S2SPMN: A Simple and Effective Framework for Response Generation with Relevant Information (D18-1)
Copied to clipboard
| Challenge: | Existing work on how to generate relevant and informative responses is focusing on how dialogue systems generate information from large dialogue corpus. |
| Approach: | They propose to use dialogue corpus to generate relevant responses by using prototypes to extract semantic information from PMN. |
| Outcome: | The proposed model outperforms classical and strong baseline models in generating relevant and informative responses. |
Similar Papers
Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework (D19-1)
Copied to clipboard
| Challenge: | generative models for end-to-end sequence generation have been shown promising for this task . however, how to precisely extract a skeleton and how to effectively train a retrieval-guided response generator is still challenging. |
| Approach: | They propose a framework where skeleton extraction is made by an interpretable matching model and a retrieval-guided response generator is followed by a separate generator. |
| Outcome: | The proposed framework outperforms baseline models in a variety of experiments. |
Knowledge-Grounded Dialogue Generation with Pre-trained Language Models (2020.emnlp-main)
Copied to clipboard
| Challenge: | Empirical results indicate that pre-trained language models can significantly outperform state-of-the-art methods in both automatic evaluation and human judgment. |
| Approach: | They propose to equip a pre-trained language model with a knowledge selection module to generate knowledge-grounded dialogues. |
| Outcome: | The proposed model outperforms state-of-the-art methods in evaluation and human judgment. |
Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory (N19-1)
Copied to clipboard
| Challenge: | Existing generative dialogue models generate responses from input queries . however, the results are limited and the models are unsatisfactory . |
| Approach: | They propose a framework which exploits retrieval results via a skeleton-to-response paradigm . they extract a query skelet and use it to generate a new skele and response . |
| Outcome: | The proposed approach significantly improves the informativeness of the generated responses. |
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. |
Cross Copy Network for Dialogue Generation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Despite the success of sequence-to-sequence models, dialogue logics are often ignored. |
| Approach: | They propose a network architecture to explore the current dialog context and similar dialogue instances’ logical structure simultaneously. |
| Outcome: | The proposed network architecture is superior to existing state-of-the-art models. |
Task-Oriented Conversation Generation Using Heterogeneous Memory Networks (D19-1)
Copied to clipboard
| Challenge: | Existing memory networks do not perform well when leveraging heterogeneous information from different sources. |
| Approach: | They propose to use user utterances, dialogue history and background knowledge tuples to integrate external knowledge into a neural dialogue model. |
| Outcome: | The proposed model outperforms the state-of-the-art data-driven task-oriented dialogue models on real-world datasets. |
An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation (D18-1)
Copied to clipboard
| Challenge: | Experimental results show that our model can generate semantically coherent responses compared to baseline models. |
| Approach: | They propose an Auto-Encoder Matching model to learn utterance-level semantic dependency . their model contains two auto-encoders and one mapping module . |
| Outcome: | Experimental results show that the proposed model can generate high coherence and fluency compared to baseline models. |
Personalized Response Generation via Generative Split Memory Network (2021.naacl-main)
Copied to clipboard
| Challenge: | Despite the success of text generation and dialogue systems, how to endow a text generation system with personality traits remains under-investigated. |
| Approach: | They propose a model to generate personalized responses on reddit using user profiles and posting histories. |
| Outcome: | The proposed model improves over the state-of-the-art response generation models. |
Approximation of Response Knowledge Retrieval in Knowledge-grounded Dialogue Generation (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies have focused on improving dialogue generation models that include knowledge related to the posts. |
| Approach: | They propose to use a novel method to generate responses from posts and related knowledge by injecting knowledge into dialogue generation models. |
| Outcome: | The proposed method outperforms baseline models in terms of knowledge relevance and quality. |
Pretrained Language Models for Dialogue Generation with Multiple Input Sources (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Large-scale pretrained language models have achieved outstanding performance on natural language understanding tasks. |
| Approach: | They propose to fuse attention information from multiple input sources to achieve better relevance with dialogue history than simple fusion baselines. |
| Outcome: | The proposed models deliver higher relevance with dialogue history than baselines. |