15 research outputs found
Neural probabilistic language model for system combination
This paper gives the system description of the neural probabilistic language modeling (NPLM) team of Dublin City University for our participation in the system combination task in the Second Workshop on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid MT (ML4HMT-12). We used the information obtained by NPLM as meta information to the system combination module. For the Spanish-English data, our paraphrasing approach achieved 25.81 BLEU points, which lost 0.19 BLEU points absolute compared to the standard confusion network-based system combination. We note that our current usage of NPLM is very limited due to the difficulty in combining NPLM and system combination
Language Models as Emotional Classifiers for Textual Conversations
Emotions play a critical role in our everyday lives by altering how we
perceive, process and respond to our environment. Affective computing aims to
instill in computers the ability to detect and act on the emotions of human
actors. A core aspect of any affective computing system is the classification
of a user's emotion. In this study we present a novel methodology for
classifying emotion in a conversation. At the backbone of our proposed
methodology is a pre-trained Language Model (LM), which is supplemented by a
Graph Convolutional Network (GCN) that propagates information over the
predicate-argument structure identified in an utterance. We apply our proposed
methodology on the IEMOCAP and Friends data sets, achieving state-of-the-art
performance on the former and a higher accuracy on certain emotional labels on
the latter. Furthermore, we examine the role context plays in our methodology
by altering how much of the preceding conversation the model has access to when
making a classification