Data collection and mining is a crucial bottleneck for cross-lingual information retrieval
(CLIR). While previous works used machine translation and iterative training, we present a …

Retrieval-augmented generation (RAG) has recently emerged as a promising solution for
incorporating up-to-date or domain-specific knowledge into large language models (LLMs) …

In today's digital age, accessing information across language barriers poses a significant
challenge, with conventional search systems often struggling to interpret and retrieve …

Effective cross-lingual dense retrieval methods that rely on multilingual pre-trained language
models (PLMs) need to be trained to encompass both the relevance matching task and the …

In monolingual dense retrieval, lots of works focus on how to distill knowledge from cross-
encoder re-ranker to dual-encoder retriever and these methods achieve better performance …

In the contemporary digital landscape, search engines play an invaluable role in information
access, yet they often face challenges in Cross-Lingual Information Retrieval (CLIR) …

Translating major language resources to build minor language resources becomes a widely-
used approach. Particularly in translating complex data points composed of multiple …

Code search is an important task that has seen many developments in recent years.
However, previous attempts have mostly considered the problem of searching for code by a …

Recent approaches to multilingual opendomain question answering (MLODQA) have
achieved promising results given abundant language-specific training data. However, the …

Clone detection is a well known task, which could be formulated on any programming
language. Although to the best of our knowledge there is no cross-lingual clone detection …