Search Results for author: Zhen-Hua Ling

Found 50 papers, 28 papers with code

Conversation- and Tree-Structure Losses for Dialogue Disentanglement

no code implementations dialdoc (ACL) 2022 Tianda Li, Jia-Chen Gu, Zhen-Hua Ling, Quan Liu

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.

Disentanglement

HeterMPC: A Heterogeneous Graph Neural Network for Response Generation in Multi-Party Conversations

1 code implementation ACL 2022 Jia-Chen Gu, Chao-Hong Tan, Chongyang Tao, Zhen-Hua Ling, Huang Hu, Xiubo Geng, Daxin Jiang

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.

Response Generation

USTC-NELSLIP at SemEval-2022 Task 11: Gazetteer-Adapted Integration Network for Multilingual Complex Named Entity Recognition

1 code implementation7 Mar 2022 Beiduo Chen, Jun-Yu Ma, Jiajun Qi, Wu Guo, Zhen-Hua Ling, Quan Liu

The proposed method is applied to several state-of-the-art Transformer-based NER models with a gazetteer built from Wikidata, and shows great generalization ability across them.

Named Entity Recognition NER

Neural Grapheme-to-Phoneme Conversion with Pre-trained Grapheme Models

1 code implementation26 Jan 2022 Lu Dong, Zhi-Qiang Guo, Chao-Hong Tan, Ya-Jun Hu, Yuan Jiang, Zhen-Hua Ling

Neural network models have achieved state-of-the-art performance on grapheme-to-phoneme (G2P) conversion.

Language Modelling

Detecting Speaker Personas from Conversational Texts

1 code implementation EMNLP 2021 Jia-Chen Gu, Zhen-Hua Ling, Yu Wu, Quan Liu, Zhigang Chen, Xiaodan Zhu

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

1 code implementation ACL 2021 Jia-Chen Gu, Chongyang Tao, Zhen-Hua Ling, Can Xu, Xiubo Geng, Daxin Jiang

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.

Language Modelling Speaker Identification

Partner Matters! An Empirical Study on Fusing Personas for Personalized Response Selection in Retrieval-Based Chatbots

1 code implementation19 May 2021 Jia-Chen Gu, Hui Liu, Zhen-Hua Ling, Quan Liu, Zhigang Chen, Xiaodan Zhu

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.

Emotion-Regularized Conditional Variational Autoencoder for Emotional Response Generation

no code implementations18 Apr 2021 Yu-Ping Ruan, Zhen-Hua Ling

This paper presents an emotion-regularized conditional variational autoencoder (Emo-CVAE) model for generating emotional conversation responses.

Response Generation

Learning to Retrieve Entity-Aware Knowledge and Generate Responses with Copy Mechanism for Task-Oriented Dialogue Systems

1 code implementation22 Dec 2020 Chao-Hong Tan, Xiaoyu Yang, Zi'ou Zheng, Tianda Li, Yufei Feng, Jia-Chen Gu, Quan Liu, Dan Liu, Zhen-Hua Ling, Xiaodan Zhu

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.

Response Generation Task-Oriented Dialogue Systems

Tracking Interaction States for Multi-Turn Text-to-SQL Semantic Parsing

1 code implementation9 Dec 2020 Run-Ze Wang, Zhen-Hua Ling, Jing-Bo Zhou, Yu Hu

The dynamic schema-state and SQL-state representations are then utilized to decode the SQL query corresponding to current utterance.

Semantic Parsing Text-To-Sql

Voice Conversion by Cascading Automatic Speech Recognition and Text-to-Speech Synthesis with Prosody Transfer

no code implementations3 Sep 2020 Jing-Xuan Zhang, Li-Juan Liu, Yan-Nian Chen, Ya-Jun Hu, Yuan Jiang, Zhen-Hua Ling, Li-Rong Dai

In this paper, we present a ASR-TTS method for voice conversion, which used iFLYTEK ASR engine to transcribe the source speech into text and a Transformer TTS model with WaveNet vocoder to synthesize the converted speech from the decoded text.

Automatic Speech Recognition Speech Synthesis +2

Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots

1 code implementation Findings of the Association for Computational Linguistics 2020 Jia-Chen Gu, Zhen-Hua Ling, Quan Liu, Zhigang Chen, Xiaodan Zhu

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

1 code implementation8 Apr 2020 Tianda Li, Jia-Chen Gu, Xiaodan Zhu, Quan Liu, Zhen-Hua Ling, Zhiming Su, Si Wei

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.

Conversation Disentanglement Disentanglement

Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models

no code implementations19 Aug 2019 Zhi-Xiu Ye, Qian Chen, Wen Wang, Zhen-Hua Ling

We also observe that fine-tuned models after the proposed pre-training approach maintain comparable performance on other NLP tasks, such as sentence classification and natural language inference tasks, compared to the original BERT models.

Common Sense Reasoning Natural Language Inference +2

Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots

1 code implementation IJCNLP 2019 Jia-Chen Gu, Zhen-Hua Ling, Xiaodan Zhu, Quan Liu

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.

Non-Parallel Sequence-to-Sequence Voice Conversion with Disentangled Linguistic and Speaker Representations

1 code implementation25 Jun 2019 Jing-Xuan Zhang, Zhen-Hua Ling, Li-Rong Dai

In this method, disentangled linguistic and speaker representations are extracted from acoustic features, and voice conversion is achieved by preserving the linguistic representations of source utterances while replacing the speaker representations with the target ones.

Audio and Speech Processing Sound

Singing Voice Synthesis Using Deep Autoregressive Neural Networks for Acoustic Modeling

no code implementations21 Jun 2019 Yuan-Hao Yi, Yang Ai, Zhen-Hua Ling, Li-Rong Dai

This paper presents a method of using autoregressive neural networks for the acoustic modeling of singing voice synthesis (SVS).

Condition-Transforming Variational AutoEncoder for Conversation Response Generation

no code implementations24 Apr 2019 Yu-Ping Ruan, Zhen-Hua Ling, Quan Liu, Zhigang Chen, Nitin Indurkhya

This paper proposes a new model, called condition-transforming variational autoencoder (CTVAE), to improve the performance of conversation response generation using conditional variational autoencoders (CVAEs).

Response Generation

Exploring Unsupervised Pretraining and Sentence Structure Modelling for Winograd Schema Challenge

no code implementations22 Apr 2019 Yu-Ping Ruan, Xiaodan Zhu, Zhen-Hua Ling, Zhan Shi, Quan Liu, Si Wei

Winograd Schema Challenge (WSC) was proposed as an AI-hard problem in testing computers' intelligence on common sense representation and reasoning.

Common Sense Reasoning

Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions

1 code implementation NAACL 2019 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.

Relation Extraction Sentence Embeddings

Promoting Diversity for End-to-End Conversation Response Generation

no code implementations27 Jan 2019 Yu-Ping Ruan, Zhen-Hua Ling, Quan Liu, Jia-Chen Gu, Xiaodan Zhu

At this stage, two different models are proposed, i. e., a variational generative (VariGen) model and a retrieval based (Retrieval) model.

Response Generation

Learning latent representations for style control and transfer in end-to-end speech synthesis

2 code implementations11 Dec 2018 Ya-Jie Zhang, Shifeng Pan, Lei He, Zhen-Hua Ling

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 manner.

Speech Synthesis Style Transfer

Forward Attention in Sequence-to-sequence Acoustic Modelling for Speech Synthesis

no code implementations18 Jul 2018 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.

Acoustic Modelling Speech Synthesis

Hybrid semi-Markov CRF for Neural Sequence Labeling

1 code implementation ACL 2018 Zhi-Xiu Ye, Zhen-Hua Ling

This paper proposes hybrid semi-Markov conditional random fields (SCRFs) for neural sequence labeling in natural language processing.

Named Entity Recognition NER

A Spoofing Benchmark for the 2018 Voice Conversion Challenge: Leveraging from Spoofing Countermeasures for Speech Artifact Assessment

no code implementations23 Apr 2018 Tomi Kinnunen, Jaime Lorenzo-Trueba, Junichi Yamagishi, Tomoki Toda, Daisuke Saito, Fernando Villavicencio, Zhen-Hua Ling

As a supplement to subjective results for the 2018 Voice Conversion Challenge (VCC'18) data, we configure a standard constant-Q cepstral coefficient CM to quantify the extent of processing artifacts.

Speaker Verification Voice Conversion

The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods

no code implementations12 Apr 2018 Jaime Lorenzo-Trueba, Junichi Yamagishi, Tomoki Toda, Daisuke Saito, Fernando Villavicencio, Tomi Kinnunen, Zhen-Hua Ling

We present the Voice Conversion Challenge 2018, designed as a follow up to the 2016 edition with the aim of providing a common framework for evaluating and comparing different state-of-the-art voice conversion (VC) systems.

Voice Conversion

A Sequential Neural Encoder with Latent Structured Description for Modeling Sentences

no code implementations15 Nov 2017 Yu-Ping Ruan, Qian Chen, Zhen-Hua Ling

The description layer utilizes modified LSTM units to process these chunk-level vectors in a recurrent manner and produces sequential encoding outputs.

Chunking Natural Language Inference +2

Neural Natural Language Inference Models Enhanced with External Knowledge

1 code implementation ACL 2018 Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Diana Inkpen, Si Wei

With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-the-art performance.

Natural Language Inference

Recurrent Neural Network-Based Sentence Encoder with Gated Attention for Natural Language Inference

2 code implementations WS 2017 Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, Diana Inkpen

The RepEval 2017 Shared Task aims to evaluate natural language understanding models for sentence representation, in which a sentence is represented as a fixed-length vector with neural networks and the quality of the representation is tested with a natural language inference task.

Natural Language Inference Natural Language Understanding

Commonsense Knowledge Enhanced Embeddings for Solving Pronoun Disambiguation Problems in Winograd Schema Challenge

no code implementations13 Nov 2016 Quan Liu, Hui Jiang, Zhen-Hua Ling, Xiaodan Zhu, Si Wei, Yu Hu

The PDP task we investigate in this paper is a complex coreference resolution task which requires the utilization of commonsense knowledge.

Coreference Resolution

Distraction-Based Neural Networks for Document Summarization

1 code implementation26 Oct 2016 Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang

Distributed representation learned with neural networks has recently shown to be effective in modeling natural languages at fine granularities such as words, phrases, and even sentences.

Document Summarization

Part-of-Speech Relevance Weights for Learning Word Embeddings

no code implementations24 Mar 2016 Quan Liu, Zhen-Hua Ling, Hui Jiang, Yu Hu

The model proposed in this paper paper jointly optimizes word vectors and the POS relevance matrices.

Learning Word Embeddings POS +1

Integrate Document Ranking Information into Confidence Measure Calculation for Spoken Term Detection

no code implementations7 Sep 2015 Quan Liu, Wu Guo, Zhen-Hua Ling

The confidence measure of each term occurrence is then re-estimated through linear interpolation with the calculated document ranking weight to improve its reliability by integrating document-level information.

Document Ranking

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