Text embeddings are useful features in many applications such as semantic search and
computing text similarity. Previous work typically trains models customized for different use …

It has been shown that dual encoders trained on one domain often fail to generalize to other
domains for retrieval tasks. One widespread belief is that the bottleneck layer of a dual …

Text retrieval is a long-standing research topic on information seeking, where a system is
required to return relevant information resources to user's queries in natural language. From …

In various natural language processing tasks, passage retrieval and passage re-ranking are
two key procedures in finding and ranking relevant information. Since both the two …

Abstract Retrieval Augment Generation (RAG) is a recent advancement in Open-Domain
Question Answering (ODQA). RAG has only been trained and explored with a Wikipedia …

Current state-of-the-art document retrieval solutions mainly follow an index-retrieve
paradigm, where the index is hard to be directly optimized for the final retrieval target. In this …

We propose a simple and effective re-ranking method for improving passage retrieval in
open question answering. The re-ranker re-scores retrieved passages with a zero-shot …

We present an end-to-end differentiable training method for retrieval-augmented open-
domain question answering systems that combine information from multiple retrieved …

Dense retrieval has become a prominent method to obtain relevant context or world
knowledge in open-domain NLP tasks. When we use a learned dense retriever on a …

Current dense text retrieval models face two typical challenges. First, they adopt a siamese
dual-encoder architecture to encode queries and documents independently for fast indexing …