Decoding Sentiment from Distributed Representations of Sentences
This work addresses the problem of understanding sentiment decoding from sentence representations for NLP researchers, but it is incremental as it builds on existing methods without introducing new paradigms.
The study investigated how much sentiment information is retained in unsupervised sentence representations across nine languages, finding that no single architecture consistently outperforms others and that additive models can sometimes surpass supervised state-of-the-art methods like bidirectional LSTMs.
Distributed representations of sentences have been developed recently to represent their meaning as real-valued vectors. However, it is not clear how much information such representations retain about the polarity of sentences. To study this question, we decode sentiment from unsupervised sentence representations learned with different architectures (sensitive to the order of words, the order of sentences, or none) in 9 typologically diverse languages. Sentiment results from the (recursive) composition of lexical items and grammatical strategies such as negation and concession. The results are manifold: we show that there is no `one-size-fits-all' representation architecture outperforming the others across the board. Rather, the top-ranking architectures depend on the language and data at hand. Moreover, we find that in several cases the additive composition model based on skip-gram word vectors may surpass supervised state-of-art architectures such as bidirectional LSTMs. Finally, we provide a possible explanation of the observed variation based on the type of negative constructions in each language.