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Found 36 papers, 14 papers with code
There Are a Thousand Hamlets in a Thousand People’s Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory
Knowledge-grounded conversation (KGC) shows great potential in building an engaging and knowledgeable chatbot, and knowledge selection is a key ingredient in it.
Multi-Granularity Structural Knowledge Distillation for Language Model Compression
Transferring the knowledge to a small model through distillation has raised great interest in recent years.
ProphetChat: Enhancing Dialogue Generation with Simulation of Future Conversation
To enable the chatbot to foresee the dialogue future, we design a beam-search-like roll-out strategy for dialogue future simulation using a typical dialogue generation model and a dialogue selector.
UnifieR: A Unified Retriever for Large-Scale Retrieval
Large-scale retrieval is to recall relevant documents from a huge collection given a query.
Learning to Express in Knowledge-Grounded Conversation
Grounding dialogue generation by extra knowledge has shown great potentials towards building a system capable of replying with knowledgeable and engaging responses.
There Are a Thousand Hamlets in a Thousand People's Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory
In this work, we introduce personal memory into knowledge selection in KGC to address the personalization issue.
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.
PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence Embeddings
A straightforward solution is resorting to more diverse positives from a multi-augmenting strategy, while an open question remains about how to unsupervisedly learn from the diverse positives but with uneven augmenting qualities in the text field.
Building an Efficient and Effective Retrieval-based Dialogue System via Mutual Learning
For this task, the adoption of pre-trained language models (such as BERT) has led to remarkable progress in a number of benchmarks.
Response Ranking with Multi-types of Deep Interactive Representations in Retrieval-based Dialogues
To tackle these challenges, we propose a representation[K]-interaction[L]-matching framework that explores multiple types of deep interactive representations to build context-response matching models for response selection.
A Pre-training Strategy for Zero-Resource Response Selection in Knowledge-Grounded Conversations
Recently, many studies are emerging towards building a retrieval-based dialogue system that is able to effectively leverage background knowledge (e. g., documents) when conversing with humans.
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.
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.
Learning to Organize a Bag of Words into Sentences with Neural Networks: An Empirical Study
Sequential information, a. k. a., orders, is assumed to be essential for processing a sequence with recurrent neural network or convolutional neural network based encoders.
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.
A Benchmarking on Cloud based Speech-To-Text Services for French Speech and Background Noise Effect
This study presents a large scale benchmarking on cloud based Speech-To-Text systems: {Google Cloud Speech-To-Text}, {Microsoft Azure Cognitive Services}, {Amazon Transcribe}, {IBM Watson Speech to Text}.
Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots
To fill the gap between these up-to-date methods and the real-world applications, we incorporate user-specific dialogue history into the response selection and propose a personalized hybrid matching network (PHMN).
Knowledge-Grounded Dialogue Generation with Pre-trained Language Models
We study knowledge-grounded dialogue generation with pre-trained language models.
Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues
To address these issues, in this paper, we propose learning a context-response matching model with auxiliary self-supervised tasks designed for the dialogue data based on pre-trained language models.
Ranked #2 on
Conversational Response Selection
on E-commerce
Zero-Resource Knowledge-Grounded Dialogue Generation
While neural conversation models have shown great potentials towards generating informative and engaging responses via introducing external knowledge, learning such a model often requires knowledge-grounded dialogues that are difficult to obtain.
EnsembleGAN: Adversarial Learning for Retrieval-Generation Ensemble Model on Short-Text Conversation
Aiming at generating responses that approximate the ground-truth and receive high ranking scores from the discriminator, the two generators learn to generate improved highly relevant responses and competitive unobserved candidates respectively, while the discriminative ranker is trained to identify true responses from adversarial ones, thus featuring the merits of both generator counterparts.
Low-Resource Knowledge-Grounded Dialogue Generation
In such a low-resource setting, we devise a disentangled response decoder in order to isolate parameters that depend on knowledge-grounded dialogues from the entire generation model.
Sampling Matters! An Empirical Study of Negative Sampling Strategies for Learning of Matching Models in Retrieval-based Dialogue Systems
We study how to sample negative examples to automatically construct a training set for effective model learning in retrieval-based dialogue systems.
One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues
Currently, researchers have paid great attention to retrieval-based dialogues in open-domain.
Ranked #8 on
Conversational Response Selection
on Douban
Mimicking Human Process: Text Representation via Latent Semantic Clustering for Classification
Considering that words with different characteristic in the text have different importance for classification, grouping them together separately can strengthen the semantic expression of each part.
Neural Response Generation with Meta-Words
We present open domain response generation with meta-words.
A Document-grounded Matching Network for Response Selection in Retrieval-based Chatbots
We present a document-grounded matching network (DGMN) for response selection that can power a knowledge-aware retrieval-based chatbot system.
Learning a Matching Model with Co-teaching for Multi-turn Response Selection in Retrieval-based Dialogue Systems
Under the framework, we simultaneously learn two matching models with independent training sets.
Overcoming Catastrophic Forgetting via Model Adaptation
Several continual learning methods have been proposed to address the problem.
Iterative Document Representation Learning Towards Summarization with Polishing
In this paper, we introduce Iterative Text Summarization (ITS), an iteration-based model for supervised extractive text summarization, inspired by the observation that it is often necessary for a human to read an article multiple times in order to fully understand and summarize its contents.
Ranked #13 on
Extractive Text Summarization
on CNN / Daily Mail
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.
Improving Matching Models with Hierarchical Contextualized Representations for Multi-turn Response Selection
In this paper, we study context-response matching with pre-trained contextualized representations for multi-turn response selection in retrieval-based chatbots.
Tree2Tree Learning with Memory Unit
Traditional recurrent neural network (RNN) or convolutional neural net- work (CNN) based sequence-to-sequence model can not handle tree structural data well.
RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems
Open-domain human-computer conversation has been attracting increasing attention over the past few years.
