25 citations · 60 across the 21 of their papers we have counts for
22 papers
On the Evaluation of Machine-Generated Reports
James Mayfield, Eugene Yang, Dawn Lawrie +10
Large Language Models (LLMs) have enabled new ways to satisfy information needs. Although great strides have been made in applying them to settings like document ranking and short-…
Language Fairness in Multilingual Information Retrieval
Eugene Yang, Thomas Jänich, James Mayfield +1
Multilingual information retrieval (MLIR) considers the problem of ranking documents in several languages for a query expressed in a language that may differ from any of those lang…
Distillation for Multilingual Information Retrieval
Eugene Yang, Dawn Lawrie, James Mayfield
Recent work in cross-language information retrieval (CLIR), where queries and documents are in different languages, has shown the benefit of the Translate-Distill framework that tr…
PLAID SHIRTTT for Large-Scale Streaming Dense Retrieval
Dawn Lawrie, Efsun Kayi, Eugene Yang +2
PLAID, an efficient implementation of the ColBERT late interaction bi-encoder using pretrained language models for ranking, consistently achieves state-of-the-art performance in mo…
Efficiency-Effectiveness Tradeoff of Probabilistic Structured Queries for Cross-Language Information Retrieval
Eugene Yang, Suraj Nair, Dawn Lawrie +3
Probabilistic Structured Queries (PSQ) is a cross-language information retrieval (CLIR) method that uses translation probabilities statistically derived from aligned corpora. PSQ i…
Extending Translate-Train for ColBERT-X to African Language CLIR
Eugene Yang, Dawn J. Lawrie, Paul McNamee +1
This paper describes the submission runs from the HLTCOE team at the CIRAL CLIR tasks for African languages at FIRE 2023. Our submissions use machine translation models to translat…
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 En…
Overview of the TREC 2023 NeuCLIR Track
Dawn Lawrie, Sean MacAvaney, James Mayfield +4
The principal goal of the TREC Neural Cross-Language Information Retrieval (NeuCLIR) track is to study the impact of neural approaches to cross-language information retrieval. The…
Translate-Distill: Learning Cross-Language Dense Retrieval by Translation and Distillation
Eugene Yang, Dawn Lawrie, James Mayfield +2
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…
When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets
Orion Weller, Kyle Lo, David Wadden +4
Using large language models (LMs) for query or document expansion can improve generalization in information retrieval. However, it is unknown whether these techniques are universal…
"According to ...": Prompting Language Models Improves Quoting from Pre-Training Data
Orion Weller, Marc Marone, Nathaniel Weir +3
Large Language Models (LLMs) may hallucinate and generate fake information, despite pre-training on factual data. Inspired by the journalistic device of "according to sources", we…
NevIR: Negation in Neural Information Retrieval
Orion Weller, Dawn Lawrie, Benjamin Van Durme
Negation is a common everyday phenomena and has been a consistent area of weakness for language models (LMs). Although the Information Retrieval (IR) community has adopted LMs as t…
Synthetic Cross-language Information Retrieval Training Data
James Mayfield, Eugene Yang, Dawn Lawrie +5
A key stumbling block for neural cross-language information retrieval (CLIR) systems has been the paucity of training data. The appearance of the MS MARCO monolingual training set…
Overview of the TREC 2022 NeuCLIR Track
Dawn Lawrie, Sean MacAvaney, James Mayfield +4
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. The main task in this y…
When Do Decompositions Help for Machine Reading?
Kangda Wei, Dawn Lawrie, Benjamin Van Durme +2
Answering complex questions often requires multi-step reasoning in order to obtain the final answer. Most research into decompositions of complex questions involves open-domain sys…
Defending Against Disinformation Attacks in Open-Domain Question Answering
Orion Weller, Aleem Khan, Nathaniel Weir +2
Recent work in open-domain question answering (ODQA) has shown that adversarial poisoning of the search collection can cause large drops in accuracy for production systems. However…
Neural Approaches to Multilingual Information Retrieval
Dawn Lawrie, Eugene Yang, Douglas W. Oard +1
Providing access to information across languages has been a goal of Information Retrieval (IR) for decades. While progress has been made on Cross Language IR (CLIR) where queries a…
Pretrained Models for Multilingual Federated Learning
Orion Weller, Marc Marone, Vladimir Braverman +2
Since the advent of Federated Learning (FL), research has applied these methods to natural language processing (NLP) tasks. Despite a plethora of papers in FL for NLP, no previous…
Patapasco: A Python Framework for Cross-Language Information Retrieval Experiments
Cash Costello, Eugene Yang, Dawn Lawrie +1
While there are high-quality software frameworks for information retrieval experimentation, they do not explicitly support cross-language information retrieval (CLIR). To fill this…
HC4: A New Suite of Test Collections for Ad Hoc CLIR
Dawn Lawrie, James Mayfield, Douglas Oard +1
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…
Transfer Learning Approaches for Building Cross-Language Dense Retrieval Models
Suraj Nair, Eugene Yang, Dawn Lawrie +5
The advent of transformer-based models such as BERT has led to the rise of neural ranking models. These models have improved the effectiveness of retrieval systems well beyond that…
Improving Neural Named Entity Recognition with Gazetteers
Chan Hee Song, Dawn Lawrie, Tim Finin +1
The goal of this work is to improve the performance of a neural named entity recognition system by adding input features that indicate a word is part of a name included in a gazett…