Генерация естественного языка

1 Efficient natural language generation has been successfully demonstrated using highly compiled knowledge about speech acts and their related social actions. A design and prototype implementation of a parser which …...

Large-scale natural language generation re- quires the integration of vast mounts of knowledge: lexical, grammatical, and concep- tual. A robust generator must be able to operate well even when pieces …...

What al+c the benefits of ttsing Natural [~anguagc (;cneratio,t in an industrial apl+lication? We have attempt t<) answer part (}f this qttcsti{}n with at descripti(}n of an assessment {}f three …...

We. present a study of the mappings from semantic content to syntactic ex- pression with the aim of isolating the precise locus and role of pragmatic infor- mation in the …...

This paper presents a method for the au- tomatic extraction of subgrammars to con- trol and speeding-up natural language gen- eration NLG. The method is based on explanation-based learning EBL. …...

In this paper, we describe a method for automatic creation of a knowledge source for text generation using information ex- traction over the Internet. We present a prototype system called …...

CogentHelp is a prototype tool for au- thoring dynamically generated on-line help for applications with graphical user inter- faces, embodying the "evolution-friendly" properties of tools in the literate program- ming …...

Starting from descriptions of French connectives (in particular "done"--therefore), on the one hand, and aspectual properties of French tenses passd simple and imparfait on the other hand, we study in …...

We present and evaluate the initial version of RIPTIDES, a system that combines information extraction, extraction-based summarization, and natural language generation to support user- directed multidocument summarization. 1....

A standard architecture for an NLG system has been defined in (Reiter and Dale, 2000). Their work describes the modularization of an NLG system and the tasks of each module. …...

An important component of any generation system is the mapping dictionary, a lexicon of elementary semantic expressions and cor- responding natural language realizations. Typically, labor-intensive knowledge-based methods are used to …...

Natural-Language Generation from flat semantics is an NP-complete problem. This makes it necessary to develop al- gorithms that run with reasonable effi- ciency in practice despite the high worst- case …...

We consider the problem of parsing non-recursive context-free grammars, i.e., context-free grammars that generate finite languages. In natural language process- ing, this problem arises in several areas of application, including …...

. Corpus-based stochastic language models have achieved significant success in speech recognition, but construction of a corpus pertaining to a specific application is a difficult task. This paper introduces a …...

In this paper we present a new approach to controlling the behaviour of a natural lan- guage generation system by correlating in- ternal decisions taken during free generation of a …...

We consider the evaluation problem in Natural Language Generation (NLG) and present results for evaluating several NLG systems with similar functionality, includ- ing a knowledge-based generator and sev- eral statistical …...

. This paper is a study on constructing a natural language interface to database, concentrating on generating textual answers. TGEN, a system that generates textual answer from query result tables …...

This paper explores the use of statisti- cal machine translation (SMT) methods for tactical natural language generation. We present results on using phrase-based SMT for learning to map meaning repre- …...

. Natural Language Generation (NLG) techniques can be applied in generating virtual documents dynamically using information from a database (Dale et al, 1999). One of the applications of NLG techniques …...

We present and evaluate a new model for Natural Language Generation (NLG) in Spoken Dialogue Systems, based on statis- tical planning, given noisy feedback from the current generation context (e.g. …...

An important question in the evaluation of Natu- ral Language Generation systems concerns the re- lationship between textual characteristics and task performance. If the results of task-based evalua- tion can …...

We present a natural language genera- tion approach which models, exploits, and manipulates the non-linguistic context in situated communication, using techniques from AI planning. We show how to gen- erate …...

One goal of natural language generation is to produce coherent text that presents in- formation in a logical order. In this pa- per, we show that topological fields, which model …...

AbstractIn Natural Language Generation (NLG), template-based surface realisation is an effective solution to the problem of producing surface strings from a given semantic representation, but many applications may not be …...

AbstractSINotas is a data-to-text NLG application intended to produce short textual reports on students’ academic performance from a database conveying their grades, weekly attendance rates and related academic information. Although …...

This paper explores the use of Natural Language Generation (NLG) for facilitating the provision of feedback to citizen scientists in the context of a nature conservation programme, BEEWATCH. BEEWATCH aims …...

We present a natural language genera- tion system which supports the incremen- tal specification of ontology-based queries in natural language. Our contribution is two fold. First, we introduce a chart …...

Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact both on usabil- ity and perceived quality. Most NLG sys- tems in common …...

AbstractIn Natural Language Generation (NLG), one important limitation is the lack of common benchmarks on which to train, evaluate and compare data-to-text generators. In this paper, we make one step …...

Part of the unique cultural heritage of Chi- na is the Chinese couplet. Given a sen- tence (namely an antecedent clause), peo- ple reply with another sentence (namely a subsequent …...

