Papers by Christopher Ré

7 papers
Reasoning over Public and Private Data in Retrieval-Based Systems (2023.tacl-1)

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Challenge: Existing retrieval systems assume relevant corpora are fully (e.g., publicly) accessible, but users are often unwilling to expose their private data to entities hosting public data.
Approach: They propose a split iterative retrieval problem involving iterating retrieval over multiple privacy scopes and propose 'concurrentQA' benchmark to test this problem.
Outcome: The proposed method improves on the existing retrieval methods but still suffers performance degradations when applied to a dataset from a public and private distribution.
Goodwill Hunting: Analyzing and Repurposing Off-the-Shelf Named Entity Linking Systems (2021.naacl-industry)

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Challenge: Named entity linking (NEL) is a preprocessing step in commercial systems . a small organization or individual could use an off-the-shelf system to accomplish the same objectives .
Approach: They examine how to repurpose off-the-shelf NEL systems to correct sport-related errors.
Outcome: The proposed model can improve sports question-answering accuracy by 25% . the proposed model is based on the best available model .
Low-Dimensional Hyperbolic Knowledge Graph Embeddings (2020.acl-main)

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Challenge: Existing methods for predicting missing facts do not account for hierarchical and logical patterns in KGs.
Approach: They propose a class of hyperbolic KG embedding models that capture hierarchical and logical patterns.
Outcome: Experimental results show that the proposed method improves by 6.1% in mean reciprocal rank in low dimensions over previous methods.
Robustness Gym: Unifying the NLP Evaluation Landscape (2021.naacl-demos)

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Challenge: Existing tools cater to specialized set of evaluations and provide no clear way to leverage or share findings from prior evaluations.
Approach: They propose a toolkit that unifies 4 evaluation paradigms to provide a common platform for evaluation.
Outcome: The proposed evaluation toolkit unifies 4 evaluation paradigms and is under active development.
Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text (2021.findings-emnlp)

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Challenge: Existing methods for named entity disambiguation are limited by coarse-grained structural resources in biomedical knowledge bases and training datasets that provide low coverage over uncommon resources.
Approach: They propose a method that integrates structural knowledge from general text knowledge bases to the medical domain.
Outcome: The proposed method improves disambiguation accuracy on two benchmark medical NED datasets by up to 57 points.
Contextual Embeddings: When Are They Worth It? (2020.acl-main)

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Challenge: In recent years, rich contextual embeddings have enabled rapid progress on benchmarks like GLUE, but require significant computational resources during pretraining and during downstream task training and inference.
Approach: They empirically compare contextual embeddings with classic pretrained embedders and a random word embeddable with a simple baseline.
Outcome: The proposed models perform within 5 to 10% accuracy on industry-scale data.
Training Classifiers with Natural Language Explanations (P18-1)

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Challenge: a semantic parser converts explanations into programmatic labeling functions . a standard protocol for obtaining a labeled dataset provides only one bit of information per example .
Approach: They propose a framework where an annotator provides an explanation for each labeling decision . they use a semantic parser to convert these explanations into programmatic labeling functions .
Outcome: The proposed framework trains classifiers faster by providing explanations instead of labels . the proposed framework is based on a rule-based semantic parser .

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