Search Results for author:
Found 19 papers, 4 papers with code
When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets
Using large language models (LMs) for query or document expansion can improve generalization in information retrieval.
"According to ..." Prompting Language Models Improves Quoting from Pre-Training Data
Large Language Models (LLMs) may hallucinate and generate fake information, despite pre-training on factual data.
NevIR: Negation in Neural Information Retrieval
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.
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.
When Do Decompositions Help for Machine Reading?
Answering complex questions often requires multi-step reasoning in order to obtain the final answer.
Defending Against Misinformation Attacks in Open-Domain Question Answering
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.
Neural Approaches to Multilingual Information Retrieval
Providing access to information across languages has been a goal of Information Retrieval (IR) for decades.
Pretrained Models for Multilingual Federated Learning
Since the advent of Federated Learning (FL), research has applied these methods to natural language processing (NLP) tasks.
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.
Building OCR/NER Test Collections
This means that named entities are annotated on the transcribed text.
Improving Neural Named Entity Recognition with Gazetteers
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.
CADET: Computer Assisted Discovery Extraction and Translation
It combines a multitude of analytics together with a flexible environment for customizing the workflow for different users.
Creating and Curating a Cross-Language Person-Entity Linking Collection
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.
