Fetching the paper…
Reading the bibliography…
Despite significant advancements in multi-label text classification, the ability of existing models to generalize to novel and seldom-encountered complex concepts, which are compositions of elementary ones, remains underexplored.
Compositional generalization in a deep seq2seq model by separating syntax and semantics
Russin, J.; Jo, J.; O’Reilly, R. C.; and Bengio, Y. 2019 · 1904
Earlier work this paper cites.
One-shot learning for text-to-sql generation
Lee, D.; Yoon, J.; Song, J.; Lee, S.; and Yoon, S. 2019 · 1905
Earlier work this paper cites.
Compositional generalization in semantic parsing: Pre-training vs. specialized architectures
Furrer, D.; van Zee, M.; Scales, N.; and Schärli, N. 2020 · 2007
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P.; and Welling, M. 2013 · 2013
Earlier work this paper cites.
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Cho, K.; Merrienboer, B.; Gulcehre, C.; Bougares, F.; Schwenk, H.; and Bengio, Y. 2014 · 2014
Earlier work this paper cites.
Aspects of the Theory of Syntax
Chomsky, N. 2014 · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2014 · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Sutskever, I.; Vinyals, O.; and Le, Q. V. 2014 · 2014
Earlier work this paper cites.
Data Recombination for Neural Semantic Parsing
Jia, R.; and Liang, P. 2016 · 2016
Earlier work this paper cites.
Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
Earlier work this paper cites.
Improving Text-to-SQL Evaluation Methodology
Finegan-Dollak, C.; Kummerfeld, J. K.; Zhang, L.; Ramanathan, K.; Sadasivam, S.; Zhang, R.; and Radev, D. 2018 · 2018
Earlier work this paper cites.
Semeval-2018 task 1: Affect in tweets
Mohammad, S.; Bravo-Marquez, F.; Salameh, M.; and Kiritchenko, S. 2018 · 2018
Earlier work this paper cites.
SGM: Sequence Generation Model for Multi-label Classification
Yang, P.; Sun, X.; Li, W.; Ma, S.; Wu, W.; and Wang, H. 2018 · 2018
Earlier work this paper cites.
Learning imbalanced datasets with label-distribution-aware margin loss
Cao, K.; Wei, C.; Gaidon, A.; Arechiga, N.; and Ma, T. 2019 · 2019
Earlier work this paper cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 2019
Cited alongside, same era.
Measuring Compositional Generalization: A Comprehensive Method on Realistic Data
Keysers, D.; Schärli, N.; Scales, N.; Buisman, H.; Furrer, D.; Kashubin, S.; Momchev, N.; Sinopalnikov, D.; Stafiniak, L.; Tihon, T.; et al. 2019 · 2019
Cited alongside, same era.
Movie-Genre-Multi-Label-Text-Classification
Maiya, S. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; Sutskever, I.; et al. 2019 · 2019
Cited alongside, same era.
Label-Specific Document Representation for Multi-Label Text Classification
Xiao, L.; Huang, X.; Chen, B.; and Jing, L. 2019 · 2019
Cited alongside, same era.
Good-Enough Compositional Data Augmentation
Andreas, J. 2020 · 2020
Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution
Huang, Y.; Giledereli, B.; Köksal, A.; Özgür, A.; and Ozkirimli, E. 2021d · 2021
Later among the works it cites.
Prefix-Tuning: Optimizing Continuous Prompts for Generation
Li, X. L.; and Liang, P. 2021 · 2021
Later among the works it cites.
Robustness to spurious correlations in text classification via automatically generated counterfactuals
Wang, Z.; and Culotta, A. 2021 · 2021
Later among the works it cites.
BERT for Sequence-to-Sequence Multi-label Text Classification
Yarullin, R.; and Serdyukov, P. 2021 · 2021
Later among the works it cites.
Prompt-Based Generative Multi-label Emotion Prediction with Label Contrastive Learning
Chai, Y.; Teng, C.; Fei, H.; Wu, S.; Li, J.; Cheng, M.; Ji, D.; and Li, F. 2022 · 2022
Later among the works it cites.
Scaling Instruction-Finetuned Language Models
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Label-Wise Document Pre-training for Multi-label Text Classification
Liu, H.; Yuan, C.; and Wang, X. 2020 · 2020
Cited alongside, same era.
The role of disentanglement in generalisation
Montero, M. L.; Ludwig, C. J.; Costa, R. P.; Malhotra, G.; and Bowers, J. 2020 · 2020
Cited alongside, same era.
MAGNET: Multi-Label Text Classification using Attention-based Graph Neural Network
Pal, A.; Selvakumar, M.; and Sankarasubbu, M. 2020 · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C.; Shazeer, N.; Roberts, A.; Lee, K.; Narang, S.; Matena, M.; Zhou, Y.; Li, W.; and Liu, P. J. 2020 · 2020
Cited alongside, same era.
An Investigation of Why Overparameterization Exacerbates Spurious Correlations
Sagawa, S.; Raghunathan, A.; Koh, P. W.; and Liang, P. 2020 · 2020
Cited alongside, same era.
HSCNN: A Hybrid-Siamese Convolutional Neural Network for Extremely Imbalanced Multi-label Text Classification
Yang, W.; Li, J.; Fukumoto, F.; and Ye, Y. 2020 · 2020
Cited alongside, same era.
Chung, H. W.; Hou, L.; Longpre, S.; Zoph, B.; Tay, Y.; Fedus, W.; Li, Y.; Wang, X.; Dehghani, M.; Brahma, S.; et al. 2022 · 2022
Later among the works it cites.
Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generation
Li, Z.; Qu, L.; Xu, Q.; Wu, T.; Zhan, T.; and Haffari, G. 2022 · 2022
Later among the works it cites.
P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks
Liu, X.; Ji, K.; Fu, Y.; Tam, W.; Du, Z.; Yang, Z.; and Tang, J. 2022 · 2022
Later among the works it cites.
Improving Compositional Generalization with Latent Structure and Data Augmentation
Qiu, L.; Shaw, P.; Pasupat, P.; Nowak, P.; Linzen, T.; Sha, F.; and Toutanova, K. 2022a · 2022
Later among the works it cites.
Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing
Qiu, L.; Shaw, P.; Pasupat, P.; Shi, T.; Herzig, J.; Pitler, E.; Sha, F.; and Toutanova, K. 2022b · 2022
Later among the works it cites.
SUBS: Subtree Substitution for Compositional Semantic Parsing
Yang, J.; Zhang, L.; and Yang, D. 2022 · 2022
Later among the works it cites.
Disentangled Sequence to Sequence Learning for Compositional Generalization
Zheng, H.; and Lapata, M. 2022 · 2022
Later among the works it cites.
Reranking for Natural Language Generation from Logical Forms: A Study based on Large Language Models
Haroutunian, L.; Li, Z.; Galescu, L.; Cohen, P.; Tumuluri, R.; and Haffari, G. 2023 · 2023
Closest in time.
Zeng, W.; Zhao, L.; He, K.; Geng, R.; Wang, J.; Wu, W.; and Xu, W. 2023 · 2023
Closest in time.