24 citations · 114 across the 41 of their papers we have counts for
19 papers · 1 filter
Exploring Energy-based Language Models with Different Architectures and Training Methods for Speech Recognition
Hong Liu, Zhaobiao Lv, Zhijian Ou +2
Energy-based language models (ELMs) parameterize an unnormalized distribution for natural sentences and are radically different from popular autoregressive language models (ALMs).…
Knowledge-Retrieval Task-Oriented Dialog Systems with Semi-Supervision
Yucheng Cai, Hong Liu, Zhijian Ou +2
Most existing task-oriented dialog (TOD) systems track dialog states in terms of slots and values and use them to query a database to get relevant knowledge to generate responses.…
A Generative User Simulator with GPT-based Architecture and Goal State Tracking for Reinforced Multi-Domain Dialog Systems
Hong Liu, Yucheng Cai, Zhijian Ou +2
Building user simulators (USs) for reinforcement learning (RL) of task-oriented dialog systems (DSs) has gained more and more attention, which, however, still faces several fundame…
Information Extraction and Human-Robot Dialogue towards Real-life Tasks: A Baseline Study with the MobileCS Dataset
Hong Liu, Hao Peng, Zhijian Ou +3
Recently, there have merged a class of task-oriented dialogue (TOD) datasets collected through Wizard-of-Oz simulated games. However, the Wizard-of-Oz data are in fact simulated da…
Advancing Semi-Supervised Task Oriented Dialog Systems by JSA Learning of Discrete Latent Variable Models
Yucheng Cai, Hong Liu, Zhijian Ou +2
Developing semi-supervised task-oriented dialog (TOD) systems by leveraging unlabeled dialog data has attracted increasing interests. For semi-supervised learning of latent state T…
A Challenge on Semi-Supervised and Reinforced Task-Oriented Dialog Systems
Zhijian Ou, Junlan Feng, Juanzi Li +5
A challenge on Semi-Supervised and Reinforced Task-Oriented Dialog Systems, Co-located with EMNLP2022 SereTOD Workshop.
Building Markovian Generative Architectures over Pretrained LM Backbones for Efficient Task-Oriented Dialog Systems
Hong Liu, Yucheng Cai, Zhijian Ou +2
Recently, Transformer based pretrained language models (PLMs), such as GPT2 and T5, have been leveraged to build generative task-oriented dialog (TOD) systems. A drawback of existi…
Variational Latent-State GPT for Semi-Supervised Task-Oriented Dialog Systems
Hong Liu, Yucheng Cai, Zhenru Lin +3
Recently, two approaches, fine-tuning large pre-trained language models and variational training, have attracted significant interests, separately, for semi-supervised end-to-end t…
Multilingual and crosslingual speech recognition using phonological-vector based phone embeddings
Chengrui Zhu, Keyu An, Huahuan Zheng +1
The use of phonological features (PFs) potentially allows language-specific phones to remain linked in training, which is highly desirable for information sharing for multilingual…
A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised Learning
Yichi Zhang, Zhijian Ou, Huixin Wang +1
Structured belief states are crucial for user goal tracking and database query in task-oriented dialog systems. However, training belief trackers often requires expensive turn-leve…
Paraphrase Augmented Task-Oriented Dialog Generation
Silin Gao, Yichi Zhang, Zhijian Ou +1
Neural generative models have achieved promising performance on dialog generation tasks if given a huge data set. However, the lack of high-quality dialog data and the expensive da…
Integrating Discrete and Neural Features via Mixed-feature Trans-dimensional Random Field Language Models
Silin Gao, Zhijian Ou, Wei Yang +1
There has been a long recognition that discrete features (n-gram features) and neural network based features have complementary strengths for language models (LMs). Improved perfor…
Task-Oriented Dialog Systems that Consider Multiple Appropriate Responses under the Same Context
Yichi Zhang, Zhijian Ou, Zhou Yu
Conversations have an intrinsic one-to-many property, which means that multiple responses can be appropriate for the same dialog context. In task-oriented dialogs, this property le…
Elastic CRFs for Open-ontology Slot Filling
Yinpei Dai, Yichi Zhang, Hong Liu +3
Slot filling is a crucial component in task-oriented dialog systems that is used to parse (user) utterances into semantic concepts called slots. An ontology is defined by the colle…
Improved training of neural trans-dimensional random field language models with dynamic noise-contrastive estimation
Bin Wang, Zhijian Ou
A new whole-sentence language model - neural trans-dimensional random field language model (neural TRF LM), where sentences are modeled as a collection of random fields, and the po…
Tracking of enriched dialog states for flexible conversational information access
Yinpei Dai, Zhijian Ou, Dawei Ren +1
Dialog state tracking (DST) is a crucial component in a task-oriented dialog system for conversational information access. A common practice in current dialog systems is to define…
Learning neural trans-dimensional random field language models with noise-contrastive estimation
Bin Wang, Zhijian Ou
Trans-dimensional random field language models (TRF LMs) where sentences are modeled as a collection of random fields, have shown close performance with LSTM LMs in speech recognit…
Language modeling with Neural trans-dimensional random fields
Bin Wang, Zhijian Ou
Trans-dimensional random field language models (TRF LMs) have recently been introduced, where sentences are modeled as a collection of random fields. The TRF approach has been show…
Model Interpolation with Trans-dimensional Random Field Language Models for Speech Recognition
Bin Wang, Zhijian Ou, Yong He +1
The dominant language models (LMs) such as n-gram and neural network (NN) models represent sentence probabilities in terms of conditionals. In contrast, a new trans-dimensional ran…