In recent years, considerable research has been dedicated to the application of neural models in the field of natural language generation (NLG). The primary objective is to generate text that is both linguistically natural and human-like, while also exerting control over the generation process. This paper offers a comprehensive and task-agnostic survey of the recent advancements in neural text generation. These advancements have been facilitated through a multitude of developments, which we categorize into four key areas: data construction, neural frameworks, training and inference strategies, and evaluation metrics. By examining these different aspects, we aim to provide a holistic overview of the progress made in the field. Furthermore, we explore the future directions for the advancement of neural text generation, which encompass the utilization of neural pipelines and the incorporation of background knowledge. These avenues present promising opportunities to further enhance the capabilities of NLG systems. Overall, this survey serves to consolidate the current state of the art in neural text generation and highlights potential avenues for future research and development in this dynamic field.

Recent Advances in Neural Text Generation: A Task-Agnostic Survey

Acknowledged as one of the most successful online cooperative projects in human society, Wikipedia has obtained rapid growth in recent years and desires continuously to expand content and disseminate knowledge values for everyone globally. The shortage of volunteers brings to Wikipedia many issues, including developing content for over 300 languages at the present. Therefore, the benefit that machines can automatically generate content to reduce human efforts on Wikipedia language projects could be considerable. In this paper, we propose our mapping process for the task of converting Wikidata statements to natural language text (WS2T) for Wikipedia projects at the sentence level. The main step is to organize statements, represented as a group of quadruples and triples, and then to map them to corresponding sentences in English Wikipedia. We evaluate the output corpus in various aspects: sentence structure analysis, noise filtering, and relationships between sentence components based on word embedding models. The results are helpful not only for the data-to-text generation task but also for other relevant works in the field.

Mapping Process for the Task: Wikidata Statements to Text as Wikipedia Sentences

The invention relates to the technical field of artificial intelligence, and provides an intelligent medication question-answering method based on a medication knowledge graph and related equipment. The intelligent medication question-answering method based on a medication knowledge graph comprises the steps of creating the medication knowledge graph; obtaining inquiry voice information of a userfor the medication knowledge; converting the inquiry voice information into inquiry text information; performing drug entity identification on the inquiry text information to obtain a medication entity; performing intention identification on the inquiry text information to obtain an inquiry intention; searching answer text information from the drug knowledge graph according to the medication entity and the inquiry intention; synthesizing answer voice information according to the answer text information; and storing and playing the answer voice information to answer the inquiry voice information. The invention is used for answering the drug voice questions of the user, and the efficiency and accuracy of intelligent medication question-answering are improved.

Intelligent medication question-answering method based on medicine knowledge graph and equipment thereof

Neural network (NN) based data2text models achieve state-of-the-art (SOTA) performance in most metrics, but they sometimes drop or modify the information in the input, and it is hard to control the generation contents. Moreover, it requires paired training data that are usually expensive to collect. Template-based methods have good fidelity and controllability but require heavy human involvement. We propose a novel template-based data2text system powered by a text stitch model. It ensures fidelity and controllability by using templates to produce the main contents. In addition, it reduces human involvement in template design by using a text stitch model to automatically stitch adjacent template units, which is a step that usually requires careful template design and limits template reusability. The text stitch model can be trained in self-supervised fashion, which only requires free texts. The experiments on a benchmark dataset show that our system outperforms SOTA NN-based systems in fidelity and surpasses template-based systems in diversity and human involvement.

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Make Templates Smarter: A Template Based Data2Text System Powered by Text Stitch Model

The invention relates to artificial intelligence, and provides an intelligent question-answering method and device based on a knowledge graph, and computer equipment, and the method comprises the steps: receiving a question text online; extracting a first entity in the question text; searching a first node corresponding to the first entity in a preset knowledge graph; utilizing a preset intentionrecognition rule engine to recognize a first intention of the question text, the intention recognition rule engine being an engine established according to a Trie tree; searching a first answer corresponding to the question text in the knowledge graph by taking the first node as a starting point according to the first intention; and feeding back the first answer according to a preset mode. When intelligent questioning and answering are realized, intention recognition can be quickly carried out through an intention recognition rule engine established based on a Trie tree, and the feedback speedof a questioning and answering system is increased. The natural language is mapped to the knowledge graph for structured query, the query speed is higher, and the result is more accurate.

Intelligent question and answer method and device based on knowledge graph and computer equipment

Aspect-based sentiment analysis (ABSA) aims to identify the opinion polarity towards a specific aspect. Traditional approaches formulate ABSA as a sentence classification task. However, it is observed that the single sentence classification paradigm cannot take full advantage of pre-trained language models. Previous work suggests it is better to cast ABSA as a question answering (QA) task for each aspect, which can be solved in the sentence-pair classification paradigm. Though QA-style ABSA achieves state-of-the-art (SOTA) results, it naturally separates the prediction process of multiple aspects belonging to the same sentence. It thus is unable to take full advantage of the correlation between different aspects. In this paper, we propose to use the global-perspective (GP) question to replace the original question in QA-style ABSA, which explicitly tells the model the existence of other relevant aspects using additional instructions. In this way, the model can distinguish relevant phrases for each aspect better and utilize the underlying relationship between different aspects. The experimental results on three benchmark ABSA datasets demonstrate the effectiveness of our method.

Bai Zuo

Papers

Make Templates Smarter: A Template Based Data2Text System Powered by Text Stitch Model

Intelligent question and answer method and device based on knowledge graph and computer equipment

From Shortsighted to Bird View: Jointly Capturing All Aspects for Question-Answering Style Aspect-Based Sentiment Analysis