74 papers with code • 1 benchmarks • 1 datasets

This task has no description! Would you like to contribute one?

Most implemented papers

UniMorph 2.0: Universal Morphology

sigmorphon/2022segmentationst LREC 2018

The Universal Morphology UniMorph project is a collaborative effort to improve how NLP handles complex morphology across the world's languages.

Deep Generation of Coq Lemma Names Using Elaborated Terms

EngineeringSoftware/roosterize 16 Apr 2020

Our results show that Roosterize substantially outperforms baselines for suggesting lemma names, highlighting the importance of using multi-input models and elaborated terms.

Morphological analysis using a sequence decoder

ai-ku/TrMor2018 TACL 2019

Our Morse implementation and the TrMor2018 dataset are available online to support future research\footnote{See \url{https://github. com/ai-ku/Morse. jl} for a Morse implementation in Julia/Knet \cite{knet2016mlsys} and \url{https://github. com/ai-ku/TrMor2018} for the new Turkish dataset.

Improving Lemmatization of Non-Standard Languages with Joint Learning

emanjavacas/pie NAACL 2019

Lemmatization of standard languages is concerned with (i) abstracting over morphological differences and (ii) resolving token-lemma ambiguities of inflected words in order to map them to a dictionary headword.

Deterministic tensor completion with hypergraph expanders

kharris/max-qnorm-tensor-completion 23 Oct 2019

We provide a novel analysis of low-rank tensor completion based on hypergraph expanders.

Generalizing to unseen domains via distribution matching

belaalb/g2dm 3 Nov 2019

In this work, we tackle such problem by focusing on domain generalization: a formalization where the data generating process at test time may yield samples from never-before-seen domains (distributions).

On the distance between two neural networks and the stability of learning

jxbz/fromage NeurIPS 2020

This paper relates parameter distance to gradient breakdown for a broad class of nonlinear compositional functions.

Post-selection inference with HSIC-Lasso

tobias-freidling/hsic-lasso-psi 29 Oct 2020

Detecting influential features in non-linear and/or high-dimensional data is a challenging and increasingly important task in machine learning.

Adaptive Data-Driven Prediction in a Building Control Hierarchy: A Case Study of Demand Response in Switzerland

yingzhaoleo/risk_src_yingzhao 17 Jul 2023

By providing various services, such as Demand Response (DR), buildings can play a crucial role in the energy market due to their significant energy consumption.