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Found 8 papers, 1 papers with code
Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling
Intent classification (IC) and slot filling (SF) are two fundamental tasks in modern Natural Language Understanding (NLU) systems.
Think While You Write: Hypothesis Verification Promotes Faithful Knowledge-to-Text Generation
Knowledge-to-text generators often struggle to faithfully generate descriptions for the input facts: they may produce hallucinations that contradict the input, or describe facts not present in the input.
FLEEK: Factual Error Detection and Correction with Evidence Retrieved from External Knowledge
Detecting factual errors in textual information, whether generated by large language models (LLM) or curated by humans, is crucial for making informed decisions.
A Better Match for Drivers and Riders: Reinforcement Learning at Lyft
This change was the first documented implementation of a ridesharing matching algorithm that can learn and improve in real time.
Construction of Paired Knowledge Graph-Text Datasets Informed by Cyclic Evaluation
Guided by these observations, we construct a new, improved dataset called LAGRANGE using heuristics meant to improve equivalence between KG and text and show the impact of each of the heuristics on cyclic evaluation.
Document Summarization with Text Segmentation
In this paper, we exploit the innate document segment structure for improving the extractive summarization task.
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Semi-Supervised Few-Shot Intent Classification and Slot Filling
Intent classification (IC) and slot filling (SF) are two fundamental tasks in modern Natural Language Understanding (NLU) systems.
Lights, Camera, Action! A Framework to Improve NLP Accuracy over OCR documents
Document digitization is essential for the digital transformation of our societies, yet a crucial step in the process, Optical Character Recognition (OCR), is still not perfect.
