Search Results for author:
Found 42 papers, 15 papers with code
UnifieR: A Unified Retriever for Large-Scale Retrieval
Large-scale retrieval is to recall relevant documents from a huge collection given a query.
Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting
(2) How to cohere with context and preserve the knowledge when generating a stylized response.
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.
Recency Dropout for Recurrent Recommender Systems
Recurrent recommender systems have been successful in capturing the temporal dynamics in users' activity trajectories.
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.
Finetuning Large-Scale Pre-trained Language Models for Conversational Recommendation with Knowledge Graph
In this paper, we present a pre-trained language model (PLM) based framework called RID for conversational recommender system (CRS).
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.
Learning to Ground Visual Objects for Visual Dialog
Specifically, a posterior distribution over visual objects is inferred from both context (history and questions) and answers, and it ensures the appropriate grounding of visual objects during the training process.
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.
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.
ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation
ProphetNet is a pre-training based natural language generation method which shows powerful performance on English text summarization and question generation tasks.
Learning Matching Representations for Individualized Organ Transplantation Allocation
Organ transplantation is often the last resort for treating end-stage illness, but the probability of a successful transplantation depends greatly on compatibility between donors and recipients.
Are Pre-trained Language Models Knowledgeable to Ground Open Domain Dialogues?
We study knowledge-grounded dialogue generation with pre-trained language models.
Knowledge-Grounded Dialogue Generation with Pre-trained Language Models
We study knowledge-grounded dialogue generation with pre-trained language models.
StyleDGPT: Stylized Response Generation with Pre-trained Language Models
Generating responses following a desired style has great potentials to extend applications of open-domain dialogue systems, yet is refrained by lacking of parallel data for training.
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.
Learning a Simple and Effective Model for Multi-turn Response Generation with Auxiliary Tasks
We study multi-turn response generation for open-domain dialogues.
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.
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.
THUEE system description for NIST 2019 SRE CTS Challenge
This paper describes the systems submitted by the department of electronic engineering, institute of microelectronics of Tsinghua university and TsingMicro Co. Ltd. (THUEE) to the NIST 2019 speaker recognition evaluation CTS challenge.
Read, Attend and Comment: A Deep Architecture for Automatic NewsComment Generation
Automatic news comment generation is a new testbed for techniques of natural language generation.
Low-Resource Response Generation with Template Prior
Since the paired data now is no longer enough to train a neural generation model, we consider leveraging the large scale of unpaired data that are much easier to obtain, and propose response generation with both paired and unpaired data.
Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation
Automatic news comment generation is a new testbed for techniques of natural language generation.
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
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.
Towards Neural Mixture Recommender for Long Range Dependent User Sequences
Our approach employs a mixture of models, each with a different temporal range.
Multiobjective Optimization Training of PLDA for Speaker Verification
Most current state-of-the-art text-independent speaker verification systems take probabilistic linear discriminant analysis (PLDA) as their backend classifiers.
Multiobjective Optimization
Text-Independent Speaker Verification
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.
Towards Explainable and Controllable Open Domain Dialogue Generation with Dialogue Acts
We study open domain dialogue generation with dialogue acts designed to explain how people engage in social chat.
Towards Interpretable Chit-chat: Open Domain Dialogue Generation with Dialogue Acts
Conventional methods model open domain dialogue generation as a black box through end-to-end learning from large scale conversation data.
Neural Response Generation with Dynamic Vocabularies
We study response generation for open domain conversation in chatbots.
A Sequential Matching Framework for Multi-turn Response Selection in Retrieval-based Chatbots
The task requires matching a response candidate with a conversation context, whose challenges include how to recognize important parts of the context, and how to model the relationships among utterances in the context.
Large Margin Discriminant Dimensionality Reduction in Prediction Space
We argue that the intermediate mapping, e. g. boosting predictor, is preserving the discriminant aspects of the data and by controlling the dimension of this mapping it is possible to achieve discriminant low dimensional representations for the data.
Visual Sentiment Prediction with Deep Convolutional Neural Networks
Images have become one of the most popular types of media through which users convey their emotions within online social networks.
Learning Receptive Fields for Pooling from Tensors of Feature Response
A new method for learning pooling receptive fields for recognition is presented.
