DE eng

Search in the Catalogues and Directories

Page: 1 2 3
Hits 1 – 20 of 46

1
Is Information Density Uniform in Task-Oriented Dialogues? ...
BASE
Show details
2
Analysing Human Strategies of Information Transmission as a Function of Discourse Context ...
BASE
Show details
3
Semantic shift in social networks ...
BASE
Show details
4
Syntactic Persistence in Language Models: Priming as a Window into Abstract Language Representations ...
BASE
Show details
5
Refer, Reuse, Reduce: Generating Subsequent References in Visual and Conversational Contexts ...
BASE
Show details
6
Words are the Window to the Soul: Language-based User Representations for Fake News Detection ...
BASE
Show details
7
Analysing Lexical Semantic Change with Contextualised Word Representations ...
BASE
Show details
8
DUPS: Diachronic Usage Pair Similarity ...
BASE
Show details
9
DUPS: Diachronic Usage Pair Similarity ...
BASE
Show details
10
DUPS: Diachronic Usage Pair Similarity ...
BASE
Show details
11
Disentangling dialects: a neural approach to Indo-Aryan historical phonology and subgrouping
In: Cathcart, Chundra; Rama, Taraka (2020). Disentangling dialects: a neural approach to Indo-Aryan historical phonology and subgrouping. In: Fernández, Raquel; Linzen, Tal. Proceedings of the 24th Conference on Computational Natural Language Learning. Online: Association for Computational Linguistics, 620-630. (2020)
BASE
Show details
12
Identifying robust markers of Parkinson's disease in typing behaviour using a CNN-LSTM network.
Dhir, Neil; Bannard, Colin J; Stafford, Tom. - : Association for Computational Linguistics, 2020
BASE
Show details
13
Evaluating the Representational Hub of Language and Vision Models ...
BASE
Show details
14
Is the Red Square Big? MALeViC: Modeling Adjectives Leveraging Visual Contexts ...
BASE
Show details
15
MALeViC: Modeling Adjectives Leveraging Visual Contexts ...
Abstract: This work aims at modeling how the meaning of gradable adjectives of size (‘big’, ‘small’) can be learned from visually-grounded contexts. Inspired by cognitive and linguistic evidence showing that the use of these expressions relies on setting a threshold that is dependent on a specific context, we investigate the ability of multi-modal models in assessing whether an object is ‘big’ or ‘small’ in a given visual scene. In contrast with the standard computational approach that simplistically treats gradable adjectives as ‘fixed’ attributes, we pose the problem as relational: to be successful, a model has to consider the full visual context. Models and visual features used in: - Pezzelle, S., Fernandez, R. (2019). Is the Red Square Big? MALeViC: Modeling Adjectives Leveraging Visual Contexts. Proceedings of EMNLP-IJCNLP 2019. - Pezzelle, S., Fernandez, R. (2019). Big Generalizations with Small Data: Exploring the Role of Training Samples in Learning Adjectives of Size. Proceedings of LANTERN 2019 co-located ...
Keyword: gradable adjectives; size; symbol grounding; vagueness; visual reasoning
URL: https://zenodo.org/record/3516924
https://dx.doi.org/10.5281/zenodo.3516924
BASE
Hide details
16
MALeViC: Modeling Adjectives Leveraging Visual Contexts ...
BASE
Show details
17
You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLP ...
BASE
Show details
18
Psycholinguistics meets Continual Learning: Measuring Catastrophic Forgetting in Visual Question Answering ...
BASE
Show details
19
La adquisición del lenguaje de tres a seis años y sus posibles trastornos
BASE
Show details
20
Beyond task success: A closer look at jointly learning to see, ask, and GuessWhat ...
BASE
Show details

Page: 1 2 3

Catalogues
0
0
0
0
1
0
0
Bibliographies
1
0
0
0
0
0
0
0
0
Linked Open Data catalogues
0
Online resources
0
0
0
0
Open access documents
44
0
0
0
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern