Name
Title | ||
|---|---|---|
Understanding Roles and Entities: Datasets and Models for Natural Language Inference. |
Abstract | ||
|---|---|---|
We present two new datasets and a novel attention mechanism for Natural Language Inference (NLI). Existing neural NLI models, even though when trained on existing large datasets, do not capture the notion of entity and role well and often end up making mistakes such as Peter signed a deal can be inferred from John signed a deal. The two datasets have been developed to mitigate such issues and make the systems better at understanding the notion of entities and roles. After training the existing architectures on the new dataset we observe that the existing architectures does not perform well on one of the new benchmark. We then propose a modification to the word-to-word attention function which has been uniformly reused across several popular NLI architectures. The resulting architectures perform as well as their unmodified counterparts on the existing benchmarks and perform significantly well on the new benchmark for roles and entities. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Computation and Language | Journal |
Volume | Citations | PageRank |
abs/1904.09720 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arindam Mitra | 1 | 62 | 12.69 |
| Ishan Shrivastava | 2 | 0 | 0.34 |
| Chitta Baral | 3 | 2353 | 269.58 |