3 research outputs found
Multimodal Embeddings from Language Models
Word embeddings such as ELMo have recently been shown to model word semantics
with greater efficacy through contextualized learning on large-scale language
corpora, resulting in significant improvement in state of the art across many
natural language tasks. In this work we integrate acoustic information into
contextualized lexical embeddings through the addition of multimodal inputs to
a pretrained bidirectional language model. The language model is trained on
spoken language that includes text and audio modalities. The resulting
representations from this model are multimodal and contain paralinguistic
information which can modify word meanings and provide affective information.
We show that these multimodal embeddings can be used to improve over previous
state of the art multimodal models in emotion recognition on the CMU-MOSEI
dataset
A Survey on Dialogue Summarization: Recent Advances and New Frontiers
With the development of dialogue systems and natural language generation
techniques, the resurgence of dialogue summarization has attracted significant
research attentions, which aims to condense the original dialogue into a
shorter version covering salient information. However, there remains a lack of
comprehensive survey for this task. To this end, we take the first step and
present a thorough review of this research field. In detail, we provide an
overview of publicly available research datasets, summarize existing works
according to the domain of input dialogue as well as organize leaderboards
under unified metrics. Furthermore, we discuss some future directions and give
our thoughts. We hope that this first survey of dialogue summarization can
provide the community with a quick access and a general picture to this task
and motivate future researches
Improving Online Forums Summarization via Unifying Hierarchical Attention Networks with Convolutional Neural Networks
Online discussion forums are prevalent and easily accessible, thus allowing
people to share ideas and opinions by posting messages in the discussion
threads. Forum threads that significantly grow in length can become difficult
for participants, both newcomers and existing, to grasp main ideas. This study
aims to create an automatic text summarizer for online forums to mitigate this
problem. We present a framework based on hierarchical attention networks,
unifying Bidirectional Long Short-Term Memory (Bi-LSTM) and Convolutional
Neural Network (CNN) to build sentence and thread representations for the forum
summarization. In this scheme, Bi-LSTM derives a representation that comprises
information of the whole sentence and whole thread; whereas, CNN recognizes
high-level patterns of dominant units with respect to the sentence and thread
context. The attention mechanism is applied on top of CNN to further highlight
the high-level representations that capture any important units contributing to
a desirable summary. Extensive performance evaluation based on three datasets,
two of which are real-life online forums and one is news dataset, reveals that
the proposed model outperforms several competitive baselines.Comment: 27 pages, 7 figure