Name
Abstract | ||
|---|---|---|
When training most modern reading comprehension models, all the questions associated with a context are treated as being independent from each other. However, closely related questions and their corresponding answers are not independent, and leveraging these relationships could provide a strong supervision signal to a model. Drawing on ideas from contrastive estimation, we introduce several new supervision techniques that compare question-answer scores across multiple related instances. Specifically, we normalize these scores across various neighborhoods of closely contrasting questions and/or answers, adding another cross entropy loss term that is used in addition to traditional maximum likelihood estimation. Our techniques require bundles of related question-answer pairs, which we can either mine from within existing data or create using various automated heuristics. We empirically demonstrate the effectiveness of training with instance bundles on two datasets -- HotpotQA and ROPES -- showing up to 11% absolute gains in accuracy. |
Year | Venue | DocType |
|---|---|---|
2021 | EMNLP | Conference |
Volume | Citations | PageRank |
2021.emnlp-main | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dheeru Dua | 1 | 38 | 4.95 |
| Pradeep Dasigi | 2 | 131 | 12.09 |
| Sameer Singh | 3 | 1060 | 71.63 |
| Matthew Gardner | 4 | 704 | 38.49 |