In the last few years, neural representation learning approaches have achieved very good performance on many natural language processing (NLP) tasks, such as language modelling and machine translation. This suggests that neural models will also yield significant performance improvements on information retrieval (IR) tasks, such as relevance ranking, addressing the query-document vocabulary mismatch problem by using semantic rather than lexical matching. IR tasks, however, are fundamentally different from NLP tasks leading to new challenges and opportunities for existing neural representation learning approaches for text.
We begin this talk with a discussion on text embedding spaces for modelling different types of relationships between items which makes them suitable for different IR tasks. Next, we present how topic-specific representations can be more effective than learning global embeddings. Finally, we conclude with an emphasis on dealing with rare terms and concepts for IR, and how embedding based approaches can be augmented with neural models for lexical matching for better retrieval performance. While our discussions are grounded in IR tasks, the findings and the insights covered during this talk should be generally applicable to other NLP and machine learning tasks.