Towards a Unified Natural Language Inference Framework to Evaluate Sentence Representations
By Adam Poliak, Aparajita Haldar, Rachel Rudinger, J. Edward Hu, Ellie Pavlick, Aaron Steven White, Benjamin Van Durme. 2018
12 citations recorded by the source corpus.
Subfields: Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Natural Language Processing
- Mohit Bansal
- Samuel R. Bowman
- Caiming Xiong
- Richard Socher
- Yonatan Belinkov
- Luke S. Zettlemoyer
- Percy Liang
- Graham Neubig
Citations in the corpus
- The Natural Language Decathlon: Multitask Learning as Question Answering (2018, background): “By unifying tasks under a single perspective, it is possible to explore these relationships [Wang et al., 2018, Poliak et al., 2018a,b].”
- Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling (2018, methodology): “…matching with MSCOCO (Lin et al., 2014), following Kiela et al. (2018); the small-to-medium-data textunderstanding tasks collected in NLI format by Poliak et al. (2018); ordinal common sense inference (Zhang et al., 2017); POS tagging on the Penn Treebank (Marcus et al., 1993); supertagging on…”
- Looking for ELMo's friends: Sentence-Level Pretraining Beyond Language Modeling (2018, methodology): “…matching with MSCOCO (Lin et al., 2014), following Kiela et al. (2018); the small-to-medium-data textunderstanding tasks collected in NLI format by Poliak et al. (2018); ordinal common sense inference (Zhang et al., 2017); POS tagging on the Penn Treebank (Marcus et al., 1993); and supertagging…”
- Transforming Question Answering Datasets Into Natural Language Inference Datasets (2018, positive): “Although White et al. (2017) and Poliak et al. (2018) have explored recasting datasets created for various semantic classification tasks (e.g. semantic role labeling and named entity recognition) into NLI datasets, we are the first to perform such an automated conversion on QA datasets.”
- Analyzing Compositionality-Sensitivity of NLI Models (2018, background): “Gururangan et al. (2018), Poliak et al. (2018b) and Tsuchiya (2018) concurrently showed hypothesis bias in NLI and RTE datasets.”