DE eng

Search in the Catalogues and Directories

Hits 1 – 13 of 13

1
Pathologies of Pre-trained Language Models in Few-shot Fine-tuning ...
BASE
Show details
2
MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning ...
BASE
Show details
3
A Dataset and Baselines for Multilingual Reply Suggestion ...
BASE
Show details
4
A Conditional Generative Matching Model for Multi-lingual Reply Suggestion ...
BASE
Show details
5
XtremeDistilTransformers: Task Transfer for Task-agnostic Distillation ...
BASE
Show details
6
Say `YES' to Positivity: Detecting Toxic Language in Workplace Communications ...
BASE
Show details
7
A Conditional Generative Matching Model for Multi-lingual Reply Suggestion ...
BASE
Show details
8
Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer ...
BASE
Show details
9
XtremeDistil: Multi-stage Distillation for Massive Multilingual Models ...
BASE
Show details
10
Smart To-Do : Automatic Generation of To-Do Items from Emails ...
BASE
Show details
11
Distilling BERT into Simple Neural Networks with Unlabeled Transfer Data ...
BASE
Show details
12
Multi-Source Cross-Lingual Model Transfer: Learning What to Share ...
Abstract: Modern NLP applications have enjoyed a great boost utilizing neural networks models. Such deep neural models, however, are not applicable to most human languages due to the lack of annotated training data for various NLP tasks. Cross-lingual transfer learning (CLTL) is a viable method for building NLP models for a low-resource target language by leveraging labeled data from other (source) languages. In this work, we focus on the multilingual transfer setting where training data in multiple source languages is leveraged to further boost target language performance. Unlike most existing methods that rely only on language-invariant features for CLTL, our approach coherently utilizes both language-invariant and language-specific features at instance level. Our model leverages adversarial networks to learn language-invariant features, and mixture-of-experts models to dynamically exploit the similarity between the target language and each individual source language. This enables our model to learn effectively what ... : ACL 2019 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
URL: https://dx.doi.org/10.48550/arxiv.1810.03552
https://arxiv.org/abs/1810.03552
BASE
Hide details
13
Identifying Roles in Social Networks using Linguistic Analysis.
BASE
Show details

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