Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes Ofer Sabo1 Yanai Elazar1,2 Yoav Goldberg1,2 Ido Dagan1 1Computer Science Department, Bar Ilan University, Israel 2Allen Institute for Artificial Intelligence {ofersabo,yanaiela,yoav.goldberg,ido.k.dagan}@gmail.com Abstract We explore few-shot learning…
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Efficient Computation of Expectations under Spanning Tree Distributions
Efficient Computation of Expectations under Spanning Tree Distributions Ran Zmigrod , University of Cambridge, United Kingdom Tim Vieira , Ryan Cotterell , Université Johns Hopkins, United States ETH Z¨urich, United Kingdom rz279@cam.ac.uk tim.f.vieira@gmail.com ryan.cotterell@inf.ethz.ch Abstract…
Pretraining the Noisy Channel Model for Task-Oriented Dialogue
Pretraining the Noisy Channel Model for Task-Oriented Dialogue Qi Liu2∗, Lei Yu1, Laura Rimell1, and Phil Blunsom1,2 1DeepMind, United Kingdom 2University of Oxford, United Kingdom qi.liu@cs.ox.ac.uk {leiyu,laurarimell,pblunsom}@google.com Abstract Direct decoding for task-oriented dialogue is known…
Self-supervised Regularization for Text Classification
Self-supervised Regularization for Text Classification Meng Zhou∗ Shanghai Jiao Tong University, China Zechen Li∗ Northeastern University, United States Pengtao Xie† UC San Diego, United States p1xie@eng.ucsd.edu zhoumeng9904@sjtu.edu.cn li.zec@northeastern.edu Abstract Text classification is a widely studied…
Evaluating Document Coherence Modeling
Evaluating Document Coherence Modeling Aili Shen♣, Meladel Mistica♣, Bahar Salehi♣, Hang Li♦, Timothy Baldwin♣, Jianzhong Qi♣ ♣ The University of Melbourne, Australia ♦ AI Lab at ByteDance, Chine {aili.shen, misticam, tbaldwin, jianzhong.qi}@unimelb.edu.au baharsalehi@gmail.com, lihang.lh@bytedance.com Abstract…
There Once Was a Really Bad Poet, It Was Automated but
There Once Was a Really Bad Poet, It Was Automated but You Didn’t Know It Jianyou Wang1, Xiaoxuan Zhang1, Yuren Zhou2, Christopher Suh1, Cynthia Rudin1,2 Duke University {1Computer Science, 2Statistics} Department, United States jw542@duke.edu, zhangxiaoxuanaa@gmail.com…
Context-aware Adversarial Training for Name Regularity Bias in
Context-aware Adversarial Training for Name Regularity Bias in Named Entity Recognition Abbas Ghaddar, Philippe Langlais†, Ahmad Rashid, and Mehdi Rezagholizadeh Huawei Noah’s Ark Lab, Montreal Research Center, Canada †RALI/DIRO, Universit´e de Montr´eal, Canada abbas.ghaddar@huawei.com, felipe@iro.umontreal.ca…
Dialogue State Tracking with Incremental Reasoning
Dialogue State Tracking with Incremental Reasoning Lizi Liao, Le Hong Long, Yunshan Ma, Wenqiang Lei, Tat-Seng Chua School of Computing National University of Singapore {liaolizi.llz, yunshan.ma, wenqianglei}@gmail.com lehonglong@u.nus.edu chuats@comp.nus.edu.sg Abstract Tracking dialogue states to better…
Characterizing English Variation across
Characterizing English Variation across Social Media Communities with BERT Li Lucy and David Bamman University of California, Berkeley {lucy3 li, dbamman}@berkeley.edu Abstract Much previous work characterizing language variation across Internet social groups has fo- cused…
Optimizing over subsequences generates context-sensitive languages
Optimizing over subsequences generates context-sensitive languages Andrew Lamont University of Massachusetts Amherst alamont@linguist.umass.edu Abstract Phonological generalizations are finite-state. While Optimality Theory is a popular frame- work for modeling phonology, it is known to generate non-finite-state…