HLTCOE at TREC 2023 NeuCLIR Track
Eugene Yang Dawn Lawrie James Mayfield
The HLTCOE team applied PLAID, an mT5 reranker, and document translation to
the TREC 2023 NeuCLIR track. For PLAID we included a variety of models and
training techniques -- the English model released with ColBERT v2,
translate-train~(TT), Translate Distill~(TD) and multilingual
translate-train~(MTT). TT trains a ColBERT model with English queries and
passages automatically translated into the document language from the MS-MARCO
v1 collection. This results in three cross-language models for the track, one
per language. MTT creates a single model for all three document languages by
combining the translations of MS-MARCO passages in all three languages into
mixed-language batches. Thus the model learns about matching queries to
passages simultaneously in all languages. Distillation uses scores from the mT5
model over non-English translated document pairs to learn how to score
query-document pairs. The team submitted runs to all NeuCLIR tasks: the CLIR
and MLIR news task as well as the technical documents task.