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Found 29 papers, 12 papers with code
Can Character-based Language Models Improve Downstream Task Performances In Low-Resource And Noisy Language Scenarios?
Recent impressive improvements in NLP, largely based on the success of contextual neural language models, have been mostly demonstrated on at most a couple dozen high- resource languages.
Fine-tuning and Sampling Strategies for Multimodal Role Labeling of Entities under Class Imbalance
We propose our solution to the multimodal semantic role labeling task from the CONSTRAINT’22 workshop.
Quand être absent de mBERT n’est que le commencement : Gérer de nouvelles langues à l’aide de modèles de langues multilingues (When Being Unseen from mBERT is just the Beginning : Handling New Languages With Multilingual Language Models)
Dans ce travail, en comparant des modèles multilingues et monolingues, nous montrons que de tels modèles se comportent de multiples façons sur des langues inconnues.
Tâches Auxiliaires Multilingues pour le Transfert de Modèles de Détection de Discours Haineux (Multilingual Auxiliary Tasks for Zero-Shot Cross-Lingual Transfer of Hate Speech Detection)
La tâche de détection de contenus haineux est ardue, car elle nécessite des connaissances culturelles et contextuelles approfondies ; les connaissances nécessaires varient, entre autres, selon la langue du locateur ou la cible du contenu.
From Raw Text to Enhanced Universal Dependencies: The Parsing Shared Task at IWPT 2021
We describe the second IWPT task on end-to-end parsing from raw text to Enhanced Universal Dependencies.
Comparison between NMT and PBSMT Performance for Translating Noisy User-Generated Content
This work compares the performances achieved by Phrase-Based Statistical Machine Translation systems (PB-SMT) and attention-based Neuronal Machine Translation systems (NMT) when translating User Generated Content (UGC), as encountered in social medias, from French to English.
From Text to Source: Results in Detecting Large Language Model-Generated Content
The research also explores Model Attribution, encompassing source model identification, model family, and model size classification, in addition to quantization and watermarking detection.
Towards a Robust Detection of Language Model Generated Text: Is ChatGPT that Easy to Detect?
This paper proposes a methodology for developing and evaluating ChatGPT detectors for French text, with a focus on investigating their robustness on out-of-domain data and against common attack schemes.
Data-Efficient French Language Modeling with CamemBERTa
In this paper, we introduce CamemBERTa, a French DeBERTa model that builds upon the DeBERTaV3 architecture and training objective.
Multilingual Auxiliary Tasks Training: Bridging the Gap between Languages for Zero-Shot Transfer of Hate Speech Detection Models
Zero-shot cross-lingual transfer learning has been shown to be highly challenging for tasks involving a lot of linguistic specificities or when a cultural gap is present between languages, such as in hate speech detection.
Towards Unsupervised Content Disentanglement in Sentence Representations via Syntactic Roles
Starting from a deep probabilistic generative model with attention, we measure the interaction between latent variables and realizations of syntactic roles and show that it is possible to obtain, without supervision, representations of sentences where different syntactic roles correspond to clearly identified different latent variables.
Exploiting Inductive Bias in Transformers for Unsupervised Disentanglement of Syntax and Semantics with VAEs
In the attention of Transformers, keys handle information selection while values specify what information is conveyed.
Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora
The problem of comparing two bodies of text and searching for words that differ in their usage between them arises often in digital humanities and computational social science.
Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios?
Recent impressive improvements in NLP, largely based on the success of contextual neural language models, have been mostly demonstrated on at most a couple dozen high-resource languages.
Understanding the Impact of UGC Specificities on Translation Quality
This work takes a critical look at the evaluation of user-generated content automatic translation, the well-known specificities of which raise many challenges for MT.
Noisy UGC Translation at the Character Level: Revisiting Open-Vocabulary Capabilities and Robustness of Char-Based Models
This work explores the capacities of character-based Neural Machine Translation to translate noisy User-Generated Content (UGC) with a strong focus on exploring the limits of such approaches to handle productive UGC phenomena, which almost by definition, cannot be seen at training time.
PAGnol: An Extra-Large French Generative Model
We fit a scaling law for compute for the French language, and compare it with its English counterpart.
Challenging the Semi-Supervised VAE Framework for Text Classification
We compare the simplified versions to standard SSVAEs on 4 text classification tasks.
First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT
Such transfer emerges by fine-tuning on a task of interest in one language and evaluating on a distinct language, not seen during the fine-tuning.
Disentangling semantics in language through VAEs and a certain architectural choice
We present an unsupervised method to obtain disentangled representations of sentences that single out semantic content.
On the Granularity of Explanations in Model Agnostic NLP Interpretability
Current methods for Black-Box NLP interpretability, like LIME or SHAP, are based on altering the text to interpret by removing words and modeling the Black-Box response.
QUACKIE: A NLP Classification Task With Ground Truth Explanations
NLP Interpretability aims to increase trust in model predictions.
Treebanking User-Generated Content: a UD Based Overview of Guidelines, Corpora and Unified Recommendations
This article presents a discussion on the main linguistic phenomena which cause difficulties in the analysis of user-generated texts found on the web and in social media, and proposes a set of annotation guidelines for their treatment within the Universal Dependencies (UD) framework of syntactic analysis.
When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models
Focusing on the latter, we show that this failure to transfer is largely related to the impact of the script used to write such languages.
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering
Coupled with the availability of large scale datasets, deep learning architectures have enabled rapid progress on the Question Answering task.
Controlling the Interaction Between Generation and Inference in Semi-Supervised Variational Autoencoders Using Importance Weighting
Even though Variational Autoencoders (VAEs) are widely used for semi-supervised learning, the reason why they work remains unclear.
Can Multilingual Language Models Transfer to an Unseen Dialect? A Case Study on North African Arabizi
Building natural language processing systems for non standardized and low resource languages is a difficult challenge.
CamemBERT: a Tasty French Language Model
We show that the use of web crawled data is preferable to the use of Wikipedia data.
Ranked #1 on
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on French GSD
