This work is about the progress of previous related work based on an experiment to improve the intelligence of robotic systems,with the aim of achieving more linguistic communication capabilities between humans and ro...This work is about the progress of previous related work based on an experiment to improve the intelligence of robotic systems,with the aim of achieving more linguistic communication capabilities between humans and robots.In this paper,the authors attempt an algorithmic approach to natural language generation through hole semantics and by applying the OMAS-III computational model as a grammatical formalism.In the original work,a technical language is used,while in the later works,this has been replaced by a limited Greek natural language dictionary.This particular effort was made to give the evolving system the ability to ask questions,as well as the authors developed an initial dialogue system using these techniques.The results show that the use of these techniques the authors apply can give us a more sophisticated dialogue system in the future.展开更多
Both analyzing a large amount of space weather observed data and alleviating personal experience bias are significant challenges in generating artificial space weather forecast products.With the use of natural languag...Both analyzing a large amount of space weather observed data and alleviating personal experience bias are significant challenges in generating artificial space weather forecast products.With the use of natural language generation methods based on the sequence-to-sequence model,space weather forecast texts can be automatically generated.To conduct our generation tasks at a fine-grained level,a taxonomy of space weather phenomena based on descriptions is presented.Then,our MDH(Multi-Domain Hybrid)model is proposed for generating space weather summaries in two stages.This model is composed of three sequence-to-sequence-based deep neural network sub-models(one Bidirectional Auto-Regressive Transformers pre-trained model and two Transformer models).Then,to evaluate how well MDH performs,quality evaluation metrics based on two prevalent automatic metrics and our innovative human metric are presented.The comprehensive scores of the three summaries generating tasks on testing datasets are 70.87,93.50,and 92.69,respectively.The results suggest that MDH can generate space weather summaries with high accuracy and coherence,as well as suitable length,which can assist forecasters in generating high-quality space weather forecast products,despite the data being starved.展开更多
A simple recursive algorithm to generate the set of natural numbers, based on Mersenne numbers: M<sub>N</sub> = 2<sup>N</sup> – 1, is used to count the number of prime numbers within the preci...A simple recursive algorithm to generate the set of natural numbers, based on Mersenne numbers: M<sub>N</sub> = 2<sup>N</sup> – 1, is used to count the number of prime numbers within the precise Mersenne natural number intervals: [0;M<sub>N</sub>]. This permits the formulation of an extended twin prime conjecture. Moreover, it is found that the prime numbers subsets contained in Mersenne intervals have cardinalities strongly correlated with the corresponding Mersenne numbers.展开更多
The expert system is an important field of the artificial intelligence. The traditional interface of the expert system is the command, menu and window at present. It limits the application of the expert system and emb...The expert system is an important field of the artificial intelligence. The traditional interface of the expert system is the command, menu and window at present. It limits the application of the expert system and embarrasses the enthusiasm of using expert system. Combining with the study on the expert system of network fault diagnosis, the natural language interface of the expert system has been discussed in this article. This interface can understand and generate Chinese sentences. Using this interface, the user and field experts can use the expert system to diagnose the fault of network conveniently. In the article, first, the extended production rule has been proposed. Then the methods of Chinese sentence generation from conceptual graphs and the model of expert system are introduced in detail. Using this model, the network fault diagnosis expert system and its natural language interface have been developed with Prolog.展开更多
Liquefied natural gas (LNG), an increasingly widely applied clean fuel, releases a large number of cold energy in its regasification process. In the present paper, the existing power generation cycles utilizing LNG ...Liquefied natural gas (LNG), an increasingly widely applied clean fuel, releases a large number of cold energy in its regasification process. In the present paper, the existing power generation cycles utilizing LNG cold energy are introduced and summarized. The direction of cycle improvement can be divided into the key factors affecting basic power generation cycles and the structural enhancement of cycles utilizing LNG cold energy. The former includes the effects of LNG-side parameters, working fluids, and inlet and outlet thermodynamic parameters of equipment, while the latter is based on Rankine cycle, Brayton cycle, Kalina cycle and their compound cycles. In the present paper, the diversities of cryogenic power generation cycles utilizing LNG cold energy are discussed and analyzed. It is pointed out that further researches should focus on the selection and component matching of organic mixed working fluids and the combination of process simulation and experi- mental investigation, etc.展开更多
In recent years,deep neural network has achieved great success in solving many natural language processing tasks.Particularly,substantial progress has been made on neural text generation,which takes the linguistic and...In recent years,deep neural network has achieved great success in solving many natural language processing tasks.Particularly,substantial progress has been made on neural text generation,which takes the linguistic and non-linguistic input,and generates natural language text.This survey aims to provide an up-to-date synthesis of core tasks in neural text generation and the architectures adopted to handle these tasks,and draw attention to the challenges in neural text generation.We first outline the mainstream neural text generation frameworks,and then introduce datasets,advanced models and challenges of four core text generation tasks in detail,including AMR-to-text generation,data-to-text generation,and two text-to-text generation tasks(i.e.,text summarization and paraphrase generation).Finally,we present future research directions for neural text generation.This survey can be used as a guide and reference for researchers and practitioners in this area.展开更多
