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Found 32 papers, 1 papers with code
On the Hidden Negative Transfer in Sequential Transfer Learning for Domain Adaptation from News to Tweets
Transfer Learning has been shown to be a powerful tool for Natural Language Processing (NLP) and has outperformed the standard supervised learning paradigm, as it takes benefit from the pre-learned knowledge.
X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents
We create a new multilingual benchmark, X-RiSAWOZ, by translating the Chinese RiSAWOZ to 4 languages: English, French, Hindi, Korean; and a code-mixed English-Hindi language.
Neural Supervised Domain Adaptation by Augmenting Pre-trained Models with Random Units
In the standard fine-tuning scheme of TL, a model is initially pre-trained on a source domain and subsequently fine-tuned on a target domain and, therefore, source and target domains are trained using the same architecture.
Multi-Task Supervised Pretraining for Neural Domain Adaptation
Two prevalent transfer learning approaches are used in recent works to improve neural networks performance for domains with small amounts of annotated data: Multi-task learning which involves training the task of interest with related auxiliary tasks to exploit their underlying similarities, and Mono-task fine-tuning, where the weights of the model are initialized with the pretrained weights of a large-scale labeled source domain and then fine-tuned with labeled data of the target domain (domain of interest).
Exploration de l'apprentissage par transfert pour l'analyse de textes des r\'eseaux sociaux (Exploring neural transfer learning for social media text analysis )
L{'}apprentissage par transfert repr{\'e}sente la capacit{\'e} qu{'}un mod{\`e}le neuronal entra{\^\i}n{\'e} sur une t{\^a}che {\`a} g{\'e}n{\'e}raliser suffisamment et correctement pour produire des r{\'e}sultats pertinents sur une autre t{\^a}che proche mais diff{\'e}rente.
Joint Learning of Pre-Trained and Random Units for Domain Adaptation in Part-of-Speech Tagging
Fine-tuning neural networks is widely used to transfer valuable knowledge from high-resource to low-resource domains.
Ranked #1 on
Part-Of-Speech Tagging
on Social media
Using Neural Transfer Learning for Morpho-syntactic Tagging of South-Slavic Languages Tweets
In this paper, we describe a morpho-syntactic tagger of tweets, an important component of the CEA List DeepLIMA tool which is a multilingual text analysis platform based on deep learning.
A Comparison of Character Neural Language Model and Bootstrapping for Language Identification in Multilingual Noisy Texts
This paper seeks to examine the effect of including background knowledge in the form of character pre-trained neural language model (LM), and data bootstrapping to overcome the problem of unbalanced limited resources.
Building Multiword Expressions Bilingual Lexicons for Domain Adaptation of an Example-Based Machine Translation System
We describe in this paper a hybrid ap-proach to build automatically bilingual lexicons of Multiword Expressions (MWEs) from parallel corpora.
Romanized Berber and Romanized Arabic Automatic Language Identification Using Machine Learning
The ALI standard methods require datasets for training and use character/word-based n-gram models.
ASIREM Participation at the Discriminating Similar Languages Shared Task 2016
In sub-task 2, which deals with Arabic dialect identification, the system achieved its best performance using character-based n-grams (49. 67{\%} accuracy), ranking fourth in the closed track (the best result being 51. 16{\%}), and an accuracy of 53. 18{\%}, ranking first in the open track.
Automatic Detection of Arabicized Berber and Arabic Varieties
Automatic Language Identification (ALI) is the detection of the natural language of an input text by a machine.
Inducing Multilingual Text Analysis Tools Using Bidirectional Recurrent Neural Networks
This work focuses on the rapid development of linguistic annotation tools for resource-poor languages.
Etude de l'impact d'un lexique bilingue sp\'ecialis\'e sur la performance d'un moteur de traduction \`a base d'exemples (Studying the impact of a specialized bilingual lexicon on the performance of an example-based machine translation engine)
La traduction automatique statistique bien que performante est aujourd{'}hui limit{\'e}e parce qu{'}elle n{\'e}cessite de gros volumes de corpus parall{\`e}les qui n{'}existent pas pour tous les couples de langues et toutes les sp{\'e}cialit{\'e}s et que leur production est lente et co{\^u}teuse.
Projection Interlingue d'\'Etiquettes pour l'Annotation S\'emantique Non Supervis\'ee (Cross-lingual Annotation Projection for Unsupervised Semantic Tagging)
Dans une pr{\'e}c{\'e}dente contribution, nous avons propos{\'e} une m{\'e}thode pour la construction automatique d{'}un analyseur morpho-syntaxique via une projection interlingue d{'}annotations linguistiques {\`a} partir de corpus parall{\`e}les (m{\'e}thode fond{\'e}e sur les r{\'e}seaux de neurones r{\'e}currents).
Utilisation des r\'eseaux de neurones r\'ecurrents pour la projection interlingue d'\'etiquettes morpho-syntaxiques \`a partir d'un corpus parall\`ele
La construction d{'}outils d{'}analyse linguistique pour les langues faiblement dot{\'e}es est limit{\'e}e, entre autres, par le manque de corpus annot{\'e}s. Dans cet article, nous proposons une m{\'e}thode pour construire automatiquement des outils d{'}analyse via une projection interlingue d{'}annotations linguistiques en utilisant des corpus parall{\`e}les.
Identifying bilingual Multi-Word Expressions for Statistical Machine Translation
MultiWord Expressions (MWEs) repesent a key issue for numerous applications in Natural Language Processing (NLP) especially for Machine Translation (MT).
