5 |
Sources Matter: A Comparison of Fake News Datasets on Linguistic Feature Performance ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
Language and Reasoning by Entropy Fractals
|
|
|
|
In: Signals ; Volume 2 ; Issue 4 ; Pages 44-770 (2021)
|
|
BASE
|
|
Show details
|
|
7 |
The Cultural Outcomes of Social Movements: A Computational Linguistics Approach
|
|
|
|
In: Partecipazione e conflitto; Vol. 14, No. 3 (2021). Special Issue on: "When, where and which kind of collective action matters?"; 1151-1179 (2021)
|
|
BASE
|
|
Show details
|
|
8 |
Une approche computationnelle de la complexité linguistique par le traitement automatique du langage naturel et l'oculométrie
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Living Machines atypical animacy dataset ...
|
|
: British Library, 2020
|
|
BASE
|
|
Show details
|
|
10 |
Machine learning methods for vector-based compositional semantics ...
|
|
|
|
BASE
|
|
Show details
|
|
11 |
Learning meaning representations for text generation with deep generative models ...
|
|
Cao, Kris. - : Apollo - University of Cambridge Repository, 2020
|
|
Abstract:
This thesis explores conditioning a language generation model with auxiliary variables. By doing so, we hope to be able to better control the output of the language generator. We explore several kinds of auxiliary variables in this thesis, from unstructured continuous, to discrete, to structured discrete auxiliary variables, and evaluate their advantages and disadvantages. We consider three primary axes of variation: how interpretable the auxiliary variables are, how much control they provide over the generated text, and whether the variables can be induced from unlabelled data. The latter consideration is particularly interesting: if we can show that induced latent variables correspond to the semantics of the generated utterance, then by manipulating the variables, we have fine-grained control over the meaning of the generated utterance, thereby learning simple meaning representations for text generation. We investigate three language generation tasks: open domain conversational response generation, ...
|
|
Keyword:
computational linguistics; machine learning; natural language processing
|
|
URL: https://dx.doi.org/10.17863/cam.52382 https://www.repository.cam.ac.uk/handle/1810/305297
|
|
BASE
|
|
Hide details
|
|
12 |
Modelling speaker adaptation in second language learner dialogue ...
|
|
|
|
BASE
|
|
Show details
|
|
15 |
Towards Programming in Natural Language: Learning New Functions from Spoken Utterances ...
|
|
|
|
BASE
|
|
Show details
|
|
16 |
Towards Programming in Natural Language: Learning New Functions from Spoken Utterances
|
|
|
|
In: International journal of semantic computing, 14 (2), 249–272 ; ISSN: 1793-351X, 1793-7108 (2020)
|
|
BASE
|
|
Show details
|
|
17 |
Modelling speaker adaptation in second language learner dialogue
|
|
|
|
BASE
|
|
Show details
|
|
19 |
Defining distinctiveness: A computational and experimental analysis
|
|
|
|
BASE
|
|
Show details
|
|
20 |
Learning to Parse Grounded Language using Reservoir Computing
|
|
|
|
In: ICDL-Epirob 2019 - Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics ; https://hal.inria.fr/hal-02422157 ; ICDL-Epirob 2019 - Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics, Aug 2019, Olso, Norway. ⟨10.1109/devlrn.2019.8850718⟩ ; https://ieeexplore.ieee.org/abstract/document/8850718 (2019)
|
|
BASE
|
|
Show details
|
|
|
|