Reviewerly/exHarmony huggingface dataset:
Research on the Evaluation of the Classical Chinese Difficulty in the Compulsory Education Stage

With the continuous emphasis on the learning of traditional cultural classics in the Chinese textbooks compiled by the Ministry of Education, the research on the selection and teaching of classical Chinese in the compulsory education stage will have an unprecedented special position in the research of Chinese teaching in recent years. This paper explores the difficulty evaluation characteristics of classical Chinese in the compulsory education stage, proposes a method to automatically analyze and examine the difficulty of classical Chinese texts by synthesizing multi-level language features, builds a corpus containing about 500 classical Chinese texts with annotated language characters, describes the textual features of classical Chinese from the three dimensions of word, syntax and text, constructed an indicator system with 17 categories, 30 language features for classical Chinese readability assessment tasks for the readability evaluation of classical Chinese texts, basically confirms that these category factors and text characteristics can affect learners' reading comprehension of classical Chinese.
Automatic Generation and Evaluation of Chinese Grammar Proofreading Corpus

The automatic proofreading technol-ogy of Chinese text is one of the important appli-cation technologies of natural language processing. One of the current challenges of this task is that there is not enough labeled data for model training. In order to solve the problem of insufficient proofreading data, this paper proposes a method for automatically generating Chinese text proofreading corpus. When building a grammar proofing corpus, there are four types of grammar errors, including redundant errors, missing errors, selection errors, and word ordering errors. In order to obtain higher quality labeled data, our paper uses machine trans-lation methods to generate redundant errors, missing errors and word ordering errors setences; our paper uses related methods of proofreading corpora to generate selection errors sentences, including Chinese characters to Pinyin, Pinyin to Chinese characters, and OCR technology; in addition, our paper also uses a rule-based method to generate a grammar proofing corpus, i.e. randomly replace, add, delete or reverse 10% of the characters in the sentence. When evaluating the generated grammar proofreading corpus, a statistical-based method and a grammar-based error detection method were used respectively. The statistical-based method proved that the generated corpus was closer to the real errors. Experiments show that on the published grammar proofreading data NLPTEA2018, the F1 value of the corpus constructed in this paper is improved by about 5.7%. Therefore, the corpus data generated in this paper can better simulate the real error sentences, which is of great help to the Chinese text proofreading task.
Constraint-based Paraphrase Generation Model and it's Application on Message Extraction of Commodity Futures News

The task of message extraction based on the Chinese futures domain is to extract the messages that may affect the futures market from the futures news or comment texts. In this work, we construct a Futures Messages Extraction Dataset (FMED) by extracting message-related entities from the Corpus for Entity and Relationship in Futures domain (CERF) and propose a message extraction model MGCC based on paraphrase generation. We define prompt templates for this task and use the pre-trained model mT5. In order to improve the model's performance, the constraint and the classifier are used to generate the target sentences. The final precision, recall and F1 values in the validation set are 0.732, 0.733 and 0.732, respectively.
Petroleum Exploration and Development Text Triplet Extraction Based on Deep Learning

The petroleum exploration and development industry is moving from digital to intelligent. Under the guidance of AI, machine reading and automatic extraction of petroleum exploration and development knowledge are needed for unstructured datas. There are a large number of long entities and complex nested entities in petroleum exploration and development text, which increase the challenge of petroleum exploration and development triplet extraction task. To solve the above problems, (1) The ALBERT-BiLSTM-Attention-CRF method based on large-scale pre-trained Chinese language model is used to extract text triples for Petroleum exploration and development. (2) The ALBERT-BiGRU-Attention method is used to carry out text dichotomies to judge whether triplet extraction is effective. By collecting the datas of Petroleum exploration and development, the corpus of Petroleum exploration and development is established. Secondly, knowledge composition is analyzed and corpus is annotated under the guidance of Petroleum exploration and development theory. Finally, the deep learning training of Petroleum exploration and development descriptive corpus knowledge extraction is carried out. The experimental results show that the accuracy of SPO triplet named entity recognition is 85.49%. The recognition accuracy of extracted triples is 95.90%, which can achieve good recognition effect in small-scale Petroleum exploration and development corpus.
Tapping the Potential of Coherence and Syntactic Features in Neural Models for Automatic Essay Scoring

