Reviewerly/exHarmony huggingface dataset:
Navigating Digital Learning Landscapes: Unveiling the Interplay Between Learning Behaviors, Digital Literacy, and Educational Outcomes

Abstract In the swiftly evolving realm of education, digital learning has emerged as a transformative force, revolutionizing how learners and educators interact with knowledge. This paper delves into the intricate interplay between digital learning abilities, instructional design, and learner support, particularly within the context of blended learning, where traditional educational paradigms merge with technological advancements. Blended learning, the fusion of in-person and digital instruction, necessitates a delicate equilibrium to unleash its full potential. We explore the multifaceted attributes that underpin self-regulation within these environments, such as authenticity, personalization, and scaffolding. Moreover, we investigate how blended learning’s effectiveness transcends various educational domains, from vocational schools to mathematics education and entrepreneurship training. The paper underscores the significance of comprehensive learner support and skillful instructional design. It highlights the symbiotic relationship between blended learning and the cultivation of digital literacy, encompassing not just technical prowess but also critical thinking and purposeful digital engagement. As educators embrace these insights, they stand poised to create dynamic educational experiences that empower students for the digital age.
User Preference Prediction for Online Dialogue Systems Based on Pre-trained Large Model

Online conversation system user preference prediction can improve online conversation system to enhance the quality of the conversation. This paper design an online conversation quality preference assessment model for this problem from the novel perspective of pre-trained large model classification, which incorporates two pre-trained large model, BERT and Ernie, by analyzing data variability, using data enhancement and pseudo-labeling techniques and semi-supervised learning, to finally build our user preference prediction assessment model. The model was evaluated on the official dataset released by NLPCC-2023, and the KL value of the evaluation metric reached 0.9173, which represents the latest level in this research direction. Other features such as linguistic expressions in dialogues are not considered in this study for the time being, and the interpretability of the model needs to be further improved. By fusing pre-trained large model and performing semi-supervised learning, the resulting model are able to predict user preferences of online dialogue systems very well.
Generating Better Responses from User Feedback via Reinforcement Learning and Commonsense Inference

Dialogue generation task is one of the popular research topics in the field of natural language processing. However, how to improve the quality of model generated responses with the user feedback in the dialogue generation task is still one of the difficulties in the research. In this paper, we propose a dialogue generation method based on user feedback by modeling the likeability of user feedback and optimizing the model by using Reinforcement Learning from Human Feedback (RLHF) techniques to generate more likeable responses to users. We also introduce commonsense inference to help the model better understand the knowledge context and user intent. Finally, we used contrastive search in the decoding stage to make the generated responses more diverse. To verify the effectiveness of the model, we conducted some experiments and compared our model with the baseline models. The experiment results show that our approach outperforms the baseline models in terms of automatic evaluation. The final evaluation results show that our model ranks 2nd in the NLPCC 2023 Shared Task 9 Track 2.
Overview of the NLPCC 2023 Shared Task 9: User Feedback Prediction and Response Generation

This paper presents an overview of user feedback prediction and response generation in the NLPCC 2023 shared task. We focus on how to utilize feedback data of user likes and dislikes to guide conversation response generation. The goal of this task is to predict accurate user preference and improve response quality to increase user likes. Participants need to integrate preference information into their models to generate responses that align with the user needs. In this paper, we summarize the key components of this task, including task description, dataset, evaluation metrics, participant methods, and final results. We also highlight the potential applications of incorporating like and dislike data in conversation generation.
Adversarial Training and Model Ensemble for User Feedback Prediciton in Conversation System

Developing automatic evaluation methods that are highly correlated with human assessment is crucial in the advancement of dialogue systems. User feedback in conversation system provides a signal that represents user preferences and response quality. The user feedback prediction (UFP) task aims to predict the probabilities of likes with machine-generated responses given a user query, offering a unique perspective to facilitate dialogue evaluation. In this paper, we propose a powerful UFP system, which leverages Chinese pre-trained language models (PLMs) to understand the user queries and system replies. To improve the robustness and generalization ability of our model, we also introduce adversarial training for PLMs and design a local and global model ensemble strategy. Our system ranks first in NLPCC 2023 shared Task 9 Track 1 (User Feedback Prediction). The experimental results show the effectiveness of the method applied in our system.
Discursive use of stability in New York Times’ coverage of China: a sentiment analysis approach

