Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling

By Alex Wang, Jan Hula, Patrick Xia, Raghavendra Reddy Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu and 6 more. 2018

113 citations recorded by the source corpus.

Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling. We conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks both as alternatives and complements to language modeling. Our primary results support the use language modeling, especially when combined with pretraining on additional labeled-data tasks. However, our results are mixed across pretraining tasks and show some concerning trends: In ELMo’s pretrain-then-freeze paradigm, random baselines are worryingly strong and results vary strikingly across target tasks. In addition, fine-tuning BERT on an intermediate task often negatively impacts downstream transfer. In a more positive trend, we see modest gains from multitask training, suggesting the development of more sophisticated multitask and transfer learning techniques as an avenue for further research.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (2019, methodology): “This has led to the development of a wide variety of pre-training objectives [Dai and Le, 2015; Ramachandran et al., 2016; Radford et al., 2018; Devlin et al., 2018; Yang et al., 2019; Liu et al., 2019b; Wang et al., 2019a; Song et al., 2019; Dong et al., 2019; Joshi et al., 2019].”
The Pushshift Reddit Dataset (2020, methodology): “Social media data collected by Pushshift has been used already by researchers in computational linguistics and natural language processing (Fulda 2019; Gamallo et al. 2019; Hidey and McKeown 2019; Jiang et al. 2018; Wang et al. 2019; Zheng and Zhou 2019; Zhuang et al. 2018), recommender systems (Buh”
Exploring and Predicting Transferability across NLP Tasks (2020, methodology): “…includes over 3,000 combinations of tasks and data regimes within and across three broad classes of problems (text classification, question answering, and sequence labeling), which is considerably more comprehensive than prior work (Wang et al., 2019a; Talmor and Berant, 2019a; Liu et al., 2019a).”
jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models (2020, background): “…including probing analyses (Ten-ney et al., 2019b,a; Warstadt et al., 2019; Lin et al., 2019; Hewitt and Manning, 2019; Jawahar et al., 2019), transfer learning experiments (Wang et al., 2019a; Phang et al., 2018), and dataset and benchmark construction (Wang et al., 2019b, 2018; Kim et al., 2019).”
Train No Evil: Selective Masking for Task-guided Pre-training (2020, background): “But, Wang et al. (2019) shows that the intermediate pre-training often negatively impacts the transfers to downstream tasks.”
Is Supervised Syntactic Parsing Beneficial for Language Understanding Tasks? An Empirical Investigation (2020, unknown): “Subsequent work (Wang et al., 2019; Pruksachatkun et al., 2020) 3 Methodology Biafﬁne Parser.”
Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data (2020, methodology): “In Figure 7 we continue to test if CA-MTL mitigates task interference by measuring GLUE average scores when progressively adding 9 GLUE tasks, 8 Super-GLUE tasks (Wang et al., 2019b), 6 MRQA tasks (Fisch et al., 2019).”