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Found 12 papers, 0 papers with code
Parameter-efficient Zero-shot Transfer for Cross-Language Dense Retrieval with Adapters
By adding adapters pretrained on language tasks for a specific language with task-specific adapters, prior work has shown that the adapter-enhanced models perform better than fine-tuning the entire model when transferring across languages in various NLP tasks.
Neural Approaches to Multilingual Information Retrieval
Providing access to information across languages has been a goal of Information Retrieval (IR) for decades.
C3: Continued Pretraining with Contrastive Weak Supervision for Cross Language Ad-Hoc Retrieval
Pretrained language models have improved effectiveness on numerous tasks, including ad-hoc retrieval.
Transfer Learning Approaches for Building Cross-Language Dense Retrieval Models
These models have improved the effectiveness of retrieval systems well beyond that of lexical term matching models such as BM25.
Effects of context, complexity, and clustering on evaluation for math formula retrieval
There are now several test collections for the formula retrieval task, in which a system's goal is to identify useful mathematical formulae to show in response to a query posed as a formula.
Cross-language Information Retrieval
Two key assumptions shape the usual view of ranked retrieval: (1) that the searcher can choose words for their query that might appear in the documents that they wish to see, and (2) that ranking retrieved documents will suffice because the searcher will be able to recognize those which they wished to find.
Cross-language Sentence Selection via Data Augmentation and Rationale Training
This paper proposes an approach to cross-language sentence selection in a low-resource setting.
The Multilingual TEDx Corpus for Speech Recognition and Translation
We present the Multilingual TEDx corpus, built to support speech recognition (ASR) and speech translation (ST) research across many non-English source languages.
Providing More Efficient Access To Government Records: A Use Case Involving Application of Machine Learning to Improve FOIA Review for the Deliberative Process Privilege
At present, the review process for material that is exempt from disclosure under the Freedom of Information Act (FOIA) in the United States of America, and under many similar government transparency regimes worldwide, is entirely manual.
