Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances (2021.acl-long)
Copied to clipboard
| Challenge: | Recent intelligent open-domain chatbots have made substantial progress thanks to the rapid development of large-scale pre-training approaches. |
| Approach: | They propose a dynamic flow mechanism to model the context flow and a model to capture the information dynamics across dialogue utterances. |
| Outcome: | The proposed model outperforms the DialoGPT on the dialogue generation task. |
Similar Papers
Towards Efficient Dialogue Pre-training with Transferable and Interpretable Latent Structure (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing models that use millions of parameters on massive data are inefficient and lack interpretability. |
| Approach: | They propose a model with a latent structure that is easily transferable from the general domain to downstream tasks in a lightweight and transparent way. |
| Outcome: | The proposed model performs better than four strong baseline models in terms of automatic and human evaluations and is 5x faster than the strongest baseline model. |
FlowDelta: Modeling Flow Information Gain in Reasoning for Conversational Machine Comprehension (D19-58)
Copied to clipboard
| Challenge: | Existing machine comprehension models focus on a single-turn setting and do not account for previous reasoning processes. |
| Approach: | They propose to explicitly model the information gain through the dialogue reasoning . they propose to apply the proposed mechanism to other machine comprehension models . |
| Outcome: | The proposed model achieves state-of-the-art performance in a conversational QA dataset QuAC and a sequential instruction understanding dataset SCONE. |
PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable (2020.acl-main)
Copied to clipboard
| Challenge: | Existing pre-training models for dialogue generation have been proven effective for a wide range of tasks. |
| Approach: | They propose a dialogue generation pre-training framework that leverages bi-directional context and uni-directional characteristic of language generation. |
| Outcome: | The proposed framework is superior to existing models on three publicly available datasets. |
Multi-Modal Open-Domain Dialogue (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent work in open-domain conversational agents has demonstrated that significant improvements in humanness and user preference can be achieved via massive scaling in both pre-training data and model size. |
| Approach: | They combine open-domain dialogue agents with vision models to investigate human preferences and humanness. |
| Outcome: | The proposed model outperforms existing models in multi-modal dialogue while performing as well as its predecessor (text-only) BlenderBot. |
Towards Exploiting Background Knowledge for Building Conversation Systems (D18-1)
Copied to clipboard
| Challenge: | Existing dialog datasets contain a sequence of utterances without any explicit background knowledge associated with them. |
| Approach: | They propose to use movie chats to generate responses by copying unstructured background knowledge . they use a dataset of 9K conversations to test whether responses are generated by copy-and-modify models . |
| Outcome: | The proposed model mimics human process of conversing by copying and/or modifying sentences from unstructured background knowledge. |
TOD-Flow: Modeling the Structure of Task-Oriented Dialogues (2023.emnlp-main)
Copied to clipboard
Sungryull Sohn, Yiwei Lyu, Anthony Liu, Lajanugen Logeswaran, Dong-Ki Kim, Dongsub Shim, Honglak Lee
| Challenge: | Recent advances in task-oriented dialogue systems have limitations regarding transparency and controllability. |
| Approach: | They propose to infer the TOD-flow graph from dialog data annotated with dialog acts and integrate it with any dialogue model to improve its prediction performance, transparency, and controllability. |
| Outcome: | The proposed approach improves dialog act classification and response generation performance in the MultiWOZ and SGD benchmarks. |
Context-Agent: Dynamic Discourse Trees for Non-Linear Dialogue (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to managing non-linear dialogue flow are misaligned with the intrinsically hierarchical and branching structure of natural discourse. |
| Approach: | They propose a framework that models multi-turn dialogue history as a dynamic tree structure. |
| Outcome: | The proposed framework enhances task completion rates and improves token efficiency across various LLMs. |
Conversation Chronicles: Towards Diverse Temporal and Relational Dynamics in Multi-Session Conversations (2023.emnlp-main)
Copied to clipboard
| Challenge: | open-domain chatbots focus on short single-session dialogue, neglecting the potential need for understanding contextual information in multiple consecutive sessions. |
| Approach: | They propose a 1M multi-session dialogue dataset for integrating time intervals and speaker relationships into a long-term conversation setup. |
| Outcome: | The proposed model can generate coherent responses according to time intervals and speaker relationships with high user engagement without contradiction in a long-term conversation setup. |
Soda-Eval: Open-Domain Dialogue Evaluation in the age of LLMs (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Current evaluation practices of open domain dialogue systems are still highly dependent on human evaluation. |
| Approach: | They propose to use an annotated dataset to evaluate chatbots using large language models. |
| Outcome: | The proposed model improves over few-shot inferences on a GPT-3.5 generated dialogue dataset. |
DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation (2020.acl-demos)
Copied to clipboard
Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan
| Challenge: | DIALOGPT is a large, tunable neural conversational response generation model . trained on 147M conversation-like exchanges extracted from Reddit comment chains . |
| Approach: | They present a large, tunable neural conversational response generation model, DIALOGPT . the model is trained on 147M conversation-like exchanges extracted from Reddit comment chains . |
| Outcome: | The proposed model can generate more relevant, contentful and context-consistent responses than baseline systems. |