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20152023
most citedPPT: Pre-trained Prompt Tuning for Few-shot Learning

100 citations · 823 across the 96 of their papers we have counts for

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Showing 2019Show all

17 papers · 1 filter

cs.CL2019★ 4 cited

Robust Reading Comprehension with Linguistic Constraints via Posterior Regularization

Mantong Zhou, Minlie Huang, Xiaoyan Zhu

In spite of great advancements of machine reading comprehension (RC), existing RC models are still vulnerable and not robust to different types of adversarial examples. Neural mode…

cs.CL2019★ 53 cited

The Eighth Dialog System Technology Challenge

Seokhwan Kim, Michel Galley, Chulaka Gunasekara +18

This paper introduces the Eighth Dialog System Technology Challenge. In line with recent challenges, the eighth edition focuses on applying end-to-end dialog technologies in a prag…

cs.CL2019★ 19 cited

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…

cs.CL2019

SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge

Pei Ke, Haozhe Ji, Siyang Liu +2

Most of the existing pre-trained language representation models neglect to consider the linguistic knowledge of texts, which can promote language understanding in NLP tasks. To ben…

cs.CL2019

Out-of-domain Detection for Natural Language Understanding in Dialog Systems

Yinhe Zheng, Guanyi Chen, Minlie Huang

Natural Language Understanding (NLU) is a vital component of dialogue systems, and its ability to detect Out-of-Domain (OOD) inputs is critical in practical applications, since the…

cs.CL2019

Robustness to Modification with Shared Words in Paraphrase Identification

Zhouxing Shi, Minlie Huang

Revealing the robustness issues of natural language processing models and improving their robustness is important to their performance under difficult situations. In this paper, we…