6 accepted papers
Tasks are central in machine learning, as they are the most natural objects to assess the capabilities of current models. The trend is to build general models able to address any task. Even though transfer learning and multitask learning try to leverage the underlying task space, no well-founded too…
We introduce an evaluation methodology for reading comprehension tasks based on the intuition that certain examples, by the virtue of their linguistic complexity, consistently yield lower scores regardless of model size or architecture. We capitalize on semantic frame annotation for characterizing t…
Current sequence tagging models based on Deep Neural Network models with pretrained language models achieve almost perfect results on many SLU benchmarks with a flat semantic annotation at the token level such as ATIS or SNIPS. When dealing with more complex human-machine interactions (multi-domain,…
In the context of contact centers, customers' satisfaction after a conversation with an agent is a critical issue which has to be collected in order to detect problems and improve quality of service. Automatically predicting customer satisfaction directly from system logs, without any survey or manu…
This paper addresses the task of assigning a title to topic segments automatically extracted from TV Broadcast News video recordings. We propose to associate a topic segment with the title of a newspaper article collected on the web at the same date. The task implies pairing newspaper articles and t…
In this work, we investigate how speaker-based information and lexical-based information can be fused efficiently for topic segmentation of spoken contents. While in recent work, we have proposed an early fusion scheme, so as to jointly model speaker and lexical distribution, we propose here a co-se…