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20022026
most citedDeep Isolation Forest for Anomaly Detection

541 citations

Showing 2020 · cs.CLShow all

8 papers · 2 filters

cs.CL2020★ 1 cited

A BERT-based Dual Embedding Model for Chinese Idiom Prediction

Minghuan Tan, Jing Jiang

Chinese idioms are special fixed phrases usually derived from ancient stories, whose meanings are oftentimes highly idiomatic and non-compositional. The Chinese idiom prediction ta…

cs.CL2020★ 2 cited

Video-Grounded Dialogues with Pretrained Generation Language Models

Hung Le, Steven C. H. Hoi

Pre-trained language models have shown remarkable success in improving various downstream NLP tasks due to their ability to capture dependencies in textual data and generate natura…

cs.CL2020★ 9 cited

Aspect and Opinion Aware Abstractive Review Summarization with Reinforced Hard Typed Decoder

Yufei Tian, Jianfei Yu, Jing Jiang

In this paper, we study abstractive review summarization.Observing that review summaries often consist of aspect words, opinion words and context words, we propose a two-stage rein…

cs.CL2020★ 2 cited

UniConv: A Unified Conversational Neural Architecture for Multi-domain Task-oriented Dialogues

Hung Le, Doyen Sahoo, Chenghao Liu +2

Building an end-to-end conversational agent for multi-domain task-oriented dialogues has been an open challenge for two main reasons. First, tracking dialogue states of multiple do…

cs.CL2020★ 32 cited

RecipeGPT: Generative Pre-training Based Cooking Recipe Generation and Evaluation System

Helena H. Lee, Ke Shu, Palakorn Achananuparp +4

Interests in the automatic generation of cooking recipes have been growing steadily over the past few years thanks to a large amount of online cooking recipes. We present RecipeGPT…

cs.CL2020★ 49 cited

FrameAxis: Characterizing Microframe Bias and Intensity with Word Embedding

Haewoon Kwak, Jisun An, Elise Jing +1

Framing is a process of emphasizing a certain aspect of an issue over the others, nudging readers or listeners towards different positions on the issue even without making a biased…