Papers by Ji-Ping Wang
Extracting Commonsense Properties from Embeddings with Limited Human Guidance (P18-2)
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| Challenge: | Existing methods for learning common sense from text require dozens of hand-annotated frames to connect the property to how it is indirectly reflected in text. |
| Approach: | They propose a method for extracting object-property comparisons from pre-trained embeddings. |
| Outcome: | The proposed approach exceeds previous work but requires less hand-annotated knowledge. |
Generative Data Augmentation for Commonsense Reasoning (2020.findings-emnlp)
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Yiben Yang, Chaitanya Malaviya, Jared Fernandez, Swabha Swayamdipta, Ronan Le Bras, Ji-Ping Wang, Chandra Bhagavatula, Yejin Choi, Doug Downey
| Challenge: | Recent advances in commonsense reasoning depend on large-scale human-authored training data. |
| Approach: | They propose a generative data augmentation technique that augments human-authored training data by using pretrained language models. |
| Outcome: | The proposed technique outperforms existing methods on commonsense reasoning benchmarks and enhances out-of-distribution generalization. |
Using Large Corpus N-gram Statistics to Improve Recurrent Neural Language Models (N19-1)
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| Challenge: | a technique that uses large corpus n-gram statistics as a regularizer for training a neural network LM on a smaller corpus is effective, and more time-efficient than training on ngrams. |
| Approach: | They propose a technique that uses large corpus n-gram statistics as a regularizer for training on a smaller corpus. |
| Outcome: | The proposed technique is effective and more time-efficient than training on a larger corpus. |