16 citations · 42 across the 14 of their papers we have counts for
14 papers
Graph Linearization Methods for Reasoning on Graphs with Large Language Models
Christos Xypolopoulos, Guokan Shang, Xiao Fei +6
Large language models have evolved to process multiple modalities beyond text, such as images and audio, which motivates us to explore how to effectively leverage them for graph re…
Atlas-Chat: Adapting Large Language Models for Low-Resource Moroccan Arabic Dialect
Guokan Shang, Hadi Abdine, Yousef Khoubrane +9
We introduce Atlas-Chat, the first-ever collection of LLMs specifically developed for dialectal Arabic. Focusing on Moroccan Arabic, also known as Darija, we construct our instruct…
Leveraging Discourse Structure for Extractive Meeting Summarization
Virgile Rennard, Guokan Shang, Michalis Vazirgiannis +1
We introduce an extractive summarization system for meetings that leverages discourse structure to better identify salient information from complex multi-party discussions. Using d…
FREDSum: A Dialogue Summarization Corpus for French Political Debates
Virgile Rennard, Guokan Shang, Damien Grari +2
Recent advances in deep learning, and especially the invention of encoder-decoder architectures, has significantly improved the performance of abstractive summarization systems. Th…
The Claire French Dialogue Dataset
Julie Hunter, Jérôme Louradour, Virgile Rennard +3
We present the Claire French Dialogue Dataset (CFDD), a resource created by members of LINAGORA Labs in the context of the OpenLLM France initiative. CFDD is a corpus containing ro…
Automatic Analysis of Substantiation in Scientific Peer Reviews
Yanzhu Guo, Guokan Shang, Virgile Rennard +2
With the increasing amount of problematic peer reviews in top AI conferences, the community is urgently in need of automatic quality control measures. In this paper, we restrict ou…
The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic Text
Yanzhu Guo, Guokan Shang, Michalis Vazirgiannis +1
This study investigates the consequences of training language models on synthetic data generated by their predecessors, an increasingly prevalent practice given the prominence of p…
DATScore: Evaluating Translation with Data Augmented Translations
Moussa Kamal Eddine, Guokan Shang, Michalis Vazirgiannis
The rapid development of large pretrained language models has revolutionized not only the field of Natural Language Generation (NLG) but also its evaluation. Inspired by the recent…
Abstractive Meeting Summarization: A Survey
Virgile Rennard, Guokan Shang, Julie Hunter +1
A system that could reliably identify and sum up the most important points of a conversation would be valuable in a wide variety of real-world contexts, from business meetings to m…
A Highly Effective Low-Rank Compression of Deep Neural Networks with Modified Beam-Search and Modified Stable Rank
Moonjung Eo, Suhyun Kang, Wonjong Rhee
Compression has emerged as one of the essential deep learning research topics, especially for the edge devices that have limited computation power and storage capacity. Among the m…
FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metricsfor Automatic Text Generation
Moussa Kamal Eddine, Guokan Shang, Antoine J. -P. Tixier +1
Fast and reliable evaluation metrics are key to R&D progress. While traditional natural language generation metrics are fast, they are not very reliable. Conversely, new metrics ba…
Speaker-change Aware CRF for Dialogue Act Classification
Guokan Shang, Antoine Jean-Pierre Tixier, Michalis Vazirgiannis +1
Recent work in Dialogue Act (DA) classification approaches the task as a sequence labeling problem, using neural network models coupled with a Conditional Random Field (CRF) as the…
Energy-based Self-attentive Learning of Abstractive Communities for Spoken Language Understanding
Guokan Shang, Antoine Jean-Pierre Tixier, Michalis Vazirgiannis +1
Abstractive community detection is an important spoken language understanding task, whose goal is to group utterances in a conversation according to whether they can be jointly sum…
Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization
Guokan Shang, Wensi Ding, Zekun Zhang +4
We introduce a novel graph-based framework for abstractive meeting speech summarization that is fully unsupervised and does not rely on any annotations. Our work combines the stren…