Search Results for author:
Found 18 papers, 3 papers with code
PanPhon: A Resource for Mapping IPA Segments to Articulatory Feature Vectors
This paper contributes to a growing body of evidence that{---}when coupled with appropriate machine-learning techniques{--}linguistically motivated, information-rich representations can outperform one-hot encodings of linguistic data.
Mix and Match: Learning-free Controllable Text Generation using Energy Language Models
Recent work on controlled text generation has either required attribute-based fine-tuning of the base language model (LM), or has restricted the parameterization of the attribute discriminator to be compatible with the base autoregressive LM.
Mix and Match: Learning-free Controllable Text Generationusing Energy Language Models
Recent work on controlled text generation has either required attribute-based fine-tuning of the base language model (LM), or has restricted the parameterization of the attribute discriminator to be compatible with the base autoregressive LM.
A Continuous Relaxation of Beam Search for End-to-end Training of Neural Sequence Models
In experiments, we show that optimizing this new training objective yields substantially better results on two sequence tasks (Named Entity Recognition and CCG Supertagging) when compared with both cross entropy trained greedy decoding and cross entropy trained beam decoding baselines.
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Differentiable Scheduled Sampling for Credit Assignment
We demonstrate that a continuous relaxation of the argmax operation can be used to create a differentiable approximation to greedy decoding for sequence-to-sequence (seq2seq) models.
Named Entity Recognition for Linguistic Rapid Response in Low-Resource Languages: Sorani Kurdish and Tajik
This paper describes our construction of named-entity recognition (NER) systems in two Western Iranian languages, Sorani Kurdish and Tajik, as a part of a pilot study of {``}Linguistic Rapid Response{''} to potential emergency humanitarian relief situations.
An Empirical Investigation of Global and Local Normalization for Recurrent Neural Sequence Models Using a Continuous Relaxation to Beam Search
Globally normalized neural sequence models are considered superior to their locally normalized equivalents because they may ameliorate the effects of label bias.
Bridge-Language Capitalization Inference in Western Iranian: Sorani, Kurmanji, Zazaki, and Tajik
In Sorani Kurdish, one of the most useful orthographic features in named-entity recognition {--} capitalization {--} is absent, as the language{'}s Perso-Arabic script does not make a distinction between uppercase and lowercase letters.
A Probabilistic Generative Model for Typographical Analysis of Early Modern Printing
We show that our approach outperforms rigid interpretable clustering baselines (Ocular) and overly-flexible deep generative models (VAE) alike on the task of completely unsupervised discovery of typefaces in mixed-font documents.
Exposing the Implicit Energy Networks behind Masked Language Models via Metropolis--Hastings
While recent work has shown that scores from models trained by the ubiquitous masked language modeling (MLM) objective effectively discriminate probable from improbable sequences, it is still an open question if these MLMs specify a principled probability distribution over the space of possible sequences.
Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks
The wide adoption and application of Masked language models~(MLMs) on sensitive data (from legal to medical) necessitates a thorough quantitative investigation into their privacy vulnerabilities -- to what extent do MLMs leak information about their training data?
Contrastive Attention Networks for Attribution of Early Modern Print
Specifically, we focus on matching uniquely damaged character type-imprints in anonymously printed books to works with known printers in order to provide evidence of their origins.
MAP's not dead yet: Uncovering true language model modes by conditioning away degeneracy
It has been widely observed that exact or approximate MAP (mode-seeking) decoding from natural language generation (NLG) models consistently leads to degenerate outputs (Stahlberg and Byrne, 2019, Holtzman et al., 2019).
