Summary of Comparing Data Augmentation Methods For End-to-end Task-oriented Dialog Systems, by Christos Vlachos et al.
Comparing Data Augmentation Methods for End-to-End Task-Oriented Dialog Systems
by Christos Vlachos, Themos Stafylakis, Ion Androutsopoulos
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the creation of reliable task-oriented dialog systems (ToDSs), which require complex structures and sufficient training data. The researchers evaluate the effectiveness of data augmentation (DA) methods in an end-to-end ToDS setting, using two systems (UBAR and GALAXY) on two datasets (MultiWOZ and KVRET). They consider three types of DA methods (word-level, sentence-level, and dialog-level) and test eight different methods. The results show that all DA methods are beneficial, with some being more effective than others. The paper also introduces a challenging few-shot cross-domain ToDS setting, which achieves similar results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ToDSs are special kinds of computer systems that can have conversations with people. Building these systems is hard because they need lots of training data to learn how to talk and respond correctly. One way to get more training data is by adding fake examples to the real data. This technique is called data augmentation (DA). The researchers in this paper tested different DA methods on two types of ToDSs and two sets of data. They found that all the DA methods helped make the systems better, but some worked better than others. |
Keywords
* Artificial intelligence * Data augmentation * Few shot
