1.2k citations · 2.5k across the 57 of their papers we have counts for
31 papers · 1 filter
Neural Grapheme-to-Phoneme Conversion with Pre-trained Grapheme Models
Lu Dong, Zhi-Qiang Guo, Chao-Hong Tan +3
Neural network models have achieved state-of-the-art performance on grapheme-to-phoneme (G2P) conversion. However, their performance relies on large-scale pronunciation dictionarie…
PoNet: Pooling Network for Efficient Token Mixing in Long Sequences
Chao-Hong Tan, Qian Chen, Wen Wang +3
Transformer-based models have achieved great success in various NLP, vision, and speech tasks. However, the core of Transformer, the self-attention mechanism, has a quadratic time…
MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding
Jia-Chen Gu, Chongyang Tao, Zhen-Hua Ling +3
Recently, various neural models for multi-party conversation (MPC) have achieved impressive improvements on a variety of tasks such as addressee recognition, speaker identification…
Partner Matters! An Empirical Study on Fusing Personas for Personalized Response Selection in Retrieval-Based Chatbots
Jia-Chen Gu, Hui Liu, Zhen-Hua Ling +3
Persona can function as the prior knowledge for maintaining the consistency of dialogue systems. Most of previous studies adopted the self persona in dialogue whose response was ab…
Emotion-Regularized Conditional Variational Autoencoder for Emotional Response Generation
Yu-Ping Ruan, Zhen-Hua Ling
This paper presents an emotion-regularized conditional variational autoencoder (Emo-CVAE) model for generating emotional conversation responses. In conventional CVAE-based emotiona…
Learning to Retrieve Entity-Aware Knowledge and Generate Responses with Copy Mechanism for Task-Oriented Dialogue Systems
Chao-Hong Tan, Xiaoyu Yang, Zi'ou Zheng +7
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 genera…
Tracking Interaction States for Multi-Turn Text-to-SQL Semantic Parsing
Run-Ze Wang, Zhen-Hua Ling, Jing-Bo Zhou +1
The task of multi-turn text-to-SQL semantic parsing aims to translate natural language utterances in an interaction into SQL queries in order to answer them using a database which…
Pre-Trained and Attention-Based Neural Networks for Building Noetic Task-Oriented Dialogue Systems
Jia-Chen Gu, Tianda Li, Quan Liu +3
The NOESIS II challenge, as the Track 2 of the 8th Dialogue System Technology Challenges (DSTC 8), is the extension of DSTC 7. This track incorporates new elements that are vital f…
Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots
Jia-Chen Gu, Zhen-Hua Ling, Quan Liu +2
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 bot…
DialBERT: A Hierarchical Pre-Trained Model for Conversation Disentanglement
Tianda Li, Jia-Chen Gu, Xiaodan Zhu +4
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…
Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots
Jia-Chen Gu, Tianda Li, Quan Liu +4
In this paper, we study the problem of employing pre-trained language models for multi-turn response selection in retrieval-based chatbots. A new model, named Speaker-Aware BERT (S…
Fine-Tuning BERT for Schema-Guided Zero-Shot Dialogue State Tracking
Yu-Ping Ruan, Zhen-Hua Ling, Jia-Chen Gu +1
We present our work on Track 4 in the Dialogue System Technology Challenges 8 (DSTC8). The DSTC8-Track 4 aims to perform dialogue state tracking (DST) under the zero-shot settings,…
Utterance-to-Utterance Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots
Jia-Chen Gu, Zhen-Hua Ling, Quan Liu
This paper proposes an utterance-to-utterance interactive matching network (U2U-IMN) for multi-turn response selection in retrieval-based chatbots. Different from previous methods…
Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models
Zhi-Xiu Ye, Qian Chen, Wen Wang +1
The state-of-the-art pre-trained language representation models, such as Bidirectional Encoder Representations from Transformers (BERT), rarely incorporate commonsense knowledge or…
Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots
Jia-Chen Gu, Zhen-Hua Ling, Xiaodan Zhu +1
This paper proposes a dually interactive matching network (DIM) for presenting the personalities of dialogue agents in retrieval-based chatbots. This model develops from the intera…
Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification
Zhi-Xiu Ye, Zhen-Hua Ling
This paper presents a multi-level matching and aggregation network (MLMAN) for few-shot relation classification. Previous studies on this topic adopt prototypical networks, which c…
Condition-Transforming Variational AutoEncoder for Conversation Response Generation
Yu-Ping Ruan, Zhen-Hua Ling, Quan Liu +2
This paper proposes a new model, called condition-transforming variational autoencoder (CTVAE), to improve the performance of conversation response generation using conditional var…
Exploring Unsupervised Pretraining and Sentence Structure Modelling for Winograd Schema Challenge
Yu-Ping Ruan, Xiaodan Zhu, Zhen-Hua Ling +3
Winograd Schema Challenge (WSC) was proposed as an AI-hard problem in testing computers' intelligence on common sense representation and reasoning. This paper presents the new stat…
Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions
Zhi-Xiu Ye, Zhen-Hua Ling
This paper presents a neural relation extraction method to deal with the noisy training data generated by distant supervision. Previous studies mainly focus on sentence-level de-no…
Building Sequential Inference Models for End-to-End Response Selection
Jia-Chen Gu, Zhen-Hua Ling, Yu-Ping Ruan +1
This paper presents an end-to-end response selection model for Track 1 of the 7th Dialogue System Technology Challenges (DSTC7). This task focuses on selecting the correct next utt…
Learning latent representations for style control and transfer in end-to-end speech synthesis
Ya-Jie Zhang, Shifeng Pan, Lei He +1
In this paper, we introduce the Variational Autoencoder (VAE) to an end-to-end speech synthesis model, to learn the latent representation of speaking styles in an unsupervised mann…
Forward Attention in Sequence-to-sequence Acoustic Modelling for Speech Synthesis
Jing-Xuan Zhang, Zhen-Hua Ling, Li-Rong Dai
This paper proposes a forward attention method for the sequenceto- sequence acoustic modeling of speech synthesis. This method is motivated by the nature of the monotonic alignment…
Enhancing Sentence Embedding with Generalized Pooling
Qian Chen, Zhen-Hua Ling, Xiaodan Zhu
Pooling is an essential component of a wide variety of sentence representation and embedding models. This paper explores generalized pooling methods to enhance sentence embedding.…
Hybrid semi-Markov CRF for Neural Sequence Labeling
Zhi-Xiu Ye, Zhen-Hua Ling
This paper proposes hybrid semi-Markov conditional random fields (SCRFs) for neural sequence labeling in natural language processing. Based on conventional conditional random field…