Name
Abstract | ||
|---|---|---|
Multi-choice reading comprehension is a challenging task that requires complex reasoning procedure. Given passage and question, a correct answer need to be selected from a set of candidate answers. In this paper, we propose textbf{D}ual textbf{C}o-textbf{M}atching textbf{N}etwork (textbf{DCMN}) which model the relationship among passage, question and answer bidirectionally. Different from existing approaches which only calculate question-aware or option-aware passage representation, we calculate passage-aware question representation and passage-aware answer representation at the same time. To demonstrate the effectiveness of our model, we evaluate our model on a large-scale multiple choice machine reading comprehension dataset({em i.e.} RACE). Experimental result show that our proposed model achieves new state-of-the-art results. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Computation and Language | Journal |
Volume | Citations | PageRank |
abs/1901.09381 | 2 | 0.36 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuailiang Zhang | 1 | 2 | 0.36 |
| Hai Zhao | 2 | 960 | 113.64 |
| Yuwei Wu | 3 | 7 | 1.79 |
| Zhuosheng Zhang | 4 | 57 | 14.93 |
| Xin Zhou | 5 | 3 | 6.45 |
| Xiang Zhou | 6 | 3 | 1.40 |