30 citations · 84 across the 12 of their papers we have counts for
14 papers · 1 filter
Tapping the Potential of Coherence and Syntactic Features in Neural Models for Automatic Essay Scoring
Xinying Qiu, Shuxuan Liao, Jiajun Xie +1
In the prompt-specific holistic score prediction task for Automatic Essay Scoring, the general approaches include pre-trained neural model, coherence model, and hybrid model that i…
ELMER: A Non-Autoregressive Pre-trained Language Model for Efficient and Effective Text Generation
Junyi Li, Tianyi Tang, Wayne Xin Zhao +2
We study the text generation task under the approach of pre-trained language models (PLMs). Typically, an auto-regressive (AR) method is adopted for generating texts in a token-by-…
Learning to Transfer Prompts for Text Generation
Junyi Li, Tianyi Tang, Jian-Yun Nie +2
Pretrained language models (PLMs) have made remarkable progress in text generation tasks via fine-tuning. While, it is challenging to fine-tune PLMs in a data-scarce situation. The…
Proactive Retrieval-based Chatbots based on Relevant Knowledge and Goals
Yutao Zhu, Jian-Yun Nie, Kun Zhou +3
A proactive dialogue system has the ability to proactively lead the conversation. Different from the general chatbots which only react to the user, proactive dialogue systems can b…
Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment
Xinying Qiu, Yuan Chen, Hanwu Chen +3
Deep learning models for automatic readability assessment generally discard linguistic features traditionally used in machine learning models for the task. We propose to incorporat…
BERT4SO: Neural Sentence Ordering by Fine-tuning BERT
Yutao Zhu, Jian-Yun Nie, Kun Zhou +3
Sentence ordering aims to arrange the sentences of a given text in the correct order. Recent work frames it as a ranking problem and applies deep neural networks to it. In this wor…