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Yan Zhen

Publications and source records attributed to Yan Zhen.

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WISE view of changing-look AGNs: evidence for a transitional stage of AGNs

The discovery of changing-look active galactic nuclei (CLAGNs) with the significant change of optical broad emission lines (optical CLAGNs) and/or strong variation of line-of-sight column densities (X-ray CLAGNs) challenges the orientation-based AGN unification model. We explore mid-infrared (mid-IR) properties for a sample of 57 optical CLAGNs and 11 X-ray CLAGNs based on the {\it Wide-field Infrared Survey Explorer} ({\it WISE}) archive data. We find that Eddington-scaled mid-IR luminosities of both optical and X-ray CLAGNs stay just between low-luminosity AGNs (LLAGNs) and luminous QSOs. The average Eddington-scaled mid-IR luminosities for optical and X-ray CLAGNs are $\sim 0.4$\% and $\sim 0.5$\%, respectively, which roughly correspond the bolometric luminosity of transition between a radiatively inefficient accretion flow (RIAF) and Shakura-Sunyaev disk (SSD). We estimate the time lags of the variation in the mid-IR behind that in the optical band for 13 CLAGNs with strong mid-IR variability, where the tight correlation between the time lag and the bolometric luminosity ($\tau - L$) for CLAGNs roughly follows that found in the luminous QSOs.

astro-ph.HE

BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension on Novels

This paper presents BiPaR, a bilingual parallel novel-style machine reading comprehension (MRC) dataset, developed to support multilingual and cross-lingual reading comprehension. The biggest difference between BiPaR and existing reading comprehension datasets is that each triple (Passage, Question, Answer) in BiPaR is written parallelly in two languages. We collect 3,667 bilingual parallel paragraphs from Chinese and English novels, from which we construct 14,668 parallel question-answer pairs via crowdsourced workers following a strict quality control procedure. We analyze BiPaR in depth and find that BiPaR offers good diversification in prefixes of questions, answer types and relationships between questions and passages. We also observe that answering questions of novels requires reading comprehension skills of coreference resolution, multi-sentence reasoning, and understanding of implicit causality, etc. With BiPaR, we build monolingual, multilingual, and cross-lingual MRC baseline models. Even for the relatively simple monolingual MRC on this dataset, experiments show that a strong BERT baseline is over 30 points behind human in terms of both EM and F1 score, indicating that BiPaR provides a challenging testbed for monolingual, multilingual and cross-lingual MRC on novels. The dataset is available at https://multinlp.github.io/BiPaR/.

cs.CL