Interpretable semantic representations from neural language models and computer vision

Today, the field of computational linguistics is dominated by high capacity neural models that in many cases outperform even human baselines on a wide variety of language-based tasks. However, a serious drawback to the development of these semantic models is in the ambiguity of these representations, as the dimensions of these feature vectors are no longer characterised by clear, recognisable units of meaning.

Even though these models have produced state-of-the-art performance on natural language tasks, it has come at the cost of interpretability of both the models and the word representations derived from them. Because of their lack of interpretability, it can be difficult for researchers to gain a deeper understanding of the types of knowledge that these semantic models actually represent, or make incremental improvements towards more structured representations.