In the prompt-specific holistic score prediction task for Automatic Essay Scoring, the general approaches include pre-trained neural model, coherence model, and hybrid model that incorporate syntactic features with neural model. In this paper, we propose a novel approach to extract and represent essay coherence features with prompt-learning NSP that shows to match the state-of-the-art AES coherence model, and achieves the best performance for long essays. We apply syntactic feature dense embedding to augment BERT-based model and achieve the best performance for hybrid methodology for AES. In addition, we explore various ideas to combine coherence, syntactic information and semantic embeddings, which no previous study has done before. Our combined model also performs better than the SOTA available for combined model, even though it does not outperform our syntactic enhanced neural model. We further offer analyses that can be useful for future study.
Joint Training Model of Intent Detection and Slot Filling for Multi Granularity Implicit Guidance

Spoken Language Understanding (SLU), which includes intent detection and slot filling, is an important module in task-based dialogue system. Most traditional models only take single intent and guide the slot filling explicitly. However, in a real conversation scenario, users may have more than one intentions in a sentence. In this paper, we propose an implicit guidance framework for joint intent detection and slot filling. We use token-level and sentence-level intent information to guide slot filling. The token level realizes fine-grained intent guidance, and the sentence level realizes coarse-grained intent guidance. The experimental results show that compared with the baseline model, our model improves the accuracy of sentence-level semantic framework on both multiple intents dataset MixSNIPS and single intent dataset SNIPS by 1.2% and 0.7%, respectively.
Answer Selection Method Based on BERT and Parallel Multi-Channel Convolution

Aiming the problem that insufficient attention to the feature information of existing answer selection algorithms, we proposed a model B-PC which based on BERT and parallel multi-channel convolutional neural network. Firstly, using BERT to get the global feature information of question-answer pair. Then, using parallel multi-channel CNN to obtain the local feature information. Finally, fusing the global and local feature information, and using fully connected neural network to calculate the score. Experimental results show that B-PC can effectively improve the effect of answer selection. On the Wiki-QA data set, the MAP is 4.2% higher than the BERT-LSTM-Attention and 9.3% higher than RE2.
End-to-End Myanmar Speech Recognition with Human-Machine Cooperation

End-to-end automatic speech recognition based on deep neural networks has achieved satisfactory results in some languages with large-scale trainable data. Due to the small scale of available training data, there is still a significant gap between Myanmar speech recognition and application needs. Based on the end-to-end model of Transformer, this paper studies the influence of character, byte pair encoding (BPE), and syllable label encoding on recognition rate. For the Myanmar speech recognition model in a low-resource environment, we propose and implement two training methods: (1) The self-supervised pre-training speech representations model based on massive English data is introduced into the Myanmar speech recognition model as an acoustic feature extractor. The experimental results show that the pre-training speech representations method has an obvious effect on end-to-end Myanmar speech recognition. (2) Expand the scale of the database using human-machine cooperation, to improve the speech recognition rate. Based on the method (1), through a human-machine cooperation experiment, 2898 new corpora were obtained after 6 iterations. The character error rate of the Myanmar speech recognition model on the development set decreased from 4.8% to 4.1%, and the character error rate of the test set decreased from 5.5% to 4.2%. The time required for manual proofreading of each batch of the speech corpus has decreased from 19.1 hours to 16.3 hours. Experiments have proved the effectiveness of the human-machine cooperation method.
An Adaptive Session Selection Method for Recommendation System

Predicting user's next action based on anonymous session is a challenging problem in session-based recommendation. Recent advances utilize attention mechanism and graph neural network to achieve excellent performance in session-based recommendation. However, most of these studies ignore the characteristics of different types of sessions, which results in user's preference from each session not being captured by the most suitable model. In this paper, we propose an adaptive session selection method for the session-based recommendation called ASSM to address these issues. In ASSM, sessions are adaptively distinguished into frequent sessions and infrequent sessions based on whether they contain high frequency items. Then a graph structure is constructed for frequent session to learn complex item transition and obtain user's local preference via graph neural network. At the same time, an improved attention network is applied to capture user's global preference from infrequent session. We conduct extensive experiments on two real-world datasets and the results demonstrate the effectiveness of our method.
NEB-Filter: A Simple but Effective Filter based on Named Entity Boundaries for Low-Resource Cross-Lingual NER

