Search Results for author:
Found 22 papers, 8 papers with code
HLTCOE at TREC 2023 NeuCLIR Track
TT trains a ColBERT model with English queries and passages automatically translated into the document language from the MS-MARCO v1 collection.
Extending Translate-Train for ColBERT-X to African Language CLIR
This paper describes the submission runs from the HLTCOE team at the CIRAL CLIR tasks for African languages at FIRE 2023.
Overview of the TREC 2023 NeuCLIR Track
The principal tasks are ranked retrieval of news in one of the three languages, using English topics.
Translate-Distill: Learning Cross-Language Dense Retrieval by Translation and Distillation
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.
Synthetic Cross-language Information Retrieval Training Data
By repeating this process, collections of arbitrary size can be created in the style of MS MARCO but using naturally-occurring documents in any desired genre and domain of discourse.
Overview of the TREC 2022 NeuCLIR Track
This is the first year of the TREC Neural CLIR (NeuCLIR) track, which aims to study the impact of neural approaches to cross-language information retrieval.
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.
TARexp: A Python Framework for Technology-Assisted Review Experiments
Technology-assisted review (TAR) is an important industrial application of information retrieval (IR) and machine learning (ML).
HC4: A New Suite of Test Collections for Ad Hoc CLIR
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.
Patapasco: A Python Framework for Cross-Language Information Retrieval Experiments
While there are high-quality software frameworks for information retrieval experimentation, they do not explicitly support cross-language information retrieval (CLIR).
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.
Certifying One-Phase Technology-Assisted Reviews
Technology-assisted review (TAR) workflows based on iterative active learning are widely used in document review applications.
TAR on Social Media: A Framework for Online Content Moderation
Content moderation (removing or limiting the distribution of posts based on their contents) is one tool social networks use to fight problems such as harassment and disinformation.
Heuristic Stopping Rules For Technology-Assisted Review
Technology-assisted review (TAR) refers to human-in-the-loop active learning workflows for finding relevant documents in large collections.
On Minimizing Cost in Legal Document Review Workflows
Technology-assisted review (TAR) refers to human-in-the-loop machine learning workflows for document review in legal discovery and other high recall review tasks.
Goldilocks: Just-Right Tuning of BERT for Technology-Assisted Review
We indeed find that the pre-trained BERT model reduces review cost by 10% to 15% in TAR workflows simulated on the RCV1-v2 newswire collection.
ToxCCIn: Toxic Content Classification with Interpretability
Despite the recent successes of transformer-based models in terms of effectiveness on a variety of tasks, their decisions often remain opaque to humans.
GUIR at SemEval-2020 Task 12: Domain-Tuned Contextualized Models for Offensive Language Detection
Offensive language detection is an important and challenging task in natural language processing.