Abstract The importance of stability has been consistently emphasized in China and the discursive use of stability is found to have legitimizing effects in Chinese newspapers, but how such political keywords are employed by the newspaper of a country that is ideologically distinct from China remains underexplored. This study addresses this gap by investigating the use of stability in The New York Times ’ coverage of China between 1980 and 2020, drawing on critical discourse analysis (particularly, the discourse-historical approach) and sentiment analysis. A diachronic quantitative analysis demonstrates an overall negative sentiment in news reports relating to China’s stability across these years, with positive sentiment evident only during the 1980s and negative sentiment prevailing from 1990 to 2020. These findings are consistent with general trends in US-China relations and US foreign policy over the four decades. Qualitative analysis reveals that negative sentiment focuses on sociopolitical and territorial issues, whereas positive sentiment focuses primarily on economic and financial aspects, indicating that the newspaper views the issue of China’s stability from a politically self-interested perspective of the US and is also concerned about the persistence of certain dominant ideologies in American society. This study contributes to a greater comprehension of the use of political keywords in national and international news discourse, especially by the media of ideologically diverse societies. Moreover, because the application of sentiment analysis to critical discourse analysis and news discourse analysis has proven to be time-efficient, verifiable, and accurate, researchers can confidently employ it to disclose hidden meanings in texts.
Immunophenotyping of Acute Leukemia in Pediatric Patients: Tertiary Care Centre Experience from Lahore

Acute Leukemia is the most prevalent malignancy of childhood, globally. Immunophenotyping by flowcytometry has developed as an important technique for its outstanding contributions in diagnosis and management of leukemia patients. Objective: To determine the frequency and immunophenotyping by flowcytometry of Acute leukemia in pediatric age group. Methods: The study was conducted at The University of Child health sciences and Children’s Hospital, Lahore from July-2021 to Feb-2022. Data were gathered from 101 consecutive patients, of age from 0.5 to 15 years, that had acute leukemia immunophenotyping using flowcytometry. Results: Flowcytometric immunophenotyping of 101 leukemia patients over 8-month period showed that 67.3% and 33.6% male were females respectively. AML, T-ALL, and B-ALL prevalence was highest in patients with 5 to 10 years of age. Out of 101 leukemia patients, 16 (15.8%) and 85 (84.1%) had AML and ALL diagnosis respectively. Of these 85 ALL cases, 72 (84.7%) were B-ALL and 13 (15.2%) were T-ALL. Study founded that most frequent CD markers in B-ALL, were CD79 and CD19 (100%) whereas CD3 and CD5 in T-ALL and CD13 (93.8%) and CD34 (87.5%) in AML. CD34 was a common marker among B-ALL, T-ALL and AML. Among B-ALL and T-ALL, AntiTdt was a common CD marker whereas HLA-DR was common among AML and B-ALL. Conclusions: Results of Acute leukemia immunophenotyping were homologous to worldwide published research. For accurate leukemia lineage, immunophenotyping of AML and ALL is essential since, if therapy is started based solely on morphological diagnosis, approximately 25% of patients may not respond or recure.
A Numeracy-Enhanced Decoding for Solving Math Word Problem

Deep neural models have achieved promising progress in solving Math Word Problems (MWPs) recently. This paper presents a deep neural solver by adopting numeracy-enhanced decoding to promote the performance of expressions generation. It leverages numerical properties to enhance the capabilities of the decoder, primarily focusing on two aspects: token embedding and target prediction. For token embedding, this paper proposes a numeracy-enhanced token embedding method, which fuses the explicit numerical feature with the contextual feature for number tokens, enabling the decoder to perceive numerical properties during the inference. For target prediction, this paper proposes a dynamic target prediction method, which utilizes a numerical attention network to identify the mathematical category of the problem and adaptively invokes category-aware parameter matrices to generate diverse expressions for different problems. Experimental results demonstrate that the proposed method not only achieves competitive performance on the Chinese MWP dataset but also achieves state-of-the-art results on the NLPCC Shared Task 3 dataset.
Overview of the NLPCC 2023 Shared Task: Chinese Medical Instructional Video Question Answering

In this paper, we present an overview of the NLPCC 2023 shared task, named Chinese Medical Instructional Video (CMIVQA), which includes three sub-tracks: temporal answer grounding in a single video, video corpus retrieval, and temporal answer grounding in video corpus. The CMIVQA datasets containing the videos, audios, and corresponding subtitles are made public, and the corresponding labels are manually annotated by medical experts. Details of the shared task, datasets, evaluation metrics, and final results will be provided in order. We hope this shared task can provide more insights into the first-aid, medical emergency, or medical education.
Enhancing Conversational Aspect-Based Sentiment Quadruple Analysis with Context Fusion Encoding Method

Aspect-based sentiment analysis (ABSA) has been a hot research topic due to its ability to fully exploit people’s opinions through social media texts. Compared with analyzing sentiment in short texts, conversational aspect-based sentiment quadruple analysis, also known as DiaASQ, aiming to extract the sentiment quadruple of target-aspect-opinion-sentiment in a dialogue, is a relatively new task that involves multiple speakers with varying stances in a conversation. Conversations are longer than ordinary texts and have richer contexts, which can lead to context loss and pairing errors. To address this issue, this work proposes a context-fusion encoding method based on conversation threads and lengths to integrate the speech of different speakers, enabling the model to better understand conversational context and extract cross-utterance quadruples. Experimental results have demonstrated that the proposed method achieves an average F1-score of 42.12% in DiaASQ, which is 6.48% higher than the best comparative model, indicating superior performance.
Scientific Reading Comprehension with Sentences Selection and Ranking

