Prior work on English monolingual retrieval has shown that a cross-encoder
trained using a large number of relevance judgments for query-document pairs
can be used as a teacher to train more efficient, but similarly effective,
dual-encoder student models. Applying a similar knowledge distillation approach
to training an efficient dual-encoder model for Cross-Language Information
Retrieval (CLIR), where queries and documents are in different languages, is
challenging due to the lack of a sufficiently large training collection when
the query and document languages differ. The state of the art for CLIR thus
relies on translating queries, documents, or both from the large English MS
MARCO training set, an approach called Translate-Train. This paper proposes an
alternative, Translate-Distill, in which knowledge distillation from either a
monolingual cross-encoder or a CLIR cross-encoder is used to train a
dual-encoder CLIR student model. This richer design space enables the teacher
model to perform inference in an optimized setting, while training the student
model directly for CLIR. Trained models and artifacts are publicly available on
Huggingface.

该研究提出了一种名为 Translate-Distill 的方法，它利用交叉编码器或 CLIR 交叉编码器的知识蒸馏来训练双编码器 CLIR 学生模型。

Translate-Distill：通过翻译和蒸馏学习跨语言稠密检索

Translate-Distill: Learning Cross-Language Dense Retrieval by  Translation and Distillation

HC4 is a new suite of test collections for ad hoc Cross-Language Information
Retrieval (CLIR), with Common Crawl News documents in Chinese, Persian, and
Russian, topics in English and in the document languages, and graded relevance
judgments. New test collections are needed because existing CLIR test
collections built using pooling of traditional CLIR runs have systematic gaps
in their relevance judgments when used to evaluate neural CLIR methods. The HC4
collections contain 60 topics and about half a million documents for each of
Chinese and Persian, and 54 topics and five million documents for Russian.
Active learning was used to determine which documents to annotate after being
seeded using interactive search and judgment. Documents were judged on a
three-grade relevance scale. This paper describes the design and construction
of the new test collections and provides baseline results for demonstrating
their utility for evaluating systems.

本文章介绍了一种新的用于跨语言信息检索的测试集合 HC4，并利用交互搜索和判断以及主动学习方法来构建测试集合，以评估神经 CLIR 方法的效用及提供基准结果。

HC4: 用于 Ad Hoc CLIR 的新测试集

HC4: A New Suite of Test Collections for Ad Hoc CLIR

We present DR.DECR (Dense Retrieval with Distillation-Enhanced Cross-Lingual
Representation), a new cross-lingual information retrieval (CLIR) system
trained using multi-stage knowledge distillation (KD). The teacher of DR.DECR
relies on a highly effective but computationally expensive two-stage inference
process consisting of query translation and monolingual IR, while the student,
DR.DECR, executes a single CLIR step. We teach DR.DECR powerful multilingual
representations as well as CLIR by optimizing two corresponding KD objectives.
Learning useful representations of non-English text from an English-only
retriever is accomplished through a cross-lingual token alignment algorithm
that relies on the representation capabilities of the underlying multilingual
encoders. In both in-domain and zero-shot out-of-domain evaluation, DR.DECR
demonstrates far superior accuracy over direct fine-tuning with labeled CLIR
data. It is also the best single-model retriever on the XOR-TyDi benchmark at
the time of this writing.

使用多阶段知识蒸馏训练的 DR.DECR 是一种新的跨语言信息检索 (CLIR) 系统，其学习了强大的多语言表示以及简化的 CLIR，具有比使用有标记的 CLIR 数据进行直接微调更高的准确性。