Grounded Response Generation Task at DSTC7

By Michel Galley, Chris Brockett, Xiang Gao, Jianfeng Gao, Bill Dolan. 2019

Subfields: Natural Language Processing, Dialogue & Conversational AI

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. Jianfeng Gao
2. Furu Wei
3. Mohit Bansal
4. Yejin Choi
5. Weizhu Chen
6. Li Dong
7. Xiaodong Liu
8. Graham Neubig

In Dialogue & Conversational AI

1. Jianfeng Gao
2. Michel Galley
3. Can Xu
4. Zhou Yu
5. Hao Cheng
6. Daxin Jiang

Citations in the corpus

• Unified Language Model Pre-training for Natural Language Understanding and Generation (2019, methodology): “As shown in Table 10, UNILM outperforms the best system [40] in the DSTC7 shared task [14] across all evaluation metrics.”
• DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation (2019, background): “The DSTC (Dialog System Technology Challenges) 7 track (Galley et al., 2019) is an end-to-end conversational modeling task, 4 in which the goal is to generate conversation responses that go beyond chitchat by injecting information that is grounded in external knowledge.”
• Challenges in Building Intelligent Open-domain Dialog Systems (2019, neutral): “Grounded Response Generation at DSTC7 [104]: The dataset, which is first released for the “sentence generation” task at the 7th Dialog System Technology Challenges (DSTC7) [31], is developed for grounded conversation modeling.”
• Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading (2019, methodology): “We made a preliminary version of this dataset available for a shared task (Galley et al., 2019) at Dialog System Technology Challenges (DSTC) (Yoshino et al., 2019).”
• Knowledge-Grounded Dialogue Generation with Term-level De-noising (2021, background): “…role the ‘post’ and the second role the ‘response’ and we aim to generate the response for the given post. noise, i.e., for inclusion of non-relevant words, and previous studies (Galley et al. (2019), Zheng et al. (2020)) have shown that adding noise can decrease the response generation quality.”
• GODEL: Large-Scale Pre-Training for Goal-Directed Dialog (2022, background): “…support knowledge grounded response generation, conversational question-answering, and task-oriented dialog, as follows: • The DSTC7 Task 2 corpus (Galley et al., 2019) is designed for end-to-end conversational modeling that goes beyond chitchat by injecting informational responses grounded in…”
• Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback (2023, methodology): “We followed the data crawling process used in DSTC7 Task 2 (Galley et al., 2019).”
• Jointly Optimizing Diversity and Relevance in Neural Response Generation (2019, background): “, 2016b), textual knowledge (Ghazvininejad et al., 2017; Galley et al., 2019), dialog act (Zhao et al.”
• ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation (2021, methodology): “For ProphetNet-Dialog-En, we utilize Reddit comments dataset (Zhou et al., 2018; Galley et al., 2019).”
• PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable (2019, background): “Large-scale conversation datasets – Twitter (Cho et al., 2014) and Reddit (Zhou et al., 2018; Galley et al., 2019) The hyper-parameters used in pre-training are listed as follows.”
• PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning (2020, methodology): “• PLATO (Bao et al., 2020) is trained on the basis of BERT BASE using 8.3M Twitter and Reddit conversations (Cho et al., 2014; Zhou et al., 2018; Galley et al., 2019).”