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Found 10 papers, 0 papers with code
A Multimodal Educational Corpus of Oral Courses: Annotation, Analysis and Case Study
The use cases concern scientific fields from both speech and text processing, with language model adaptation, thematic segmentation and transcription to slide alignment.
Cross-lingual and Cross-domain Evaluation of Machine Reading Comprehension with Squad and CALOR-Quest Corpora
Machine Reading received recently a lot of attention thanks to both the availability of very large corpora such as SQuAD or MS MARCO containing triplets (document, question, answer), and the introduction of Transformer Language Models such as BERT which obtain excellent results, even matching human performance according to the SQuAD leaderboard.
CALOR-QUEST : generating a training corpus for Machine Reading Comprehension models from shallow semantic annotations
Machine reading comprehension is a task related to Question-Answering where questions are not generic in scope but are related to a particular document.
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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MaskParse@Deskin at SemEval-2019 Task 1: Cross-lingual UCCA Semantic Parsing using Recursive Masked Sequence Tagging
Parsing is done recursively, we perform a first inference on the sentence to extract the main scenes and links and then we recursively apply our model on the sentence using a masking feature that reflects the decisions made in previous steps.
Robust Semantic Parsing with Adversarial Learning for Domain Generalization
This paper addresses the issue of generalization for Semantic Parsing in an adversarial framework.
The Impact of Word Representations on Sequential Neural MWE Identification
Recent initiatives such as the PARSEME shared task allowed the rapid development of MWE identification systems.
CALOR-QUEST : un corpus d'entra\^\inement et d'\'evaluation pour la compr\'ehension automatique de textes (Machine reading comprehension is a task related to Question-Answering where questions are not generic in scope but are related to a particular document)
Le but de cette {\'e}tude est de permettre le d{\'e}veloppement de telles ressources pour d{'}autres langues {\`a} moindre co{\^u}t en proposant une m{\'e}thode g{\'e}n{\'e}rant de mani{\`e}re semi-automatique des questions {\`a} partir d{'}une analyse s{\'e}mantique d{'}un grand corpus.
