DE eng

Search in the Catalogues and Directories

Page: 1 2 3 4 5...7
Hits 1 – 20 of 129

1
Delving Deeper into Cross-lingual Visual Question Answering ...
BASE
Show details
2
Combating Temporal Drift in Crisis with Adapted Embeddings ...
Stowe, Kevin; Gurevych, Iryna. - : arXiv, 2021
BASE
Show details
3
Annotation Curricula to Implicitly Train Non-Expert Annotators ...
BASE
Show details
4
Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation ...
BASE
Show details
5
xGQA: Cross-Lingual Visual Question Answering ...
BASE
Show details
6
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models ...
BASE
Show details
7
Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning ...
BASE
Show details
8
Metaphor Generation with Conceptual Mappings ...
BASE
Show details
9
GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval ...
BASE
Show details
10
Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs
In: EISSN: 2307-387X ; Transactions of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03020314 ; Transactions of the Association for Computational Linguistics, The MIT Press, 2020, 8, ⟨10.1162/tacl_a_00332⟩ (2020)
BASE
Show details
11
Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation ...
Reimers, Nils; Gurevych, Iryna. - : arXiv, 2020
Abstract: We present an easy and efficient method to extend existing sentence embedding models to new languages. This allows to create multilingual versions from previously monolingual models. The training is based on the idea that a translated sentence should be mapped to the same location in the vector space as the original sentence. We use the original (monolingual) model to generate sentence embeddings for the source language and then train a new system on translated sentences to mimic the original model. Compared to other methods for training multilingual sentence embeddings, this approach has several advantages: It is easy to extend existing models with relatively few samples to new languages, it is easier to ensure desired properties for the vector space, and the hardware requirements for training is lower. We demonstrate the effectiveness of our approach for 50+ languages from various language families. Code to extend sentence embeddings models to more than 400 languages is publicly available. ... : Accepted at EMNLP 2020 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/2004.09813
https://dx.doi.org/10.48550/arxiv.2004.09813
BASE
Hide details
12
How to Probe Sentence Embeddings in Low-Resource Languages: On Structural Design Choices for Probing Task Evaluation ...
BASE
Show details
13
MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer ...
BASE
Show details
14
Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning ...
BASE
Show details
15
How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models ...
BASE
Show details
16
UNKs Everywhere: Adapting Multilingual Language Models to New Scripts ...
BASE
Show details
17
PuzzLing Machines: A Challenge on Learning From Small Data ...
BASE
Show details
18
A Matter of Framing: The Impact of Linguistic Formalism on Probing Results ...
Kuznetsov, Ilia; Gurevych, Iryna. - : arXiv, 2020
BASE
Show details
19
Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs ...
BASE
Show details
20
Empowering Active Learning to Jointly Optimize System and User Demands ...
BASE
Show details

Page: 1 2 3 4 5...7

Catalogues
5
3
2
0
11
0
0
Bibliographies
3
0
3
0
0
0
13
0
1
Linked Open Data catalogues
0
Online resources
0
0
0
0
Open access documents
91
0
0
1
0
© 2013 - 2024 Lin|gu|is|tik | Imprint | Privacy Policy | Datenschutzeinstellungen ändern