes that could provide irrelevant learning signals to models that use it for training and thus degrade their performance.We propose focused prefix tuning (FPT) to mitigate the problem and to enable the control to focus on the desired attribute.Experimental results show that FPT can achieve better control accuracy and text fluency than baseline models in single-attribute control tasks.In multiattribute control tasks, FPT achieves comparable control accuracy with the state-of-the-art approach while keeping the flexibility to control new attributes without retraining existing models.
MTGP: Multi-turn Target-oriented Dialogue Guided by Generative Global Path with Flexible Turns

Target-oriented dialogue guides the dialogue to a target quickly and smoothly.The latest approaches focus on global planning, which plans toward the target before the conversation instead of adopting a greedy strategy during the conversation.However, the global plan in existing works is fixed to certain turns by generating paths with certain nodes, which limits the optimization of turns and coherence of the target-oriented process.Toward flexible global planning, we propose to generate a global path as a natural language sentence instead of a sequence of nodes.With this path, the dialog is guided to the target with flexible turns of dialog.For model training, we also extract targetoriented dialogues from the chit-chat corpus with a knowledge graph.We conduct experiments on three datasets and simulate scenarios with and without user participation.The results show that our method has fewer turns, more coherent semantics, and a higher success rate in reaching the target than baselines.
Considerations for meaningful sign language machine translation based on glosses

Automatic sign language processing is gaining popularity in Natural Language Processing (NLP) research (Yin et al., 2021).In machine translation (MT) in particular, sign language translation based on glosses is a prominent approach.In this paper, we review recent works on neural gloss translation.We find that limitations of glosses in general and limitations of specific datasets are not discussed in a transparent manner and that there is no common standard for evaluation.To address these issues, we put forward concrete recommendations for future research on gloss translation.Our suggestions advocate awareness of the inherent limitations of glossbased approaches, realistic datasets, stronger baselines and convincing evaluation.Glosses (DSGS) KINDER FREUEN WARUM FERIEN NÄHER-KOMMEN Translation (DE) Die Kinder freuen sich, weil die Ferien näher rücken.Glosses (EN) ('CHILDREN REJOICE WHY HOLIDAYS APPROACHING') Translation (EN) ('The children are happy because the holidays are approaching.')
Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference

Fine-tuning has been proven to be a simple and effective technique to transfer the learned knowledge of Pre-trained Language Models (PLMs) to downstream tasks.However, vanilla fine-tuning easily overfits the target data and degrades the generalization ability.Most existing studies attribute it to catastrophic forgetting, and they retain the pre-trained knowledge indiscriminately without identifying what knowledge is transferable.Motivated by this, we frame fine-tuning into a causal graph and discover that the crux of catastrophic forgetting lies in the missing causal effects from the pretrained data.Based on the causal view, we propose a unified objective for fine-tuning to retrieve the causality back.Intriguingly, the unified objective can be seen as the sum of the vanilla fine-tuning objective, which learns new knowledge from target data, and the causal objective, which preserves old knowledge from PLMs.Therefore, our method is flexible and can mitigate negative transfer while preserving knowledge.Since endowing models with commonsense is a long-standing challenge, we implement our method on commonsense QA with a proposed heuristic estimation to verify its effectiveness.In the experiments, our method outperforms state-of-the-art fine-tuning methods on all six commonsense QA datasets and can be implemented as a plug-in module to inflate the performance of existing QA models.
Towards Alleviating the Object Bias in Prompt Tuning-based Factual Knowledge Extraction

Many works employed prompt tuning methods to automatically optimize prompt queries and extract the factual knowledge stored in Pretrained Language Models.In this paper, we observe that the optimized prompts, including discrete prompts and continuous prompts, exhibit undesirable object bias.To handle this problem, we propose a novel prompt tuning method called MeCoD consisting of three modules: Prompt Encoder, Object Equalization and Biased Object Obstruction.Experimental results show that MeCoD can significantly reduce the object bias and at the same time improve accuracy of factual knowledge extraction.
GRASUM at BioLaySumm Task 1: Background Knowledge Grounding for Readable, Relevant, and Factual Biomedical Lay Summaries

