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Found 7 papers, 1 papers with code
Étiquetage ou génération de séquences pour la compréhension automatique du langage en contexte d’interaction? (Sequence tagging or sequence generation for Natural Language Understanding ?)
La tâche de compréhension automatique du langage en contexte d’interaction (NLU pour Natural Language Understanding) est souvent réduite à la détection d’intentions et de concepts sur des corpus mono-domaines annotés avec une seule intention par énoncé.
WikiFactDiff: A Large, Realistic, and Temporally Adaptable Dataset for Atomic Factual Knowledge Update in Causal Language Models
To study this task, we present WikiFactDiff, a dataset that describes the evolution of factual knowledge between two dates as a collection of simple facts divided into three categories: new, obsolete, and static.
Predicting Links on Wikipedia with Anchor Text Information
Wikipedia, the largest open-collaborative online encyclopedia, is a corpus of documents bound together by internal hyperlinks.
Adapting a FrameNet Semantic Parser for Spoken Language Understanding Using Adversarial Learning
We show that adversarial learning increases all models generalization capabilities both on manual and automatic speech transcription as well as on encyclopedic data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Robust Semantic Parsing with Adversarial Learning for Domain Generalization
This paper addresses the issue of generalization for Semantic Parsing in an adversarial framework.
Un duel probabiliste pour départager deux présidents (LIA @ DEFT'2005)
We present a set of probabilistic models applied to binary classification as defined in the DEFT'05 challenge.
Sources of Complexity in Semantic Frame Parsing for Information Extraction
This paper describes a Semantic Frame parsing System based on sequence labeling methods, precisely BiLSTM models with highway connections, for performing information extraction on a corpus of French encyclopedic history texts annotated according to the Berkeley FrameNet formalism.
