# Data Augmentation for Improving Tail-traffic Robustness in Skill-routing for Dialogue Systems

arXiv: https://arxiv.org/abs/2306.04823 · HTML: https://mlsanity.com/paper/2306.04823/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 31 references that are in the map.
- First posted on arXiv 7 Jun 2023.

## Builds on (most cited first, 25 of 25)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders](https://mlsanity.com/paper/1703.10960/) (2017)
- [Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization](https://mlsanity.com/paper/1809.05972/) (2018)
- [Conditional BERT Contextual Augmentation](https://mlsanity.com/paper/1812.06705/) (2018)
- [An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://mlsanity.com/paper/2106.07499/) (2021)
- [Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding](https://mlsanity.com/paper/1807.01554/) (2018)
- [Contrastive Learning with Adversarial Perturbations for Conditional Text Generation](https://mlsanity.com/paper/2012.07280/) (2020)
- [Paraphrase Augmented Task-Oriented Dialog Generation](https://mlsanity.com/paper/2004.07462/) (2020)
- [Improving Neural Response Diversity with Frequency-Aware Cross-Entropy Loss](https://mlsanity.com/paper/1902.09191/) (2019)
- [Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-Encoders](https://mlsanity.com/paper/1911.03882/) (2019)
- [CG-BERT: Conditional Text Generation with BERT for Generalized Few-shot Intent Detection](https://mlsanity.com/paper/2004.01881/) (2020)
- [Data Augmentation for Spoken Language Understanding via Joint Variational Generation](https://mlsanity.com/paper/1809.02305/) (2018)
- [Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents](https://mlsanity.com/paper/1910.03487/) (2019)
- [GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentation](https://mlsanity.com/paper/2109.03079/) (2021)
- [Structured Attention for Unsupervised Dialogue Structure Induction](https://mlsanity.com/paper/2009.08552/) (2020)
- [Improving Robustness of Task Oriented Dialog Systems](https://mlsanity.com/paper/1911.05153/) (2019)
- [OodGAN: Generative Adversarial Network for Out-of-Domain Data Generation](https://mlsanity.com/paper/2104.02484/) (2021)
- [Efficient Contrastive Learning via Novel Data Augmentation and Curriculum Learning](https://mlsanity.com/paper/2109.05941/) (2021)
- [Self-Supervised Contrastive Learning for Efficient User Satisfaction Prediction in Conversational Agents](https://mlsanity.com/paper/2010.11230/) (2020)
- [Neural model robustness for skill routing in large-scale conversational AI systems: A design choice exploration](https://mlsanity.com/paper/2103.03373/) (2021)
- [Scalable and Robust Self-Learning for Skill Routing in Large-Scale Conversational AI Systems](https://mlsanity.com/paper/2204.07135/) (2022)
- [Constrained Policy Optimization for Controlled Self-Learning in Conversational AI Systems](https://mlsanity.com/paper/2209.08429/) (2022)
- [Generative Adversarial Networks for Annotated Data Augmentation in Data Sparse NLU](https://mlsanity.com/paper/2012.05302/) (2020)
- [Handling Long-Tail Queries with Slice-Aware Conversational Systems](https://mlsanity.com/paper/2104.13216/) (2021)

## Abstract (arXiv)

Large-scale conversational systems typically rely on a skill-routing component to route a user request to an appropriate skill and interpretation to serve the request. In such system, the agent is responsible for serving thousands of skills and interpretations which create a long-tail distribution due to the natural frequency of requests. For example, the samples related to play music might be a thousand times more frequent than those asking for theatre show times. Moreover, inputs used for ML-based skill routing are often a heterogeneous mix of strings, embedding vectors, categorical and scalar features which makes employing augmentation-based long-tail learning approaches challenging. To improve the skill-routing robustness, we propose an augmentation of heterogeneous skill-routing data and training targeted for robust operation in long-tail data regimes. We explore a variety of conditional encoder-decoder generative frameworks to perturb original data fields and create synthetic training data. To demonstrate the effectiveness of the proposed method, we conduct extensive experiments using real-world data from a commercial conversational system. Based on the experiment results, the proposed approach improves more than 80% (51 out of 63) of intents with less than 10K of traffic instances in the skill-routing replication task.