Named Entity Recognition (NER) on low-resource languages remains a challenging task due to the scarcity of labeled data. With the advent of large-scale neural machine translation systems and multilingual pre-training models, it has become possible to transfer knowledge from the resource-rich source language side (e.g., English) to the resource-poor target language side. In this paper, we focus on scenarios where the source language side labeled data is also insufficient. We found that transfer entity boundary information alone cross the languages is much easier than transfer the whole labeled information, which includes not only the entity boundary but also the entity category. Therefore, we propose a simple but effective cross-lingual NER method called NEB-Filter for the first time. NEB-Filter trains a filter based on the boundaries of named entities, which can greatly improve the precision of the cross-lingual NER model, but only brings a small reduction in recall. Moreover, our method has been shown to benefit from knowledge distillation (KD), leading to greater improvements on F1 score. A series of experiments have shown encouraging results for our approach in low-resource cross-lingual NER.
Reliablization of Fall Recognition via Morphological Analysis

Aiming at the shortcomings of existing fall recognition methods such as weak adaptability and less reliable recognition rate in some scenes, this paper proposes a method to improve the reliability of fall recognition via morphological analysis. This method processes the video obtained from the camera to gain the human skeleton data for morphological analysis. Firstly, it calculates the aspect ratio of the human detection frame. Secondly, because the human body falls at a certain speed, this method computes the speed of human movement. Thirdly, it calculates the relative distance between the buttocks and the heel of the human body. And then because of the continuity of the fall, the reliability of fall recognition can be improved by eliminating the false alarm. If all the above conditions are satisfied, it can be considered a fall. The experiment result shows that the method in this paper is more reliable for the recognition of fall movements.
Globally Consistent Image Inpainting based on WGAN-GP Network optimization

There have been many methods applied to image inpainting. Although these algorithms can roughly produce visually plausible image structure and texture, they also create a lot of chaotic structural information and blurry texture details, resulting in inconsistencies with the surrounding content area. This paper proposes a globally consistent image inpainting network with a nonlocal module based on WGAN-GP optimization. It can make the network obtain the relevant information on long-distance dependence without superimposing network layers. And it is also able to prevent the limitations such as inefficient calculation and complex optimization caused by the local operation of the convolutional neural network. Thus making full use of the surrounding information of the area to be repaired will improve the semantic and structural consistency of generating predictions with the entire background area. Experiments with this model are conducted on a Places2 dataset, and the results prove that our method was superior to ordinary convolutional neural networks.
An Investigation of Magnitude-Based and Phase-Based Features for Large-Scale Speaker Identification

Most of the current speaker identification (SI) methods focus on the use of magnitude-based acoustic features, such as fbank and MFCC. Previous studies have shown that phase-based features also contain speaker-specific information and have comparable system performance with magnitude-based features. However, these studies were conducted on small datasets, and the performance of SI systems obtained from actual large-scale speech data is still unclear. To address this issue, this paper compared the performance of the phase-based system with the magnitude-based system on a real-world, large-scale Mandarin SI dataset. We also explored three distinct fusion strategies (frame/embedding/decision level) to capture the deep relationship between the two features. Experimental results showed that the phase-based system outperformed the magnitude-based system by Top-1 error rate reduction of 13.11% relative on a test set of 200,000 speakers. In addition, the SI system using decision-level fusion method achieved the best performance on the same test dataset, and outperformed the magnitude-based/phase-based baseline systems by Top-1 error rate reduction of 18.5%/6.2% relative.
Applying Emotional Keyphrase Correlation for Diversity Enhancement in Empathetic Dialogue Response Generation

Developing an empathetic dialogue system has been facing challenges for years. Although most of the responses generated from the existing empathetic dialogue systems are to some extent grammatical and emotional, they are still far from human standards. The system-generated responses are usually uncorrelated to the user's emotional contexts. In this paper, we propose an approach to automatic extraction of the emotional keyphrases from the context, then the emotional keyphrases are fed into the Conditional Transformer language model as the conditional information. We also design a loss function called Keyphrase Correlation (KPC) to guide the model to generate responses with relevant emotional keyphrases. As a result, the diversity of the generated responses is highly enhanced. The experimental results also verified the effectiveness in terms of empathy capability and emotional correlation in the user-system conversation.
An Improved Algorithm of Word Semantic Similarity Based on HowNet

Word semantic similarity calculation has been widely used in information retrieval, text classification, data similarity detection and other fields. However, the commonly used HowNet based word semantic similarity calculation method does not consider the density information of the minimum common parent node and some of the first basic sememes do not participate in the calculation when there is an inclusion or intersection relationship in the semantics, resulting in the calculation results not conforming to the reality and inaccurate, thus limiting its application. To solve this problem, we improve the edge weight function and the word similarity calculation function between nodes. The density of minimum common parent node is considered into the edge weight function. In order to deal with the inclusion and intersection problems, we consider the weight of the number of sememes participating in the calculation and the proportion of the number of first sememes not participating in the calculation to the total number of first basic sememes. The experimental results show that the improved semantic similarity algorithm can effectively improve the accuracy of the sememe similarity calculation, and can solve the problem that the result of the word similarity calculation is too large when the words are included or intersected.
Lost-Found Item Net for Classification Based on Inception-Resnet

