19 citations · 45 across the 15 of their papers we have counts for
15 papers
Granular Change Accuracy: A More Accurate Performance Metric for Dialogue State Tracking
Taha Aksu, Nancy F. Chen
Current metrics for evaluating Dialogue State Tracking (DST) systems exhibit three primary limitations. They: i) erroneously presume a uniform distribution of slots throughout the…
DecBERT: Enhancing the Language Understanding of BERT with Causal Attention Masks
Ziyang Luo, Yadong Xi, Jing Ma +4
Since 2017, the Transformer-based models play critical roles in various downstream Natural Language Processing tasks. However, a common limitation of the attention mechanism utiliz…
Dialog Intent Induction via Density-based Deep Clustering Ensemble
Jiashu Pu, Guandan Chen, Yongzhu Chang +1
Existing task-oriented chatbots heavily rely on spoken language understanding (SLU) systems to determine a user's utterance's intent and other key information for fulfilling specif…
Youling: an AI-Assisted Lyrics Creation System
Rongsheng Zhang, Xiaoxi Mao, Le Li +6
Recently, a variety of neural models have been proposed for lyrics generation. However, most previous work completes the generation process in a single pass with little human inter…
Taming Repetition in Dialogue Generation
Yadong Xi, Jiashu Pu, Xiaoxi Mao
The wave of pre-training language models has been continuously improving the quality of the machine-generated conversations, however, some of the generated responses still suffer f…
Unsupervised Domain Adaptation with Adapter
Rongsheng Zhang, Yinhe Zheng, Xiaoxi Mao +1
Unsupervised domain adaptation (UDA) with pre-trained language models (PrLM) has achieved promising results since these pre-trained models embed generic knowledge learned from vari…
Transferable Persona-Grounded Dialogues via Grounded Minimal Edits
Chen Henry Wu, Yinhe Zheng, Xiaoxi Mao +1
Grounded dialogue models generate responses that are grounded on certain concepts. Limited by the distribution of grounded dialogue data, models trained on such data face the trans…
LOT: A Story-Centric Benchmark for Evaluating Chinese Long Text Understanding and Generation
Jian Guan, Zhuoer Feng, Yamei Chen +4
Standard multi-task benchmarks are essential for developing pretraining models that can generalize to various downstream tasks. Existing benchmarks for natural language processing…
Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence
Jian Guan, Xiaoxi Mao, Changjie Fan +3
Generating long and coherent text is an important but challenging task, particularly for open-ended language generation tasks such as story generation. Despite the success in model…
OpenMEVA: A Benchmark for Evaluating Open-ended Story Generation Metrics
Jian Guan, Zhexin Zhang, Zhuoer Feng +5
Automatic metrics are essential for developing natural language generation (NLG) models, particularly for open-ended language generation tasks such as story generation. However, ex…
Easy and Efficient Transformer : Scalable Inference Solution For large NLP model
Gongzheng Li, Yadong Xi, Jingzhen Ding +5
Recently, large-scale transformer-based models have been proven to be effective over various tasks across many domains. Nevertheless, applying them in industrial production require…
Positional Artefacts Propagate Through Masked Language Model Embeddings
Ziyang Luo, Artur Kulmizev, Xiaoxi Mao
In this work, we demonstrate that the contextualized word vectors derived from pretrained masked language model-based encoders share a common, perhaps undesirable pattern across la…
Stylized Dialogue Response Generation Using Stylized Unpaired Texts
Yinhe Zheng, Zikai Chen, Rongsheng Zhang +3
Generating stylized responses is essential to build intelligent and engaging dialogue systems. However, this task is far from well-explored due to the difficulties of rendering a p…
Dialogue Distillation: Open-Domain Dialogue Augmentation Using Unpaired Data
Rongsheng Zhang, Yinhe Zheng, Jianzhi Shao +3
Recent advances in open-domain dialogue systems rely on the success of neural models that are trained on large-scale data. However, collecting large-scale dialogue data is usually…
A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data
Yinhe Zheng, Rongsheng Zhang, Xiaoxi Mao +1
Endowing dialogue systems with personas is essential to deliver more human-like conversations. However, this problem is still far from well explored due to the difficulties of both…