Results 1 to 10 of about 9,203,426 (395)
Natural Language Ontology [PDF]
, 2017 The aim of natural language ontology is to uncover the ontological categories and structures that are implicit in the use of natural language, that is, that a speaker accepts when using a language.
Moltmann, Friederike
core +3 more sources
e-SNLI: Natural Language Inference with Natural Language Explanations [PDF]
, 2018 In order for machine learning to garner widespread public adoption, models must be able to provide interpretable and robust explanations for their decisions, as well as learn from human-provided explanations at train time.
Blunsom, Phil +3 more
core +4 more sources
NLAS-multi: A multilingual corpus of automatically generated Natural Language Argumentation Schemes. [PDF]
Data BriefSome of the major limitations identified in the areas of argument mining, argument generation, and natural language argument analysis are related to the complexity of annotating argumentatively rich data, the limited size of these corpora, and the constraints that represent the different languages and domains in which these data is annotated.
Ruiz-Dolz R +3 more
europepmc +2 more sources
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing [PDF]
ACM Computing Surveys, 2021 This article surveys and organizes research works in a new paradigm in natural language processing, which we dub “prompt-based learning.” Unlike traditional supervised learning, which trains a model to take in an input x and predict an output y as P(y|x),
Pengfei Liu +5 more
semanticscholar +1 more source
Survey of Hallucination in Natural Language Generation [PDF]
ACM Computing Surveys, 2022 Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG,
Ziwei Ji +11 more
semanticscholar +1 more source
Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation [PDF]
International Conference on Learning Representations, 2023 We introduce a method to measure uncertainty in large language models. For tasks like question answering, it is essential to know when we can trust the natural language outputs of foundation models.
Lorenz Kuhn, Y. Gal, Sebastian Farquhar
semanticscholar +1 more source
Organizing an in-class hackathon to correct PDF-to-text conversion errors of 1.0 [PDF]
Genomics & Informatics, 2020 This paper describes a community effort to improve earlier versions of the full-text corpus of Genomics & Informatics by semi-automatically detecting and correcting PDF-to-text conversion errors and optical character recognition errors during the first ...
Sunho Kim +44 more
doaj +1 more source
Is ChatGPT a General-Purpose Natural Language Processing Task Solver? [PDF]
Conference on Empirical Methods in Natural Language Processing, 2023 Spurred by advancements in scale, large language models (LLMs) have demonstrated the ability to perform a variety of natural language processing (NLP) tasks zero-shot -- i.e., without adaptation on downstream data.
Chengwei Qin +5 more
semanticscholar +1 more source
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts [PDF]
Annual Meeting of the Association for Computational Linguistics, 2022 PromptSource is a system for creating, sharing, and using natural language prompts. Prompts are functions that map an example from a dataset to a natural language input and target output.
Stephen H. Bach +25 more
semanticscholar +1 more source
Computational Linguistics, 2021 Text-to-SQL is the problem of converting a user question into an SQL query, when the question and database are given. In this article, we present a neural network approach called RYANSQL (Recursively Yielding Annotation Network for SQL) to solve complex ...
DongHyun Choi +3 more
doaj +1 more source