Multimodal sentence summarization(MMSS)is a new yet challenging task that aims to generate a concise summary of a long sentence and its corresponding image.Although existing methods have gained promising success in MM...Multimodal sentence summarization(MMSS)is a new yet challenging task that aims to generate a concise summary of a long sentence and its corresponding image.Although existing methods have gained promising success in MMSS,they overlook the powerful generation ability of generative pre-trained language models(GPLMs),which have shown to be effective in many text generation tasks.To fill this research gap,we propose to using GPLMs to promote the performance of MMSS.Notably,adopting GPLMs to solve MMSS inevitably faces two challenges:1)What fusion strategy should we use to inject visual information into GPLMs properly?2)How to keep the GPLM′s generation ability intact to the utmost extent when the visual feature is injected into the GPLM.To address these two challenges,we propose a vision enhanced generative pre-trained language model for MMSS,dubbed as Vision-GPLM.In Vision-GPLM,we obtain features of visual and textual modalities with two separate encoders and utilize a text decoder to produce a summary.In particular,we utilize multi-head attention to fuse the features extracted from visual and textual modalities to inject the visual feature into the GPLM.Meanwhile,we train Vision-GPLM in two stages:the vision-oriented pre-training stage and fine-tuning stage.In the vision-oriented pre-training stage,we particularly train the visual encoder by the masked language model task while the other components are frozen,aiming to obtain homogeneous representations of text and image.In the fine-tuning stage,we train all the components of Vision-GPLM by the MMSS task.Extensive experiments on a public MMSS dataset verify the superiority of our model over existing baselines.展开更多
Due to the significance and value in human-computer interaction and natural language processing,task-oriented dialog systems are attracting more and more attention in both academic and industrial communities.In this p...Due to the significance and value in human-computer interaction and natural language processing,task-oriented dialog systems are attracting more and more attention in both academic and industrial communities.In this paper,we survey recent advances and challenges in task-oriented dialog systems.We also discuss three critical topics for task-oriented dialog systems:(1)improving data efficiency to facilitate dialog modeling in low-resource settings,(2)modeling multi-turn dynamics for dialog policy learning to achieve better task-completion performance,and(3)integrating domain ontology knowledge into the dialog model.Besides,we review the recent progresses in dialog evaluation and some widely-used corpora.We believe that this survey,though incomplete,can shed a light on future research in task-oriented dialog systems.展开更多
Bivariate map visualizations use different encodings to visualize two variables but comparison across multiple encodings is challenging.Compared to a univariate visualization,it is significantly harder to read regiona...Bivariate map visualizations use different encodings to visualize two variables but comparison across multiple encodings is challenging.Compared to a univariate visualization,it is significantly harder to read regional differences and spot geographical outliers.Especially targeting inexperienced users of visualizations,we advocate the use of natural language text for augmenting map visualizations and understanding the relationship between two geo-statistical variables.We propose an approach that selects interesting findings from data analysis,generates a respective text and visualization,and integrates both into a single document.The generated reports interactively link the visualization with the textual narrative.Users can get additional explanations and have the ability to compare different regions.The text generation process is flexible and adapts to various geographical and contextual settings based on small sets of parameters.We showcase this flexibility through a number of application examples.展开更多
One of the major challenges to build a task-oriented dialogue system is that dialogue state transition frequently happens between multiple domains such as booking hotels or restaurants.Recently,the encoder-decoder mod...One of the major challenges to build a task-oriented dialogue system is that dialogue state transition frequently happens between multiple domains such as booking hotels or restaurants.Recently,the encoder-decoder model based on the end-to-end neural network has become an attractive approach to meet this challenge.However,it usually requires a sufficiently large amount of training data and it is not flexible to handle dialogue state transition.This paper addresses these problems by proposing a simple but practical framework called Multi-Domain KB-BOT(MDKB-BOT),which leverages both neural networks and rule-based strategy in natural language understanding(NLU)and dialogue management(DM).Experiments on the data set of the Chinese Human-Computer Dialogue Technology Evaluation Campaign show that MDKB-BOT achieves competitive performance on several evaluation metrics,including task completion rate and user satisfaction.展开更多
文摘This work is about the progress of previous related work based on an experiment to improve the intelligence of robotic systems,with the aim of achieving more linguistic communication capabilities between humans and robots.In this paper,the authors attempt an algorithmic approach to natural language generation through hole semantics and by applying the OMAS-III computational model as a grammatical formalism.In the original work,a technical language is used,while in the later works,this has been replaced by a limited Greek natural language dictionary.This particular effort was made to give the evolving system the ability to ask questions,as well as the authors developed an initial dialogue system using these techniques.The results show that the use of these techniques the authors apply can give us a more sophisticated dialogue system in the future.