AbstractNatural language generation (NLG) is an important component of question answering(QA) systems which has a significant impact on system quality. Most tranditional QA systems based on templates or rules tend …...

AbstractThe search space in grammar-based natural language generation tasks can get very large, which is particularly problematic when generating long utterances or paragraphs. Using surface realization with OpenCCG as an …...

AbstractNatural language generation (NLG) is the task of generating natural language from a meaning representation. Current rule-based approaches require domain-specific and manually constructed linguistic resources, while most machine-learning based approaches …...

AbstractAdvances in neural variational inference have facilitated the learning of powerful directed graphical models with continuous latent variables, such as variational autoencoders. The hope is that such models will learn …...

AbstractDomain Adaptation arises when we aim at learning from source domain a model that can perform acceptably well on a different target domain. It is especially crucial for Natural Language …...

AbstractRecent deep learning models have shown improving results to natural language generation (NLG) irrespective of providing sufficient annotated data. However, a modest training data may harm such models’ performance. Thus, …...

AbstractText generation may require the pluralization of nouns, such as in context-sensitive user interfaces and in natural language generation more broadly. While this has been solved for the widely-used languages, …...

In game-like applications and many others, an underlying Natural Language Generation system may have to express urgency or other dynamic aspects of a fast-evolving situation as text, which may be …...

AbstractHuman evaluations are broadly thought to be more valuable the higher the inter-annotator agreement. In this paper we examine this idea. We will describe our experiments and analysis within the …...

Alignments between natural language and Knowledge Base (KB) triples are an essential prerequisite for training machine learning approaches employed in a variety of Natural Language Processing problems. These include Relation …...

Museums and other cultural heritage institutions have large databases of information about the objects in their collections, and existing Natural Language Generation (NLG) systems can generate fluent and adaptive texts …...

AbstractNatural language understanding (NLU) and natural language generation (NLG) are both critical research topics in the NLP and dialogue fields. Natural language understanding is to extract the core semantic meaning …...

AbstractWe conduct a large-scale, systematic study to evaluate the existing evaluation methods for natural language generation in the context of generating online product reviews. We compare human-based evaluators with a …...

AbstractLarge-scale pretrained language models define state of the art in natural language processing, achieving outstanding performance on a variety of tasks. We study how these architectures can be applied and …...

AbstractWe present set to ordered text, a natural language generation task applied to automatically generating discharge instructions from admission ICD (International Classification of Diseases) codes. This task differs from other …...

AbstractWe present a systematic study of biases in natural language generation (NLG) by analyzing text generated from prompts that contain mentions of different demographic groups. In this work, we introduce …...

AbstractHow can we measure whether a natural language generation system produces both high quality and diverse outputs? Human evaluation captures quality but not diversity, as it does not catch models …...

AbstractSemantic similarity is an area of Natural Language Processing that is useful for several downstream applications, such as machine translation, natural language generation, information retrieval, or question answering. The task …...

AbstractNatural language understanding (NLU) and natural language generation (NLG) are two fundamental and related tasks in building task-oriented dialogue systems with opposite objectives: NLU tackles the transformation from natural language …...

AbstractNatural language understanding (NLU) and Natural language generation (NLG) tasks hold a strong dual relationship, where NLU aims at predicting semantic labels based on natural language utterances and NLG does …...

AbstractNatural Language Processing (NLP) can help unlock the vast troves of unstructured data in clinical text and thus improve healthcare research. However, a big barrier to developments in this field …...

AbstractThe automatic feedback of school assignments is an important application of AI in education. In this work, we focus on the task of personalized multimodal feedback generation, which aims to …...

AbstractData-driven approaches using neural networks have achieved promising performances in natural language generation (NLG). However, neural generators are prone to make mistakes, e.g., neglecting an input slot value and generating …...

OpenNMT is a multi-year open-source ecosystem for neural machine translation (NMT) and natural language generation (NLG). The toolkit consists of multiple projects to cover the com- plete machine learning workflow: …...

AbstractHuman evaluations are typically considered the gold standard in natural language generation, but as models’ fluency improves, how well can evaluators detect and judge machine-generated text? We run a study …...

AbstractNatural Language Generation (NLG) is a key component in a task-oriented dialogue system, which converts the structured meaning representation (MR) to the natural language. For large-scale conversational systems, where it …...

AbstractA well-known limitation in pretrain-finetune paradigm lies in its inflexibility caused by the one-size-fits-all vocabulary. This potentially weakens the effect when applying pretrained models into natural language generation (NLG) tasks, …...

AbstractNatural language generation (NLG) spans a broad range of tasks, each of which serves for specific objectives and desires different properties of generated text. The complexity makes automatic evaluation of …...

AbstractRecent years have witnessed various types of generative models for natural language generation (NLG), especially RNNs or transformer based sequence-to-sequence models, as well as variational autoencoder (VAE) and generative adversarial …...

AbstractNatural language generation (NLG) tasks on pro-drop languages are known to suffer from zero pronoun (ZP) problems, and the problems remain challenging due to the scarcity of ZP-annotated NLG corpora. …...

. This paper presents an unsupervised approach for natural language generation within the framework of question-answering systems. This approach addresses the issue of generating answers that are usually too short …...

With the massive success of stochastic lan- guage models, some claim that the symbolic approach is not necessary for natural language processing. However, recent studies made use of linguistically motivated …...

The adoption of natural language generation (NLG) models can leave individuals vulnera- ble to the generation of harmful information memorized by the models, such as conspiracy theories. While previous studies …...

AbstractPersonalization of natural language generation plays a vital role in a large spectrum of tasks, such as explainable recommendation, review summarization and dialog systems. In these tasks, user and item …...

AbstractFine-tuning is the de facto way of leveraging large pretrained language models for downstream tasks. However, fine-tuning modifies all the language model parameters and therefore necessitates storing a full copy …...

AbstractEstimating the expected output quality of generation systems is central to NLG. This paper qualifies the notion that automatic metrics are not as good as humans in estimating system-level quality. …...

Despite the recent advancement in NLP re- search, cross-lingual transfer for natural lan- guage generation is relatively understudied. In this work, we transfer supervision from high resource language (HRL) to …...

AbstractWhen a natural language generation (NLG) component is implemented in a real-world task-oriented dialogue system, it is necessary to generate not only natural utterances as learned on training data but …...

AbstractThe task of generating texts of different categories has attracted more and more attention in the area of natural language generation recently. Meanwhile, generative adversarial net (GAN) has demonstrated its …...

AbstractThere are many ways to express similar things in text, which makes evaluating natural language generation (NLG) systems difficult. Compounding this difficulty is the need to assess varying quality criteria …...

AbstractFew-shot Question Generation (QG) is an important and challenging problem in the Natural Language Generation (NLG) domain. Multilingual BERT (mBERT) has been successfully used in various Natural Language Understanding (NLU) …...

AbstractLarge-scale pretrained language models have led to significant improvements in Natural Language Processing. Unfortunately, they come at the cost of high computational and storage requirements that complicate their deployment on …...

AbstractNumerical tables are widely employed to communicate or report the classification performance of machine learning (ML) models with respect to a set of evaluation metrics. For non-experts, domain knowledge is …...

AbstractIn knowledge distillation, a student model is trained with supervisions from both knowledge from a teacher and observations drawn from a training data distribution. Knowledge of a teacher is considered …...

AbstractWe consider the problem of generating natural language given a communicative goal and a world description. We ask the question: is it possible to combine complementary meaning representations to scale …...

AbstractPrefix-tuning, or more generally continuous prompt tuning, has become an essential paradigm of parameter-efficient transfer learning. Using a large pre-trained language model (PLM), prefix-tuning can obtain strong performance by training …...

AbstractNatural language generation in real-time settings with raw sensor data is a challenging task. We find that formulating the task as an end-to-end problem leads to two major challenges in …...

AbstractDriven by deep learning breakthroughs, natural language generation (NLG) models have been at the center of steady progress in the last few years, with a ubiquitous task influence. However, since …...

AbstractNatural language generation (NLG) models can propagate social bias towards particular demography. Though several studies investigated bias from data and model, NLG task distinctively uses stochastic decoder that can positively …...

AbstractNatural language modeling with limited training data is a challenging problem, and many algorithms make use of large-scale pretrained language models (PLMs) for this due to its great generalization ability. …...

AbstractHealth coaching helps patients identify and accomplish lifestyle-related goals, effectively improving the control of chronic diseases and mitigating mental health conditions. However, health coaching is cost-prohibitive due to its highly …...

AbstractThis position paper proposes a conceptual framework for the design of Natural Language Generation (NLG) systems that follow efficient and effective production strategies in order to achieve complex communicative goals. …...

AbstractIn this paper, we propose a variational autoencoder with disentanglement priors, VAE-Dprior, for task-specific natural language generation with none or a handful of task-specific labeled examples. In order to tackle …...

AbstractLanguage is the principal tool for human communication, in which humor is one of the most attractive parts. Producing natural language like humans using computers, a.k.a, Natural Language Generation (NLG), …...

AbstractNatural language generation models reproduce and often amplify the biases present in their training data. Previous research explored using sequence-to-sequence rewriting models to transform biased model outputs (or original texts) …...

AbstractWe propose a novel task, G4C, to study teacher-student natural language interactions in a goal-driven and grounded environment. Dungeons and Dragons (D&D), a role-playing game, provides an ideal setting to …...

AbstractFactual correctness is often the limiting factor in practical applications of natural language generation in high-stakes domains such as healthcare. An essential requirement for maintaining factuality is the ability to …...