# CAiRE in DialDoc21: Data Augmentation for Information-Seeking Dialogue System

arXiv: https://arxiv.org/abs/2106.03530 · HTML: https://mlsanity.com/paper/2106.03530/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 24 references that are in the map.
- First posted on arXiv 7 Jun 2021.

## Builds on (most cited first, 21 of 21)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [Wizard of Wikipedia: Knowledge-Powered Conversational agents](https://mlsanity.com/paper/1811.01241/) (2018)
- [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018)
- [SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine](https://mlsanity.com/paper/1704.05179/) (2017)
- [MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension](https://mlsanity.com/paper/1910.09753/) (2019)
- [Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue](https://mlsanity.com/paper/2002.07510/) (2020)
- [Learning to Select Knowledge for Response Generation in Dialog Systems](https://mlsanity.com/paper/1902.04911/) (2019)
- [MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models](https://mlsanity.com/paper/2010.00840/) (2020)
- [DoQA -- Accessing Domain-Specific FAQs via Conversational QA](https://mlsanity.com/paper/2005.01328/) (2020)
- [XPersona: Evaluating Multilingual Personalized Chatbot](https://mlsanity.com/paper/2003.07568/) (2020)
- [Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2009.13656/) (2020)
- [Plug-and-Play Conversational Models](https://mlsanity.com/paper/2010.04344/) (2020)
- [Retrieval-Free Knowledge-Grounded Dialogue Response Generation with Adapters](https://mlsanity.com/paper/2105.06232/) (2021)
- [Zero-shot Cross-lingual Dialogue Systems with Transferable Latent Variables](https://mlsanity.com/paper/1911.04081/) (2019)
- [Technical report on Conversational Question Answering](https://mlsanity.com/paper/1909.10772/) (2019)
- [ERICA: An Empathetic Android Companion for Covid-19 Quarantine](https://mlsanity.com/paper/2106.02325/) (2021)
- [Nora: The Well-Being Coach](https://mlsanity.com/paper/2106.00410/) (2021)

## Built on by (most cited first, 5 of 5)

- [Can Question Rewriting Help Conversational Question Answering?](https://mlsanity.com/paper/2204.06239/) (2022)
- [UniGDD: A Unified Generative Framework for Goal-Oriented Document-Grounded Dialogue](https://mlsanity.com/paper/2204.07770/) (2022)
- [Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior Inference](https://mlsanity.com/paper/2306.01153/) (2023)
- [Topic-Aware Response Generation in Task-Oriented Dialogue with Unstructured Knowledge Access](https://mlsanity.com/paper/2212.05373/) (2022)
- [MonoTODia: Translating Monologue Requests to Task-Oriented Dialogues](https://mlsanity.com/paper/2502.17268/) (2025)

## Abstract (arXiv)

Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative responses based on users' needs, which. To tackle this challenge, we utilize data augmentation methods and several training techniques with the pre-trained language models to learn a general pattern of the task and thus achieve promising performance. In DialDoc21 competition, our system achieved 74.95 F1 score and 60.74 Exact Match score in subtask 1, and 37.72 SacreBLEU score in subtask 2. Empirical analysis is provided to explain the effectiveness of our approaches.