Search Results for author: Allen Schmaltz

Found 10 papers, 6 papers with code

Detecting Local Insights from Global Labels: Supervised and Zero-Shot Sequence Labeling via a Convolutional Decomposition

1 code implementation CL (ACL) 2021 Allen Schmaltz

From this sequence-labeling layer we derive dense representations of the input that can then be matched to instances from training, or a support set with known labels.

Approximate Conditional Coverage & Calibration via Neural Model Approximations

no code implementations28 May 2022 Allen Schmaltz, Danielle Rasooly

A typical desideratum for quantifying the uncertainty from a classification model as a prediction set is class-conditional singleton set calibration.

Classification Document Classification +4

Coarse-to-Fine Memory Matching for Joint Retrieval and Classification

1 code implementation29 Nov 2020 Allen Schmaltz, Andrew Beam

We present a novel end-to-end language model for joint retrieval and classification, unifying the strengths of bi- and cross- encoders into a single language model via a coarse-to-fine memory matching search procedure for learning and inference.

Classification Fact Verification +3

Exemplar Auditing for Multi-Label Biomedical Text Classification

no code implementations7 Apr 2020 Allen Schmaltz, Andrew Beam

These challenges are compounded for modalities such as text, where the feature space is very high-dimensional, and often contains considerable amounts of noise.

General Classification Multi-Label Classification +2

Detecting Local Insights from Global Labels: Supervised & Zero-Shot Sequence Labeling via a Convolutional Decomposition

1 code implementation4 Jun 2019 Allen Schmaltz

From this sequence-labeling layer we derive dense representations of the input that can then be matched to instances from training, or a support set with known labels.

Grammatical Error Detection Text Generation +1

On the Utility of Lay Summaries and AI Safety Disclosures: Toward Robust, Open Research Oversight

no code implementations WS 2018 Allen Schmaltz

In this position paper, we propose that the community consider encouraging researchers to include two riders, a {``}Lay Summary{''} and an {``}AI Safety Disclosure{''}, as part of future NLP papers published in ACL forums that present user-facing systems.

Machine Translation

Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data

4 code implementations4 Apr 2018 Andrew L. Beam, Benjamin Kompa, Allen Schmaltz, Inbar Fried, Griffin Weber, Nathan P. Palmer, Xu Shi, Tianxi Cai, Isaac S. Kohane

Word embeddings are a popular approach to unsupervised learning of word relationships that are widely used in natural language processing.

Test Word Embeddings

Adapting Sequence Models for Sentence Correction

1 code implementation EMNLP 2017 Allen Schmaltz, Yoon Kim, Alexander M. Rush, Stuart M. Shieber

In a controlled experiment of sequence-to-sequence approaches for the task of sentence correction, we find that character-based models are generally more effective than word-based models and models that encode subword information via convolutions, and that modeling the output data as a series of diffs improves effectiveness over standard approaches.

Machine Translation Sentence +1

Word Ordering Without Syntax

1 code implementation EMNLP 2016 Allen Schmaltz, Alexander M. Rush, Stuart M. Shieber

Recent work on word ordering has argued that syntactic structure is important, or even required, for effectively recovering the order of a sentence.

Language Modelling Sentence

Sentence-Level Grammatical Error Identification as Sequence-to-Sequence Correction

no code implementations WS 2016 Allen Schmaltz, Yoon Kim, Alexander M. Rush, Stuart M. Shieber

We demonstrate that an attention-based encoder-decoder model can be used for sentence-level grammatical error identification for the Automated Evaluation of Scientific Writing (AESW) Shared Task 2016.

Sentence

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