Papers with ESIM
Compare, Compress and Propagate: Enhancing Neural Architectures with Alignment Factorization for Natural Language Inference (D18-1)
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| Challenge: | Using a new architecture, alignment pairs are compared, compressed and then propagated to upper layers for enhanced representation learning. |
| Approach: | They propose a new architecture where alignment pairs are compared, compressed and then propagated to upper layers for enhanced representation learning. |
| Outcome: | The proposed architecture achieves competitive performance on three popular benchmarks, SNLI, MultiNLI and SciTail, while maintaining lightweight parameter size. |
pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference (N19-1)
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| Challenge: | Existing inference models that rely heavily on unsupervised single-word embeddings struggle to learn implied relationships between pairs of words. |
| Approach: | They propose to use word embeddings to learn and use background knowledge about implied relationships between words that are crucial for cross-sentence inference problems. |
| Outcome: | The proposed models gain 2.7% on the recently released SQuAD 2.0 and 1.3% on MultiNLI, and 8.8% on the adversarial SQu AD datasets. |
HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference (2020.lrec-1)
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| Challenge: | Recent studies have shown that for models trained on datasets for natural language inference (NLI), it is possible to make correct predictions by looking at the hypothesis while completely ignoring the premise. |
| Approach: | They propose to derive adversarial examples in terms of the hypothesis-only bias and explore eligible ways to mitigate such bias. |
| Outcome: | The proposed models can be used to mitigate the hypothesis-only bias by using down-sampling and adversarial training. |