Search Results for author: Dawn Lawrie

Found 20 papers, 8 papers with code

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

1 code implementation9 Jan 2024 Eugene Yang, Dawn Lawrie, James Mayfield, Douglas W. Oard, Scott Miller

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.

Information Retrieval Knowledge Distillation +2

"According to ...": Prompting Language Models Improves Quoting from Pre-Training Data

no code implementations22 May 2023 Orion Weller, Marc Marone, Nathaniel Weir, Dawn Lawrie, Daniel Khashabi, Benjamin Van Durme

Large Language Models (LLMs) may hallucinate and generate fake information, despite pre-training on factual data.

NevIR: Negation in Neural Information Retrieval

1 code implementation12 May 2023 Orion Weller, Dawn Lawrie, Benjamin Van Durme

Although the Information Retrieval (IR) community has adopted LMs as the backbone of modern IR architectures, there has been little to no research in understanding how negation impacts neural IR.

Information Retrieval Negation +1

Synthetic Cross-language Information Retrieval Training Data

no code implementations29 Apr 2023 James Mayfield, Eugene Yang, Dawn Lawrie, Samuel Barham, Orion Weller, Marc Mason, Suraj Nair, Scott Miller

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.

Information Retrieval Language Modelling +4

Overview of the TREC 2022 NeuCLIR Track

no code implementations24 Apr 2023 Dawn Lawrie, Sean MacAvaney, James Mayfield, Paul McNamee, Douglas W. Oard, Luca Soldaini, Eugene Yang

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.

Information Retrieval Retrieval

Parameter-efficient Zero-shot Transfer for Cross-Language Dense Retrieval with Adapters

no code implementations20 Dec 2022 Eugene Yang, Suraj Nair, Dawn Lawrie, James Mayfield, Douglas W. Oard

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.

Information Retrieval Language Modelling +1

When Do Decompositions Help for Machine Reading?

no code implementations20 Dec 2022 Kangda Wei, Dawn Lawrie, Benjamin Van Durme, Yunmo Chen, Orion Weller

Answering complex questions often requires multi-step reasoning in order to obtain the final answer.

Reading Comprehension Retrieval

Defending Against Disinformation Attacks in Open-Domain Question Answering

no code implementations20 Dec 2022 Orion Weller, Aleem Khan, Nathaniel Weir, Dawn Lawrie, Benjamin Van Durme

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.

Data Poisoning Misinformation +1

Neural Approaches to Multilingual Information Retrieval

1 code implementation3 Sep 2022 Dawn Lawrie, Eugene Yang, Douglas W. Oard, James Mayfield

Providing access to information across languages has been a goal of Information Retrieval (IR) for decades.

Document Translation Information Retrieval +3

Patapasco: A Python Framework for Cross-Language Information Retrieval Experiments

1 code implementation24 Jan 2022 Cash Costello, Eugene Yang, Dawn Lawrie, James Mayfield

While there are high-quality software frameworks for information retrieval experimentation, they do not explicitly support cross-language information retrieval (CLIR).

Information Retrieval Retrieval

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

1 code implementation24 Jan 2022 Dawn Lawrie, James Mayfield, Douglas Oard, Eugene Yang

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.

Active Learning Information Retrieval +1

Improving Neural Named Entity Recognition with Gazetteers

1 code implementation6 Mar 2020 Chan Hee Song, Dawn Lawrie, Tim Finin, James Mayfield

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 gazetteer.

named-entity-recognition Named Entity Recognition +1

Creating and Curating a Cross-Language Person-Entity Linking Collection

no code implementations LREC 2012 Dawn Lawrie, James Mayfield, Paul McNamee, Douglas Oard

To stimulate research in cross-language entity linking, we present a new test collection for evaluating the accuracy of cross-language entity linking in twenty-one languages.

Entity Linking Knowledge Base Population +1

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