Communication of scientific findings to the public is important for keeping non-experts informed of developments such as life-saving medical treatments.However, generating readable lay summaries from scientific documents is challenging, and currently, these summaries suffer from critical factual errors.One popular intervention for improving factuality is using additional external knowledge to provide factual grounding.However, it is unclear how these grounding sources should be retrieved, selected, or integrated, and how supplementary grounding documents might affect the readability or relevance of the generated summaries.We develop a simple method for selecting grounding sources and integrating them with source documents.We then use the Bio-LaySumm summarization dataset to evaluate the effects of different grounding sources on summary quality.We found that grounding source documents improves the relevance and readability of lay summaries but does not improve factuality of lay summaries.This continues to be true in zero-shot summarization settings where we hypothesized that grounding might be even more important for factual lay summaries 1 .Lay Summary: [Messenger RNAs carry the instructions necessary to synthesize proteins that do work for the cell]background In this work , we surveyed mRNA ends from 10 , 000 genes in immune cells from genetically distinct human individuals.Abstracts: Virtually all messenger RNAs (mRNAs) in eukaryotes are cleaved and polyadenylated at their 3 ends.UMLS: RNA is unique among biological macromolecules in that it can encode genetic information.Wiki Simple: Messenger RNA carries a genetic message from the DNA to the protein making machinery of the cell.Wikipedia: An mRNA molecule is transcribed from the DNA sequence and is later translated into protein.
NLNDE at SemEval-2023 Task 12: Adaptive Pretraining and Source Language Selection for Low-Resource Multilingual Sentiment Analysis

This paper describes our system developed for the SemEval-2023 Task 12 "Sentiment Analysis for Low-resource African Languages using Twitter Dataset".Sentiment analysis is one of the most widely studied applications in natural language processing.However, most prior work still focuses on a small number of high-resource languages.Building reliable sentiment analysis systems for low-resource languages remains challenging, due to the limited training data in this task.In this work, we propose to leverage language-adaptive and task-adaptive pretraining on African texts and study transfer learning with source language selection on top of an African language-centric pretrained language model.Our key findings are: (1) Adapting the pretrained model to the target language and task using a small yet relevant corpus improves performance remarkably by more than 10 F1 score points.(2) Selecting source languages with positive transfer gains during training can avoid harmful interference from dissimilar languages, leading to better results in multilingual and cross-lingual settings.In the shared task, our system wins 8 out of 15 tracks and, in particular, performs best in the multilingual evaluation.
FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answering

Automatic fact verification has received significant attention recently.Contemporary automatic fact-checking systems focus on estimating truthfulness using numerical scores which are not human-interpretable.A human factchecker generally follows several logical steps to verify a verisimilitude claim and conclude whether it's truthful or a mere masquerade.Popular fact-checking websites follow a common structure for fact categorization such as half true, half false, false, pants on fire, etc.Therefore, it is necessary to have an aspectbased (delineating which part(s) are true and which are false) explainable system that can assist human fact-checkers in asking relevant questions related to a fact, which can then be validated separately to reach a final verdict.In this paper, we propose a 5W framework (who, what, when, where, and why) for question-answer-based fact explainability.To that end, we present a semi-automatically generated dataset called FACTIFY-5WQA, which consists of 391, 041 facts along with relevant 5W QAs -underscoring our major contribution to this paper.A semantic role labeling system has been utilized to locate 5Ws, which generates QA pairs for claims using a masked language model.Finally, we report a baseline QA system to automatically locate those answers from evidence documents, which can serve as a baseline for future research in the field.Lastly, we propose a robust fact verification system that takes paraphrased claims and automatically validates them.The dataset and the baseline model are available at https: //github.com/ankuranii/acl-5W-QA• Q1: Who lawsuits against whom?Ans: Moderna lawsuits against Pfizer-BioNTech • Q1: What the lawsuit shows?