基金Supported by the Key Research Program of the Chinese Academy of Sciences(ZDRE-KT-2021-3)。
文摘Both analyzing a large amount of space weather observed data and alleviating personal experience bias are significant challenges in generating artificial space weather forecast products.With the use of natural language generation methods based on the sequence-to-sequence model,space weather forecast texts can be automatically generated.To conduct our generation tasks at a fine-grained level,a taxonomy of space weather phenomena based on descriptions is presented.Then,our MDH(Multi-Domain Hybrid)model is proposed for generating space weather summaries in two stages.This model is composed of three sequence-to-sequence-based deep neural network sub-models(one Bidirectional Auto-Regressive Transformers pre-trained model and two Transformer models).Then,to evaluate how well MDH performs,quality evaluation metrics based on two prevalent automatic metrics and our innovative human metric are presented.The comprehensive scores of the three summaries generating tasks on testing datasets are 70.87,93.50,and 92.69,respectively.The results suggest that MDH can generate space weather summaries with high accuracy and coherence,as well as suitable length,which can assist forecasters in generating high-quality space weather forecast products,despite the data being starved.
文摘A simple recursive algorithm to generate the set of natural numbers, based on Mersenne numbers: M<sub>N</sub> = 2<sup>N</sup> – 1, is used to count the number of prime numbers within the precise Mersenne natural number intervals: [0;M<sub>N</sub>]. This permits the formulation of an extended twin prime conjecture. Moreover, it is found that the prime numbers subsets contained in Mersenne intervals have cardinalities strongly correlated with the corresponding Mersenne numbers.
基金This work was supported by the National Natural Science Foundation of China (No.60173066) .
文摘The expert system is an important field of the artificial intelligence. The traditional interface of the expert system is the command, menu and window at present. It limits the application of the expert system and embarrasses the enthusiasm of using expert system. Combining with the study on the expert system of network fault diagnosis, the natural language interface of the expert system has been discussed in this article. This interface can understand and generate Chinese sentences. Using this interface, the user and field experts can use the expert system to diagnose the fault of network conveniently. In the article, first, the extended production rule has been proposed. Then the methods of Chinese sentence generation from conceptual graphs and the model of expert system are introduced in detail. Using this model, the network fault diagnosis expert system and its natural language interface have been developed with Prolog.
文摘Liquefied natural gas (LNG), an increasingly widely applied clean fuel, releases a large number of cold energy in its regasification process. In the present paper, the existing power generation cycles utilizing LNG cold energy are introduced and summarized. The direction of cycle improvement can be divided into the key factors affecting basic power generation cycles and the structural enhancement of cycles utilizing LNG cold energy. The former includes the effects of LNG-side parameters, working fluids, and inlet and outlet thermodynamic parameters of equipment, while the latter is based on Rankine cycle, Brayton cycle, Kalina cycle and their compound cycles. In the present paper, the diversities of cryogenic power generation cycles utilizing LNG cold energy are discussed and analyzed. It is pointed out that further researches should focus on the selection and component matching of organic mixed working fluids and the combination of process simulation and experi- mental investigation, etc.
