Papers with response generation model
EM Pre-training for Multi-party Dialogue Response Generation (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to pretrain large language models for dialogue response generation are difficult due to the lack of annotated addressee labels in multi-party dialogue datasets. |
| Approach: | They propose an Expectation-Maximization approach that iteratively performs expectation steps to generate addressee labels and maximize a response generation model. |
| Outcome: | The proposed method is based on two-party dialogues and multi-party dialogs. |
Paraphrase Augmented Task-Oriented Dialog Generation (2020.acl-main)
Copied to clipboard
| Challenge: | Neural generative models can perform dialog generation tasks with a large data set, but lack of high-quality data and expensive data annotation process limit their application in real world settings. |
| Approach: | They propose to combine paraphrase and response generation models to improve dialog generation performance by annotating dialog states and dialog act labels. |
| Outcome: | The proposed framework outperforms existing methods significantly in dialog generation tasks, especially under low resource settings. |
Controllable Factuality in Document-Grounded Dialog Systems Using a Noisy Channel Model (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Recent document-grounded dialog systems have seen an increase in popularity. |
| Approach: | They propose a model for document-grounded response generation in dialog that is decomposed into two components according to Bayes’ theorem and propose different approximate decoding schemes. |
| Outcome: | The proposed model is more factual in terms of automatic factuality metrics than the baseline model and can be combined with a recently proposed method to control factuity in grounded dialog, CTRL. |
Variational Hierarchical User-based Conversation Model (D19-1)
Copied to clipboard
| Challenge: | Recent approaches to conversation response generation model speakers and utterances together but are too tailored to the speakers. |
| Approach: | They propose a new conversation model with a stochastic variable conditioned on the speakers and affects the context. |
| Outcome: | The proposed model outperforms existing models in generating appropriate conversation responses. |
External Knowledge Acquisition for End-to-End Document-Oriented Dialog Systems (2023.eacl-main)
Copied to clipboard
| Challenge: | End-to-end neural models for conversational AI often assume that a response can be generated by considering only the knowledge acquired during training. |
| Approach: | They propose an architecture for document-oriented conversations with access to external knowledge sources. |
| Outcome: | The proposed architecture outperforms baseline models on the Wizard of Wikipedia dataset by 10.3% and 7.4%. |
HeterMPC: A Heterogeneous Graph Neural Network for Response Generation in Multi-Party Conversations (2022.acl-long)
Copied to clipboard
| Challenge: | Experimental results show that HeterMPC outperforms various baseline models for response generation in multi-party conversations. |
| Approach: | They propose a heterogeneous graph-based neural network for response generation in multi-party conversations which models the semantics of utterances and interlocutors simultaneously with two types of nodes in a graph. |
| Outcome: | The proposed model outperforms baseline models on the Ubuntu Internet Relay Chat (IRC) channel. |
Persona-Consistent Dialogue Generation via Pseudo Preference Tuning (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods for improving persona consistency in dialogues require external resources. |
| Approach: | They propose a method for enhancing persona consistency in dialogue response generation using direct preference optimization using persona data. |
| Outcome: | The proposed method produces more consistent and natural responses than previous methods. |
Improved Instruction Ordering in Recipe-Grounded Conversation (2023.acl-long)
Copied to clipboard
| Challenge: | In this paper, we explore the task of instructional dialogue and focus on the cooking domain. |
| Approach: | They propose to explore two auxiliary subtasks to support response generation with improved instruction grounding by incorporating user intent and instruction state information into the model. |
| Outcome: | The proposed model lacks understanding of user intent and inability to track instruction state (i.e., which step was last instructed) incorporating user intent information helps the response generation model mitigate the incorrect order issue. |
Triplet-Free Knowledge-Guided Response Generation (2023.findings-acl)
Copied to clipboard
| Challenge: | Prior work focused on constructing ”latent” knowledge and learning how to ground it based on pseudo triplets. |
| Approach: | They propose to pretrain a response language model to measure relevance and consistency between any context and response and use search engines to collect top-ranked passages to serve as guiding knowledge without explicitly optimizing the ‘‘best’ latent knowledge. |
| Outcome: | The proposed model pretrains a response language model to measure relevance and consistency between any context and response, then uses search engines to collect the top-ranked passages to serve as the guiding knowledge without explicitly optimizing the ‘‘best’ latent knowledge. |