9 citations · 17 across the 11 of their papers we have counts for
13 papers
Fuzzy Fingerprinting Transformer Language-Models for Emotion Recognition in Conversations
Patrícia Pereira, Rui Ribeiro, Helena Moniz +2
Fuzzy Fingerprints have been successfully used as an interpretable text classification technique, but, like most other techniques, have been largely surpassed in performance by Lar…
Towards a Fully Unsupervised Framework for Intent Induction in Customer Support Dialogues
Rita Costa, Bruno Martins, Sérgio Viana +1
State of the art models in intent induction require annotated datasets. However, annotating dialogues is time-consuming, laborious and expensive. In this work, we propose a complet…
Using Implicit Feedback to Improve Question Generation
Hugo Rodrigues, Eric Nyberg, Luisa Coheur
Question Generation (QG) is a task of Natural Language Processing (NLP) that aims at automatically generating questions from text. Many applications can benefit from automatically…
PGTask: Introducing the Task of Profile Generation from Dialogues
Rui Ribeiro, Joao P. Carvalho, Luísa Coheur
Recent approaches have attempted to personalize dialogue systems by leveraging profile information into models. However, this knowledge is scarce and difficult to obtain, which mak…
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…
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…
Question rewriting? Assessing its importance for conversational question answering
Gonçalo Raposo, Rui Ribeiro, Bruno Martins +1
In conversational question answering, systems must correctly interpret the interconnected interactions and generate knowledgeable answers, which may require the retrieval of releva…
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…
Chatbots' Greetings to Human-Computer Communication
Maria João Pereira, Luísa Coheur, Pedro Fialho +1
Both dialogue systems and chatbots aim at putting into action communication between humans and computers. However, instead of focusing on sophisticated techniques to perform natura…
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…
Controlling Complexity in Part-of-Speech Induction
João V. Graça, Kuzman Ganchev, Luisa Coheur +2
We consider the problem of fully unsupervised learning of grammatical (part-of-speech) categories from unlabeled text. The standard maximum-likelihood hidden Markov model for this…
Learning to answer questions
Ana Cristina Mendes, Luísa Coheur, Sérgio Curto
We present an open-domain Question-Answering system that learns to answer questions based on successful past interactions. We follow a pattern-based approach to Answer-Extraction,…
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…