Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the Effects of Using word2vec Representations in Neural Networks for
  Dialogue Act Recognition   Dialogue act recognition is an important component of a large number of
natural language processing pipelines. Many research works have been carried
out in this area, but relatively few investigate deep neural networks and word
embeddings. This is surprising, given that both of these techniques have proven
exceptionally good in most other language-related domains. We propose in this
work a new deep neural network that explores recurrent models to capture word
sequences within sentences, and further study the impact of pretrained word
embeddings. We validate this model on three languages: English, French and
Czech. The performance of the proposed approach is consistent across these
languages and it is comparable to the state-of-the-art results in English. More
importantly, we confirm that deep neural networks indeed outperform a Maximum
Entropy classifier, which was expected. However , and this is more surprising,
we also found that standard word2vec em-beddings do not seem to bring valuable
information for this task and the proposed model, whatever the size of the
training corpus is. We thus further analyse the resulting embeddings and
conclude that a possible explanation may be related to the mismatch between the
type of lexical-semantic information captured by the word2vec embeddings, and
the kind of relations between words that is the most useful for the dialogue
act recognition task.

21
The categories of this paper are: adap-org
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph