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20182020
most citedEntity-Relation Extraction as Multi-Turn Question Answering

48 citations · 167 across the 7 of their papers we have counts for

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6 papers · 1 filter

cs.CL2020★ 10 cited

LAVA NAT: A Non-Autoregressive Translation Model with Look-Around Decoding and Vocabulary Attention

Xiaoya Li, Yuxian Meng, Arianna Yuan +2

Non-autoregressive translation (NAT) models generate multiple tokens in one forward pass and is highly efficient at inference stage compared with autoregressive translation (AT) me…

cs.CL2019★ 21 cited

Coreference Resolution as Query-based Span Prediction

Wei Wu, Fei Wang, Arianna Yuan +2

In this paper, we present an accurate and extensible approach for the coreference resolution task. We formulate the problem as a span prediction task, like in machine reading compr…

cs.CL2019★ 8 cited

Large-scale Pretraining for Neural Machine Translation with Tens of Billions of Sentence Pairs

Yuxian Meng, Xiangyuan Ren, Zijun Sun +4

In this paper, we investigate the problem of training neural machine translation (NMT) systems with a dataset of more than 40 billion bilingual sentence pairs, which is larger than…

cs.CL2019★ 48 cited

Entity-Relation Extraction as Multi-Turn Question Answering

Xiaoya Li, Fan Yin, Zijun Sun +6

In this paper, we propose a new paradigm for the task of entity-relation extraction. We cast the task as a multi-turn question answering problem, i.e., the extraction of entities a…

cs.CL2019★ 20 cited

Is Word Segmentation Necessary for Deep Learning of Chinese Representations?

Xiaoya Li, Yuxian Meng, Xiaofei Sun +3

Segmenting a chunk of text into words is usually the first step of processing Chinese text, but its necessity has rarely been explored. In this paper, we ask the fundamental questi…

cs.CL2018★ 26 cited

IcoRating: A Deep-Learning System for Scam ICO Identification

Shuqing Bian, Zhenpeng Deng, Fei Li +9

Cryptocurrencies (or digital tokens, digital currencies, e.g., BTC, ETH, XRP, NEO) have been rapidly gaining ground in use, value, and understanding among the public, bringing asto…