This paper provides an extensive and thorough overview of the models and techniques
utilized in the first and second stages of the typical information retrieval processing chain …

Multi-stage ranking pipelines have been a practical solution in modern search systems,
where the first-stage retrieval is to return a subset of candidate documents and latter stages …

The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in
response to a query. Although the most common formulation of text ranking is search …

We propose a new task, Text2Mol, to retrieve molecules using natural language descriptions
as queries. Natural language and molecules encode information in very different ways …

Cross-lingual representations of words enable us to reason about word meaning in
multilingual contexts and are a key facilitator of cross-lingual transfer when develo** …

This paper introduces Explicit Semantic Ranking (ESR), a new ranking technique that
leverages knowledge graph embedding. Analysis of the query log from our academic search …

Neural ranking models for information retrieval (IR) use shallow or deep neural networks to
rank search results in response to a query. Traditional learning to rank models employ …

The core of information retrieval (IR) is to identify relevant information from large-scale
resources and return it as a ranked list to respond to user's information need. In recent years …

Abstract We present Attract-Repel, an algorithm for improving the semantic quality of word
vectors by injecting constraints extracted from lexical resources. Attract-Repel facilitates the …

Cross-lingual word embeddings (CLEs) enable multilingual modeling of meaning and
facilitate cross-lingual transfer of NLP models. Despite their ubiquitous usage in downstream …