High-risk learning: acquiring new word vectors from tiny data

20 July 2017

Papers citing "High-risk learning: acquiring new word vectors from tiny data"

12 / 12 papers shown

Title
The Impact of Word Splitting on the Semantic Content of Contextualized Word Representations Aina Garí Soler Matthieu Labeau Chloé Clavel VLM 42 2 0 22 Feb 2024
Using dependency parsing for few-shot learning in distributional semantics S. Preda Guy Edward Toh Emerson 27 0 0 12 May 2022
An Empirical Survey of Data Augmentation for Limited Data Learning in NLP Jiaao Chen Derek Tam Colin Raffel Joey Tianyi Zhou Diyi Yang 28 172 0 14 Jun 2021
Dynamic Language Models for Continuously Evolving Content Spurthi Amba Hombaiah Tao Chen Mingyang Zhang Michael Bendersky Marc Najork CLL KELM 40 37 0 11 Jun 2021
Few-Shot Representation Learning for Out-Of-Vocabulary Words Ziniu Hu Ting-Li Chen Kai-Wei Chang Yizhou Sun 21 76 0 01 Jul 2019
Attentive Mimicking: Better Word Embeddings by Attending to Informative Contexts Timo Schick Hinrich Schütze 17 47 0 02 Apr 2019
Relation Extraction Datasets in the Digital Humanities Domain and their Evaluation with Word Embeddings G. Wohlgenannt Ekaterina Chernyak Dmitry Ilvovsky A. Barinova D. Mouromtsev 16 4 0 04 Mar 2019
Learning Semantic Representations for Novel Words: Leveraging Both Form and Context Timo Schick Hinrich Schütze AI4TS NAI 35 33 0 09 Nov 2018
Rearranging the Familiar: Testing Compositional Generalization in Recurrent Networks J. Loula Marco Baroni Brenden M. Lake KELM CoGe 20 129 0 19 Jul 2018
A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors M. Khodak Nikunj Saunshi Yingyu Liang Tengyu Ma Brandon M Stewart Sanjeev Arora 38 98 0 14 May 2018
Recent Trends in Deep Learning Based Natural Language Processing Tom Young Devamanyu Hazarika Soujanya Poria Min Zhang 35 2,824 0 09 Aug 2017
From Frequency to Meaning: Vector Space Models of Semantics Peter D. Turney Patrick Pantel 110 2,981 0 04 Mar 2010