A novel Lost-Found item net has been introduced in our paper. This net is help to classify the types of items in the clutter which collected in one train and waiting area of railway station. The objective of this net is contributing to a more efficient, comfortable ambiance for travelers. This paper develops and evaluates a rapid, low-cost system for item classification. Firstly, a high-quality lost-found items classification dataset is created, named Lost-Found Item Dataset (LFID). Secondly, our paper is introduced an improved architecture named inception-Resnet which is for the classification to recognize the item by extracting and learning some critical feature. In order to enhance the performance of our network, some significant data augmentation methods are applied to preprocess the item images. Through these novel technologies the important parameters of this model could be learned abundantly tend to generalization. Furtherly, a few experiments have been carried out and these performances have been discussed using this above dataset. The performance of our model has been well described and introduced. Finally, these results are compared with the most advanced methods available. The comparison with the popular methods shows the superiority of the novel model.
Detection algorithm of frequency hopping signals based on S Transform and Deep Learning

Aiming at problems of low time-frequency resolution and easy spectrum leakage of the traditional detection method of frequency hopping signal based on spectrogram, a frequency hopping signal detection algorithm based on S-transform (ST) and deep learning (DL) is proposed. Firstly, the time-frequency spectrum is obtained by performing S-transformation on the received signal. The time-frequency spectrum is normalized which makes it robust to noise power uncertainty and is used as input.Secodly, a convolutional neural network (CNN) structure is designed. The network directly extracts the time-frequency features of the signal and Gaussian noise, so as to realize the detection of frequency hopping signal. Lastly, simulation results show when the SNR is-10dB, the detection probability can reach more than 0.9, so the proposed detection algorithm has better detection performance in low signal-to-noise ratio(SNR).
Memory Enhanced Replay for Continual Learning

Currently, artificial intelligence can not continually learn the new knowledge and retain the acquired knowledge in the past like human beings. It is affected by a phenomenon called catastrophic forgetting. Although continual learning based on generative replay could alleviate the problem to a certain extent without accessing the past data, it utilizes not fully potentials of the framework. Inspired by the mechanism of the interaction between the brain hippocampus and neocortex, we propose a novel memory enhanced replay networks (MER) based on generative replay for continual learning. This is a generative adversarial networks (GANs) integrating memory enhancement module. Specifically, the method adopts GANs to simulate the learning and memory process in the brain and add a memory enhancement module in the final process of training. This approach allows the network to relieve the uneven distribution of tasks in the previous sequence training, so as to better preserve the past learned knowledge. Class-incremental experiments show that the proposed framework is able to promote knowledge consolidation after training and has a competitive result compared with other advanced methods.
Morphological Validation of Animal-Skeleton Estimation Result

In this study, we use a morphological analysis-based method for the time sequence of animal-skeleton estimation, which helps recognize bad estimation and split the video accordingly. Signed areas of four polygon regions indicated by the animal’s four legs are used to describe the motion pattern of an animal’s legs. The nine signed areas have a significant change pattern under the given action, while unconventional abrupt changes in the time series indicate serious errors in skeleton identification, such as identifying the front left leg of the animal as the front right leg. Therefore, we can analyze these area changes to identify the wrong frames and use them as a basis for segmenting long segments of video. The experimental result verifies the validation of the analysis method.
Pretrained Models with Adversarial Training for Named Entity Recognition in Scientific Text

Named entity recognition (NER) is an important fundamental task in natural language processing (NLP). This paper describes a method for named entity recognition based on pretrained models and adversarial training in scientific text. The scientific entity recognition task requires the model to identify 7 different scientific term entities. There are some issues with a given dataset, such as imbalanced label classes, excessively long entity bounds, and inconsistent entity labeling. To address these issues, we proposed to use focal loss instead of existing cross-entropy loss. Further, we used one of the common adversarial training methods, i.e., Fast Gradient Method (FGM) to perform semi-supervised NER. The experimental results show that our adversarial training method considerably enhances the performance of the model, and the method used in this study achieves the highest F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -score of 88.92%. Moreover, our results also prove that SciBERT is better suited to the task of named entity recognition in scientific text and that the focal loss successfully solves the problem of data imbalance.