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Found 6 papers, 4 papers with code
Laying Anchors: Semantically Priming Numerals in Language Modeling
Off-the-shelf pre-trained language models have become the de facto standard in NLP pipelines for a multitude of downstream tasks.
Learning Non-linguistic Skills without Sacrificing Linguistic Proficiency
The field of Math-NLP has witnessed significant growth in recent years, motivated by the desire to expand LLM performance to the learning of non-linguistic notions (numerals, and subsequently, arithmetic reasoning).
Overcoming Barriers to Skill Injection in Language Modeling: Case Study in Arithmetic
Through their transfer learning abilities, highly-parameterized large pre-trained language models have dominated the NLP landscape for a multitude of downstream language tasks.
Innovations in Neural Data-to-text Generation: A Survey
The neural boom that has sparked natural language processing (NLP) research through the last decade has similarly led to significant innovations in data-to-text generation (DTG).
TCube: Domain-Agnostic Neural Time-series Narration
We present TCube (Time-series-to-text), a domain-agnostic neural framework for time-series narration, that couples the representation of essential time-series elements in the form of a dense knowledge graph and the translation of said knowledge graph into rich and fluent narratives through the transfer-learning capabilities of PLMs (Pre-trained Language Models).
Once Upon A Time In Visualization: Understanding the Use of Textual Narratives for Causality
In this paper, we propose the use of textual narratives as a data-driven storytelling method to augment causality visualization.