Scientific Machine Reading Comprehension (SMRC) aims to understand scientific long text by providing answers for the given questions. Most existing methods trend to answer the question using Transformer-based models. However, in the scientific domain, the original text is longer than the general domain. In this paper, we proposed a model that consists of a content retrieval module and a pre-trained model module. The content retrieval module finds the most semantically relevant sentences from the text and re-rank them. The seleted sentences and question will be input into the pre-trained model to get the answers. This model could overcome the length limitation of Transformer model length while achieving impressive results. Our model achieved 0.45 score of RougeL, resulting in the second place in the NLPCC2023 Shared Task2.
Semantic Candidate Retrieval for Few-Shot Entity Linking

Entity Linking (EL) is the task of automatically linking entity mentions in texts to the corresponding entries in a knowledge base. Current EL systems exhibit the great performances on the standard datasets, but in real-world applications, they are computationally intensive and expensive in large-scale processing, and the entity entries are limited to the knowledge bases. The newly-emerging entities may hinder the generalization ability of the EL systems. To this end, we propose the semantic candidate retrieval method for the few-shot entity linking task. The semantic candidates corresponding to the mentions are selected by inverted indexing, and then, the semantic ranker is proposed to choose the top appropriate candidate to be linked. The proposed model achieves the accuracy of 53.19% in the shared task 6 of NLPCC-2023.
Improving Cross-Modal Visual Answer Localization in Chinese Medical Instructional Video Using Language Prompts

The growing popularity of video content for acquiring knowledge highlights the need for efficient methods to extract relevant information from videos. Visual Answer Localization (VAL) is a solution to this challenge, as it identifies video clips that can provide answers to user questions. In this paper, we explore the VAL task using the Chinese Medical instructional video dataset as part of the CMIVQA track1 shared task. However, VAL encounters difficulties due to differences between visual and textual modalities. Existing VAL methods use separate video and text encoding streams, as well as cross encoders, to align and predict relevant video clips. To address this issue, we adopt prompt-based learning, a successful paradigm in Natural Language Processing (NLP). Prompt-based learning reformulates downstream tasks to simulate the masked language modeling task used in pre-training, using a textual prompt. In our work, we develop a prompt template for the VAL task and employ the prompt learning approach. Additionally, we integrate an asymmetric co-attention module to enhance the integration of video and text modalities and facilitate their mutual interaction. Through comprehensive experiments, we demonstrate the effectiveness of our proposed methods, achieving first place in the CMIVQA track1 leaderboard with a total score of 0.3891 in testB.
TiBERT: A Non-autoregressive Pre-trained Model for Text Editing

Text editing refers to the task of creating new sentences by altering existing text through methods such as replacing, inserting, or deleting. Two commonly used techniques for text editing are Seq2Seq and sequence labeling. The Seq2Seq method can be time-consuming, while the sequence labeling method struggles with multi-token insertion. To solve these issues, we propose a novel pre-trained model called TiBERT, which is specially designed for Text Editing tasks. TiBERT addresses these challenges by adjusting the length of the hidden representation to insert and delete tokens. We pre-train our model using a denoising task on a large dataset. As a result, TiBERT provides not only fast inference but also an improvement in the quality of text generation. We test the model on grammatical error correction, text simplification, and Chinese spelling check tasks. The experimental results show that TiBERT predicts faster and achieves better results than other pre-trained models in these text editing tasks.
Overview of the NLPCC 2023 Shared Task: Chinese Essay Discourse Coherence Evaluation

In this paper, we present an overview of the Chinese Essay Discourse Coherence Evaluation task in the NLPCC 2023 shared tasks. We give detailed descriptions of the task definition and the data for training as well as evaluation. We also summarize the approaches investigated by the participants of this task. Such approaches demonstrate the state-of-the-art of discourse coherence evaluation for Chinese essay. The data set and evaluation tool used by this task is available at https://github.com/cubenlp/NLPCC-2023-Shared-Task7 .
HWCGEC:HW-TSC’s 2023 Submission for the NLPCC2023’s Chinese Grammatical Error Correction Task

