53 citations · 156 across the 12 of their papers we have counts for
10 papers · 1 filter
lambeq: An Efficient High-Level Python Library for Quantum NLP
Dimitri Kartsaklis, Ian Fan, Richie Yeung +7
We present lambeq, the first high-level Python library for Quantum Natural Language Processing (QNLP). The open-source toolkit offers a detailed hierarchy of modules and classes im…
Something Old, Something New: Grammar-based CCG Parsing with Transformer Models
Stephen Clark
This report describes the parsing problem for Combinatory Categorial Grammar (CCG), showing how a combination of Transformer-based neural models and a symbolic CCG grammar can lead…
Grounded Language Learning Fast and Slow
Felix Hill, Olivier Tieleman, Tamara von Glehn +3
Recent work has shown that large text-based neural language models, trained with conventional supervised learning objectives, acquire a surprising propensity for few- and one-shot…
Learning to Segment Actions from Observation and Narration
Daniel Fried, Jean-Baptiste Alayrac, Phil Blunsom +3
We apply a generative segmental model of task structure, guided by narration, to action segmentation in video. We focus on unsupervised and weakly-supervised settings where no acti…
Neural Generative Rhetorical Structure Parsing
Amandla Mabona, Laura Rimell, Stephen Clark +1
Rhetorical structure trees have been shown to be useful for several document-level tasks including summarization and document classification. Previous approaches to RST parsing hav…
Scalable Syntax-Aware Language Models Using Knowledge Distillation
Adhiguna Kuncoro, Chris Dyer, Laura Rimell +2
Prior work has shown that, on small amounts of training data, syntactic neural language models learn structurally sensitive generalisations more successfully than sequential langua…