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Found 18 papers, 8 papers with code
Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting
(2) How to cohere with context and preserve the knowledge when generating a stylized response.
FORCE: A Framework of Rule-Based Conversational Recommender System
The conversational recommender systems (CRSs) have received extensive attention in recent years.
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.
PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks
This paper focuses on the Data Augmentation for low-resource Natural Language Understanding (NLU) tasks.
Small Changes Make Big Differences: Improving Multi-turn Response Selection in Dialogue Systems via Fine-Grained Contrastive Learning
The sequence representation plays a key role in the learning of matching degree between the dialogue context and the response.
Multimodal Dialogue Response Generation
In such a low-resource setting, we devise a novel conversational agent, Divter, in order to isolate parameters that depend on multimodal dialogues from the entire generation model.
RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models
Furthermore, we propose to evaluate the CRS models in an end-to-end manner, which can reflect the overall performance of the entire system rather than the performance of individual modules, compared to the separate evaluations of the two modules used in previous work.
Learning Neural Templates for Recommender Dialogue System
Second, only the items mentioned in the training corpus have a chance to be recommended in the conversation.
Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations
We study the problem of coarse-grained response selection in retrieval-based dialogue systems.
Re-entry Prediction for Online Conversations via Self-Supervised Learning
In recent years, world business in online discussions and opinion sharing on social media is booming.
Neural Rule-Execution Tracking Machine For Transformer-Based Text Generation
Sequence-to-Sequence (S2S) neural text generation models, especially the pre-trained ones (e. g., BART and T5), have exhibited compelling performance on various natural language generation tasks.
Maria: A Visual Experience Powered Conversational Agent
The retriever aims to retrieve a correlated image to the dialog from an image index, while the visual concept detector extracts rich visual knowledge from the image.
NASE: Learning Knowledge Graph Embedding for Link Prediction via Neural Architecture Search
While various forms of models are proposed for the link prediction task, most of them are designed based on a few known relation patterns in several well-known datasets.
Dance Revolution: Long-Term Dance Generation with Music via Curriculum Learning
In this paper, we formalize the music-conditioned dance generation as a sequence-to-sequence learning problem and devise a novel seq2seq architecture to efficiently process long sequences of music features and capture the fine-grained correspondence between music and dance.
Ranked #1 on
Motion Synthesis
on BRACE
Open Domain Dialogue Generation with Latent Images
Thus, we propose learning a response generation model with both image-grounded dialogues and textual dialogues by assuming that the visual scene information at the time of a conversation can be represented by an image, and trying to recover the latent images of the textual dialogues through text-to-image generation techniques.
Neural Response Generation with Meta-Words
We present open domain response generation with meta-words.
Playing 20 Question Game with Policy-Based Reinforcement Learning
The 20 Questions (Q20) game is a well known game which encourages deductive reasoning and creativity.
