activity
20132022
most citedOnception: Active Learning with Expert Advice for Real World Machine Translation

3 citations · 3 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL2022

SUMBot: Summarizing Context in Open-Domain Dialogue Systems

Rui Ribeiro, Luísa Coheur

In this paper, we investigate the problem of including relevant information as context in open-domain dialogue systems. Most models struggle to identify and incorporate important k…

cs.CL20223 cited

Onception: Active Learning with Expert Advice for Real World Machine Translation

Vânia Mendonça, Ricardo Rei, Luisa Coheur +1

Active learning can play an important role in low-resource settings (i.e., where annotated data is scarce), by selecting which instances may be more worthy to annotate. Most active…

cs.CL2021

Online Learning Meets Machine Translation Evaluation: Finding the Best Systems with the Least Human Effort

Vânia Mendonça, Ricardo Rei, Luisa Coheur +2

In Machine Translation, assessing the quality of a large amount of automatic translations can be challenging. Automatic metrics are not reliable when it comes to high performing sy…

cs.CL2016

Graph-Community Detection for Cross-Document Topic Segment Relationship Identification

Pedro Mota, Maxine Eskenazi, Luisa Coheur

In this paper we propose a graph-community detection approach to identify cross-document relationships at the topic segment level. Given a set of related documents, we automaticall…

cs.CL2013

Towards the Rapid Development of a Natural Language Understanding Module

Catarina Moreira, Ana Cristina Mendes, Luísa Coheur +1

When developing a conversational agent, there is often an urgent need to have a prototype available in order to test the application with real users. A Wizard of Oz is a possibilit…