# C2C-GenDA: Cluster-to-Cluster Generation for Data Augmentation of Slot Filling

Source: [C2C-GenDA: Cluster-to-Cluster Generation for Data Augmentation of Slot Filling](<https://www.sota2.com/research/paper/68fa68f41e209131cea1f47a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:49:52.437Z

Authors: Yutai Hou, Sanyuan Chen, Wanxiang Che, Cheng Chen, Ting Liu

Published: 2020-12-13T08:35:37.000Z

## Abstract

Slot filling, a fundamental module of spoken language understanding, often suffers from insufficient quantity and diversity of training data. To remedy this, we propose a novel Cluster-to-Cluster generation framework for Data Augmentation (DA), named C2C-GenDA. It enlarges the training set by reconstructing existing utterances into alternative expressions while keeping semantic. Different from previous DA works that reconstruct utterances one by one independently, C2C-GenDA jointly encodes multiple existing utterances of the same semantics and simultaneously decodes multiple unseen expressions. Jointly generating multiple new utterances allows to consider the relations between generated instances and encourages diversity. Besides, encoding multiple existing utterances endows C2C with a wider view of existing expressions, helping to reduce generation that duplicates existing data. Experiments on ATIS and Snips datasets show that instances augmented by C2C-GenDA improve slot filling by 7.99 (11.9%) and 5.76 (13.6%) F-scores respectively, when there are only hundreds of training utterances.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Joint Entity and Relation Extraction on WebNLG](<https://www.sota2.com/research/sota/joint-entity-and-relation-extraction-on-webnlg.md>): Joint Entity and Relation Extraction; WebNLG; Precision: 91.84.

- [Joint Entity and Relation Extraction on NYT](<https://www.sota2.com/research/sota/joint-entity-and-relation-extraction-on-nyt.md>): Joint Entity and Relation Extraction; NYT; Precision: 88.6.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2012.07004>)

[PDF](<https://arxiv.org/pdf/2012.07004.pdf>)