Incomplete Utterance Rewriting as Semantic Segmentation
Qian Liu, Bei Chen, Jian-Guang Lou, Bin Zhou, Dongmei Zhang
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ReproduceCode
- github.com/microsoft/ContextualSPOfficialIn paperpytorch★ 386
Abstract
Recent years the task of incomplete utterance rewriting has raised a large attention. Previous works usually shape it as a machine translation task and employ sequence to sequence based architecture with copy mechanism. In this paper, we present a novel and extensive approach, which formulates it as a semantic segmentation task. Instead of generating from scratch, such a formulation introduces edit operations and shapes the problem as prediction of a word-level edit matrix. Benefiting from being able to capture both local and global information, our approach achieves state-of-the-art performance on several public datasets. Furthermore, our approach is four times faster than the standard approach in inference.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| Multi-Rewrite | RUN+BERT | Rewriting F3 | 47.7 | — | Unverified |
| Rewrite | RUN+BERT | ROUGE-L | 93.5 | — | Unverified |