Deep learning has shown remarkable effectiveness in various language tasks. This paper presents Huawei Translation Services Center’s (HW-TSC’s) work called HWCGEC which get the best performance among the seven submitted results in the NLPCC2023 shared task 1, namely Chinese grammatical error correction (CGEC). CGEC aims to automatically correct grammatical errors that violate language rules and converts the noisy input texts to clean output texts. This paper, through experiments, discovered that after model fine-tuning the BART a sequence to sequence (seq2seq) model performs better than the ChatGLM a large language model (LLM) in situations where training data is large while the LoRA mode has a smaller number of parameters for fine-tuning. Additionally, the BART model achieves good results in the CGEC task through data augmentation and curriculum learning methods. Although the performance of LLM is poor in experiments, they possess excellent logical abilities. With the training set becoming more diverse and the methods for training set data augmentation becoming more refined, the supervised fine-tuning (SFT) mode trained LLMs are expected to achieve significant improvements in CGEC tasks in the future.
A Two-Stage Chinese Medical Video Retrieval Framework with LLM

With the increasing popularity of online videos, research on video corpus retrieval (VCR) has made significant progress. However, existing VCR models have not performed well in the medical field due to the unique characteristics of medical VCR task. Specifically, the open-ended queries used in medical VCR are more challenging compared to image-caption style queries, and the long duration of medical videos poses a great burden on model retrieval efficiency. To address these challenges, we propose a two-stage framework based on GPT-3.5 and cross-modal contrastive global-span (CCGS) for medical video VCR (termed GPT-CMR). In the first stage, we leverage the powerful natural language processing capabilities of the large language model (LLM) GPT-3.5 to improve retrieval efficiency. In the second stage, we use CCGS model to further enhance retrieval accuracy. Additionally, we developed a CCGS-VCR Analyzer to leverage the characteristics of the CCGS model’s output without additional training costs. According to the official result, our method achieve first place in Track 2 of the NLPCC 2023 Task 5 competition. Experiments show that our method has retrieval efficiency and accuracy far exceeding the official baseline.
CCC: Chinese Commercial Contracts Dataset for Documents Layout Understanding

In recent years, Visual document understanding tasks have become increasingly popular due to the growing demand for commercial applications, especially for processing complex image documents such as contracts, and patents. However, there is no high-quality domain-specific dataset available except for English. And for other languages like Chinese, it is hard to utilize current English datasets due to the significant differences in writing norms and layout formats. To mitigate this issue, we introduce the Chinese Commercial Contracts (CCC) dataset to explore better visual document layout understanding modeling for Chinese commercial contract in the paper. This dataset contains 10,000 images, each containing various elements such as text, tables, seals, and handwriting. Moreover, we propose the Chinese Layout Understanding Pre-train Transformer (CLUPT) Model, which is pre-trained on the proposed CCC dataset by incorporating textual and layout information into the pre-train task. Based on the VisionEncoder-LanguageDecoder model structure, our model can perform end-to-end Chinese document layout understanding tasks. The data and code are available at https://github.com/yysirs/CLUPT .
UniER: A Unified and Efficient Entity-Relation Extraction Method with Single-Table Modeling

Joint entity and relation extraction are crucial tasks in natural language processing and knowledge graph construction. However, existing methods face several challenges. Firstly, they lack effective interaction modeling between entities and entity relations. Secondly, they often overlook the correlation between entities and relations. Lastly, the utilization of complex models results in increased memory usage. To tackle these challenges, we propose a unified approach that utilizes a homogeneous representation to model the interaction between subject and object entities, as well as entity relations, through a novel method of utilizing a single two-dimensional table for joint extraction tasks. This approach effectively handles multiple token entities, leading to faster and more efficient extraction. Our proposed approach achieves performance comparable to state-of-the-art models on two widely-used datasets and outperforms existing methods on the SEO and HTO problems, which are particularly challenging in relation extraction. It exhibits significant advantages in terms of both speed and space consumption. Our code and models are available at https://anonymous.4open.science/r/UniER.
Entity-Related Unsupervised Pretraining with Visual Prompts for Multimodal Aspect-Based Sentiment Analysis

Multimodal aspect-based sentiment analysis (MABSA) aims to extract aspect terms and predict their sentiments with the assistance of visual modalities. The main challenge of existing models lies in the alignment of visual and textual modalities. Recent studies have applied several task-specific supervised methods to enhance the alignment. Nevertheless, the supervised pretraining requires sufficient annotated samples, making it difficult to apply these methods in practice. This paper proposes an entity-related unsupervised pretraining with visual prompts for MABSA. Instead of using sentiment-related supervised pretraining, two entity-related unsupervised pretraining tasks are applied and compared, which are targeted at locating the entities in text with the support of visual prompts. The images are transformed into continuous prompt through a trainable adapter. Based on this, the model is trained to enhance the ability to quickly identify entities (aspects) and thus can reasonably classify the associated polarities with the visual prompts and textual information. The experimental results on two benchmarks show that the proposed method outperforms other baselines. (The code is available at: https://github.com/lkh-meredith/Entity-related-Unsupervised-Pretraining-with-Visual-Prompts-for-MABSA ).