基金the National Natural Science Foundation of China(Grant No.61772036)the Key Laboratory of Science,Technology and Standard in Press Industry(Key Laboratory of Intelligent Press Media Technology)。
文摘In recent years,deep neural network has achieved great success in solving many natural language processing tasks.Particularly,substantial progress has been made on neural text generation,which takes the linguistic and non-linguistic input,and generates natural language text.This survey aims to provide an up-to-date synthesis of core tasks in neural text generation and the architectures adopted to handle these tasks,and draw attention to the challenges in neural text generation.We first outline the mainstream neural text generation frameworks,and then introduce datasets,advanced models and challenges of four core text generation tasks in detail,including AMR-to-text generation,data-to-text generation,and two text-to-text generation tasks(i.e.,text summarization and paraphrase generation).Finally,we present future research directions for neural text generation.This survey can be used as a guide and reference for researchers and practitioners in this area.
文摘Multimodal sentence summarization(MMSS)is a new yet challenging task that aims to generate a concise summary of a long sentence and its corresponding image.Although existing methods have gained promising success in MMSS,they overlook the powerful generation ability of generative pre-trained language models(GPLMs),which have shown to be effective in many text generation tasks.To fill this research gap,we propose to using GPLMs to promote the performance of MMSS.Notably,adopting GPLMs to solve MMSS inevitably faces two challenges:1)What fusion strategy should we use to inject visual information into GPLMs properly?2)How to keep the GPLM′s generation ability intact to the utmost extent when the visual feature is injected into the GPLM.To address these two challenges,we propose a vision enhanced generative pre-trained language model for MMSS,dubbed as Vision-GPLM.In Vision-GPLM,we obtain features of visual and textual modalities with two separate encoders and utilize a text decoder to produce a summary.In particular,we utilize multi-head attention to fuse the features extracted from visual and textual modalities to inject the visual feature into the GPLM.Meanwhile,we train Vision-GPLM in two stages:the vision-oriented pre-training stage and fine-tuning stage.In the vision-oriented pre-training stage,we particularly train the visual encoder by the masked language model task while the other components are frozen,aiming to obtain homogeneous representations of text and image.In the fine-tuning stage,we train all the components of Vision-GPLM by the MMSS task.Extensive experiments on a public MMSS dataset verify the superiority of our model over existing baselines.
基金the National Natural Science Foundation of China(Grant Nos.61936010 and 61876096)the National Key R&D Program of China(Grant No.2018YFC0830200)。
文摘Due to the significance and value in human-computer interaction and natural language processing,task-oriented dialog systems are attracting more and more attention in both academic and industrial communities.In this paper,we survey recent advances and challenges in task-oriented dialog systems.We also discuss three critical topics for task-oriented dialog systems:(1)improving data efficiency to facilitate dialog modeling in low-resource settings,(2)modeling multi-turn dynamics for dialog policy learning to achieve better task-completion performance,and(3)integrating domain ontology knowledge into the dialog model.Besides,we review the recent progresses in dialog evaluation and some widely-used corpora.We believe that this survey,though incomplete,can shed a light on future research in task-oriented dialog systems.
文摘Bivariate map visualizations use different encodings to visualize two variables but comparison across multiple encodings is challenging.Compared to a univariate visualization,it is significantly harder to read regional differences and spot geographical outliers.Especially targeting inexperienced users of visualizations,we advocate the use of natural language text for augmenting map visualizations and understanding the relationship between two geo-statistical variables.We propose an approach that selects interesting findings from data analysis,generates a respective text and visualization,and integrates both into a single document.The generated reports interactively link the visualization with the textual narrative.Users can get additional explanations and have the ability to compare different regions.The text generation process is flexible and adapts to various geographical and contextual settings based on small sets of parameters.We showcase this flexibility through a number of application examples.
基金This work was supported by Beijing Natural Science Foundation(No.4174098)National Natural Science Foundation of China(No.61702047)the Fundamental Research Funds for the Central Universities(No.2017RC02).
文摘One of the major challenges to build a task-oriented dialogue system is that dialogue state transition frequently happens between multiple domains such as booking hotels or restaurants.Recently,the encoder-decoder model based on the end-to-end neural network has become an attractive approach to meet this challenge.However,it usually requires a sufficiently large amount of training data and it is not flexible to handle dialogue state transition.This paper addresses these problems by proposing a simple but practical framework called Multi-Domain KB-BOT(MDKB-BOT),which leverages both neural networks and rule-based strategy in natural language understanding(NLU)and dialogue management(DM).Experiments on the data set of the Chinese Human-Computer Dialogue Technology Evaluation Campaign show that MDKB-BOT achieves competitive performance on several evaluation metrics,including task completion rate and user satisfaction.