5 research outputs found
Modeling Long-Range Context for Concurrent Dialogue Acts Recognition
In dialogues, an utterance is a chain of consecutive sentences produced by
one speaker which ranges from a short sentence to a thousand-word post. When
studying dialogues at the utterance level, it is not uncommon that an utterance
would serve multiple functions. For instance, "Thank you. It works great."
expresses both gratitude and positive feedback in the same utterance. Multiple
dialogue acts (DA) for one utterance breeds complex dependencies across
dialogue turns. Therefore, DA recognition challenges a model's predictive power
over long utterances and complex DA context. We term this problem Concurrent
Dialogue Acts (CDA) recognition. Previous work on DA recognition either assumes
one DA per utterance or fails to realize the sequential nature of dialogues. In
this paper, we present an adapted Convolutional Recurrent Neural Network (CRNN)
which models the interactions between utterances of long-range context. Our
model significantly outperforms existing work on CDA recognition on a tech
forum dataset.Comment: Accepted to CIKM '1
Ranking Enhanced Dialogue Generation
How to effectively utilize the dialogue history is a crucial problem in
multi-turn dialogue generation. Previous works usually employ various neural
network architectures (e.g., recurrent neural networks, attention mechanisms,
and hierarchical structures) to model the history. However, a recent empirical
study by Sankar et al. has shown that these architectures lack the ability of
understanding and modeling the dynamics of the dialogue history. For example,
the widely used architectures are insensitive to perturbations of the dialogue
history, such as words shuffling, utterances missing, and utterances
reordering. To tackle this problem, we propose a Ranking Enhanced Dialogue
generation framework in this paper. Despite the traditional representation
encoder and response generation modules, an additional ranking module is
introduced to model the ranking relation between the former utterance and
consecutive utterances. Specifically, the former utterance and consecutive
utterances are treated as query and corresponding documents, and both local and
global ranking losses are designed in the learning process. In this way, the
dynamics in the dialogue history can be explicitly captured. To evaluate our
proposed models, we conduct extensive experiments on three public datasets,
i.e., bAbI, PersonaChat, and JDC. Experimental results show that our models
produce better responses in terms of both quantitative measures and human
judgments, as compared with the state-of-the-art dialogue generation models.
Furthermore, we give some detailed experimental analysis to show where and how
the improvements come from.Comment: Accepted at CIKM 202
Multilingual sentiment analysis in social media.
252 p.This thesis addresses the task of analysing sentiment in messages coming from social media. The ultimate goal was to develop a Sentiment Analysis system for Basque. However, because of the socio-linguistic reality of the Basque language a tool providing only analysis for Basque would not be enough for a real world application. Thus, we set out to develop a multilingual system, including Basque, English, French and Spanish.The thesis addresses the following challenges to build such a system:- Analysing methods for creating Sentiment lexicons, suitable for less resourced languages.- Analysis of social media (specifically Twitter): Tweets pose several challenges in order to understand and extract opinions from such messages. Language identification and microtext normalization are addressed.- Research the state of the art in polarity classification, and develop a supervised classifier that is tested against well known social media benchmarks.- Develop a social media monitor capable of analysing sentiment with respect to specific events, products or organizations