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Found 17 papers, 14 papers with code
Conversation- and Tree-Structure Losses for Dialogue Disentanglement
When multiple conversations occur simultaneously, a listener must decide which conversation each utterance is part of in order to interpret and respond to it appropriately.
HeterMPC: A Heterogeneous Graph Neural Network for Response Generation in Multi-Party Conversations
To address these challenges, we present HeterMPC, a heterogeneous graph-based neural network for response generation in MPCs which models the semantics of utterances and interlocutors simultaneously with two types of nodes in a graph.
TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge
To address the problem, we propose augmenting TExt Generation via Task-specific and Open-world Knowledge (TegTok) in a unified framework.
Detecting Speaker Personas from Conversational Texts
This is a many-to-many semantic matching task because both contexts and personas in SPD are composed of multiple sentences.
MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding
Recently, various neural models for multi-party conversation (MPC) have achieved impressive improvements on a variety of tasks such as addressee recognition, speaker identification and response prediction.
Partner Matters! An Empirical Study on Fusing Personas for Personalized Response Selection in Retrieval-Based Chatbots
Empirical studies on the Persona-Chat dataset show that the partner personas neglected in previous studies can improve the accuracy of response selection in the IMN- and BERT-based models.
Learning to Retrieve Entity-Aware Knowledge and Generate Responses with Copy Mechanism for Task-Oriented Dialogue Systems
Task-oriented conversational modeling with unstructured knowledge access, as track 1 of the 9th Dialogue System Technology Challenges (DSTC 9), requests to build a system to generate response given dialogue history and knowledge access.
Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots
The challenges of building knowledge-grounded retrieval-based chatbots lie in how to ground a conversation on its background knowledge and how to match response candidates with both context and knowledge simultaneously.
DialBERT: A Hierarchical Pre-Trained Model for Conversation Disentanglement
Disentanglement is a problem in which multiple conversations occur in the same channel simultaneously, and the listener should decide which utterance is part of the conversation he will respond to.
Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots
In this paper, we study the problem of employing pre-trained language models for multi-turn response selection in retrieval-based chatbots.
Pre-Trained and Attention-Based Neural Networks for Building Noetic Task-Oriented Dialogue Systems
The NOESIS II challenge, as the Track 2 of the 8th Dialogue System Technology Challenges (DSTC 8), is the extension of DSTC 7.
Ranked #1 on
Conversation Disentanglement
on irc-disentanglement
Fine-Tuning BERT for Schema-Guided Zero-Shot Dialogue State Tracking
We present our work on Track 4 in the Dialogue System Technology Challenges 8 (DSTC8).
Utterance-to-Utterance Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots
The distances between context and response utterances are employed as a prior component when calculating the attention weights.
Ranked #8 on
Conversational Response Selection
on E-commerce
Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots
Compared with previous persona fusion approaches which enhance the representation of a context by calculating its similarity with a given persona, the DIM model adopts a dual matching architecture, which performs interactive matching between responses and contexts and between responses and personas respectively for ranking response candidates.
Promoting Diversity for End-to-End Conversation Response Generation
At this stage, two different models are proposed, i. e., a variational generative (VariGen) model and a retrieval based (Retrieval) model.
Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots
In this paper, we propose an interactive matching network (IMN) for the multi-turn response selection task.
Ranked #7 on
Conversational Response Selection
on E-commerce
Building Sequential Inference Models for End-to-End Response Selection
This paper presents an end-to-end response selection model for Track 1 of the 7th Dialogue System Technology Challenges (DSTC7).
Ranked #5 on
Conversational Response Selection
on DSTC7 Ubuntu
