The success of the large neural language models on many NLP tasks is exciting. However,
we find that these successes sometimes lead to hype in which these models are being …

Advancements in neural networks have led to developments in fields like computer vision,
speech recognition and natural language processing (NLP). One of the most influential …

Sentiment analysis concerns the study of opinions expressed in a text. Due to the huge
amount of reviews, sentiment analysis plays a basic role to extract significant information …

We study a formalization of the grammar induction problem that models sentences as being
generated by a compound probabilistic context-free grammar. In contrast to traditional …

There has been much recent, exciting work on combining the complementary strengths of
latent variable models and deep learning. Latent variable modeling makes it easy to …

Word embedding has been proven to be a useful model for various natural language
processing tasks. Traditional word embedding methods merely take into account word …

Compared with single-label image classification, multi-label image classification is more
practical and challenging. Some recent studies attempted to leverage the semantic …

Dense word embeddings, which encode meanings of words to low-dimensional vector
spaces, have become very popular in natural language processing (NLP) research due to …

We introduce an extension to the bag-ofwords model for learning words representations that
take into account both syntactic and semantic properties within language. This is done by …

In the absence of annotations in the target language, multilingual models typically draw on
extensive parallel resources. In this paper, we demonstrate that accurate multilingual partof …