ТілГенерациясы

Planning provides the basis for a theory of language generation that considers the communicative goals of the speaker when producing utterances. One central problem in designing a system based on …...

We present a method to automatically generate a concise summary by identifying and synthe- sizing similar elements across related text from a set of multiple documents. Our approach is unique …...

We present and compare two approaches to the task of summarizing evaluative ar- guments. The first is a sentence extraction- based approach while the second is a lan- guage generation-based …...

In evaluating the output of language tech- nology applications—MT, natural language generation, summarisation—automatic eval- uation techniques generally conflate mea- surement of faithfulness to source content with fluency of the resulting …...

AbstractIn recent years there has been a surge of interest in the natural language prosessing related to the real world, such as symbol grounding, language generation, and nonlinguistic data search …...

AbstractWe consider the problem of parsing natural language descriptions into source code written in a general-purpose programming language like Python. Existing data-driven methods treat this problem as a language generation …...

AbstractVisual Question Generation (VQG) aims to ask natural questions about an image automatically. Existing research focus on training model to fit the annotated data set that makes it indifferent from …...

AbstractWe show that explicit pragmatic inference aids in correctly generating and following natural language instructions for complex, sequential tasks. Our pragmatics-enabled models reason about why speakers produce certain instructions, and …...

AbstractIn recent years, there has been a surge of interest in natural language processing related to the real world, such as symbol grounding, language generation, and non-linguistic data search by …...

AbstractWe propose a family of metrics to assess language generation derived from population estimation methods widely used in ecology. More specifically, we use mark-recapture and maximum-likelihood methods that have been …...

AbstractWe introspect black-box sentence embeddings by conditionally generating from them with the objective to retrieve the underlying discrete sentence. We perceive of this as a new unsupervised probing task and …...

AbstractNatural language often exhibits inherent hierarchical structure ingrained with complex syntax and semantics. However, most state-of-the-art deep generative models learn embeddings only in Euclidean vector space, without accounting for this …...

AbstractWe present BERTGen, a novel, generative, decoder-only model which extends BERT by fusing multimodal and multilingual pre-trained models VL-BERT and M-BERT, respectively. BERTGen is auto-regressively trained for language generation tasks, …...

AbstractBeam search is a go-to strategy for decoding neural sequence models. The algorithm can naturally be viewed as a subset optimization problem, albeit one where the corresponding set function does …...

AbstractTechnology for language generation has advanced rapidly, spurred by advancements in pre-training large models on massive amounts of data and the need for intelligent agents to communicate in a natural …...

AbstractOur study focuses on language generation by considering various information representing the meaning of utterances as multiple conditions of generation. Generating an utterance from a Meaning representation (MR) usually passes …...

AbstractRecently, pretrained language models (PLMs) have had exceptional success in language generation. To leverage the rich knowledge encoded by PLMs, a simple yet powerful paradigm is to use prompts in …...

AbstractLarge pretrained models enable transfer learning to low-resource domains for language generation tasks. However, previous end-to-end approaches do not account for the fact that some generation sub-tasks, specifically aggregation and …...

AbstractModel interpretability methods are often used to explain NLP model decisions on tasks such as text classification, where the output space is relatively small. However, when applied to language generation, …...

AbstractStudying and mitigating gender and other biases in natural language have become important areas of research from both algorithmic and data perspectives. This paper explores the idea of reducing gender …...

AbstractWord ordering is a constrained language generation task taking unordered words as input. Existing work uses linear models and neural networks for the task, yet pre-trained language models have not …...

AbstractRecent studies show that auto-encoder based approaches successfully perform language generation, smooth sentence interpolation, and style transfer over unseen attributes using unlabelled datasets in a zero-shot manner. The latent space …...