arXiv subjects

Qin Chen

Publications and source records attributed to Qin Chen.

90 records · Page 5Linked to original sources

arXiv2022

A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck

Event argument extraction (EAE) aims to extract arguments with given roles from texts, which have been widely studied in natural language processing. Most previous works have achieved good performance in specific EAE datasets with dedicated neural architectures. Whereas, these architectures are usually difficult to adapt to new datasets/scenarios with various annotation schemas or formats. Furthermore, they rely on large-scale labeled data for training, which is unavailable due to the high labelling cost in most cases. In this paper, we propose a multi-format transfer learning model with variational information bottleneck, which makes use of the information especially the common knowledge in existing datasets for EAE in new datasets. Specifically, we introduce a shared-specific prompt framework to learn both format-shared and format-specific knowledge from datasets with different formats. In order to further absorb the common knowledge for EAE and eliminate the irrelevant noise, we integrate variational information bottleneck into our architecture to refine the shared representation. We conduct extensive experiments on three benchmark datasets, and obtain new state-of-the-art performance on EAE.

cs.CL↗

arXiv2022

CUP: Curriculum Learning based Prompt Tuning for Implicit Event Argument Extraction

Implicit event argument extraction (EAE) aims to identify arguments that could scatter over the document. Most previous work focuses on learning the direct relations between arguments and the given trigger, while the implicit relations with long-range dependency are not well studied. Moreover, recent neural network based approaches rely on a large amount of labeled data for training, which is unavailable due to the high labelling cost. In this paper, we propose a Curriculum learning based Prompt tuning (CUP) approach, which resolves implicit EAE by four learning stages. The stages are defined according to the relations with the trigger node in a semantic graph, which well captures the long-range dependency between arguments and the trigger. In addition, we integrate a prompt-based encoder-decoder model to elicit related knowledge from pre-trained language models (PLMs) in each stage, where the prompt templates are adapted with the learning progress to enhance the reasoning for arguments. Experimental results on two well-known benchmark datasets show the great advantages of our proposed approach. In particular, we outperform the state-of-the-art models in both fully-supervised and low-data scenarios.

cs.CL↗

arXiv2022

A Knowledge-Enhanced Adversarial Model for Cross-lingual Structured Sentiment Analysis

Structured sentiment analysis, which aims to extract the complex semantic structures such as holders, expressions, targets, and polarities, has obtained widespread attention from both industry and academia. Unfortunately, the existing structured sentiment analysis datasets refer to a few languages and are relatively small, limiting neural network models' performance. In this paper, we focus on the cross-lingual structured sentiment analysis task, which aims to transfer the knowledge from the source language to the target one. Notably, we propose a Knowledge-Enhanced Adversarial Model (\texttt{KEAM}) with both implicit distributed and explicit structural knowledge to enhance the cross-lingual transfer. First, we design an adversarial embedding adapter for learning an informative and robust representation by capturing implicit semantic information from diverse multi-lingual embeddings adaptively. Then, we propose a syntax GCN encoder to transfer the explicit semantic information (e.g., universal dependency tree) among multiple languages. We conduct experiments on five datasets and compare \texttt{KEAM} with both the supervised and unsupervised methods. The extensive experimental results show that our \texttt{KEAM} model outperforms all the unsupervised baselines in various metrics.

cs.CL↗

arXiv2022

Enhancing Event-Level Sentiment Analysis with Structured Arguments

Previous studies about event-level sentiment analysis (SA) usually model the event as a topic, a category or target terms, while the structured arguments (e.g., subject, object, time and location) that have potential effects on the sentiment are not well studied. In this paper, we redefine the task as structured event-level SA and propose an End-to-End Event-level Sentiment Analysis ($\textit{E}^{3}\textit{SA}$) approach to solve this issue. Specifically, we explicitly extract and model the event structure information for enhancing event-level SA. Extensive experiments demonstrate the great advantages of our proposed approach over the state-of-the-art methods. Noting the lack of the dataset, we also release a large-scale real-world dataset with event arguments and sentiment labelling for promoting more researches\footnote{The dataset is available at https://github.com/zhangqi-here/E3SA}.

cs.CL↗

arXiv2022

PoKE: A Prompt-based Knowledge Eliciting Approach for Event Argument Extraction

Eliciting knowledge from pre-trained language models via prompt-based learning has shown great potential in many natural language processing tasks. Whereas, the applications for more complex tasks such as event extraction are less studied since the design of prompt is not straightforward for the structured event containing various triggers and arguments. % Meanwhile, current conditional generation methods employ large encoder-decoder models, which are costly to train and serve. In this paper, we present a novel prompt-based approach, which elicits both the independent and joint knowledge about different events for event argument extraction. The experimental results on the benchmark ACE2005 dataset show the great advantages of our proposed approach. In particular, our approach is superior to the recent advanced methods in both fully-supervised and low-resource scenarios.

cs.CL↗

arXiv2022

Meta-Weight Graph Neural Network: Push the Limits Beyond Global Homophily

Graph Neural Networks (GNNs) show strong expressive power on graph data mining, by aggregating information from neighbors and using the integrated representation in the downstream tasks. The same aggregation methods and parameters for each node in a graph are used to enable the GNNs to utilize the homophily relational data. However, not all graphs are homophilic, even in the same graph, the distributions may vary significantly. Using the same convolution over all nodes may lead to the ignorance of various graph patterns. Furthermore, many existing GNNs integrate node features and structure identically, which ignores the distributions of nodes and further limits the expressive power of GNNs. To solve these problems, we propose Meta Weight Graph Neural Network (MWGNN) to adaptively construct graph convolution layers for different nodes. First, we model the Node Local Distribution (NLD) from node feature, topological structure and positional identity aspects with the Meta-Weight. Then, based on the Meta-Weight, we generate the adaptive graph convolutions to perform a node-specific weighted aggregation and boost the node representations. Finally, we design extensive experiments on real-world and synthetic benchmarks to evaluate the effectiveness of MWGNN. These experiments show the excellent expressive power of MWGNN in dealing with graph data with various distributions.

cs.LG↗

arXiv2021

Reasoning Chain Based Adversarial Attack for Multi-hop Question Answering

Recent years have witnessed impressive advances in challenging multi-hop QA tasks. However, these QA models may fail when faced with some disturbance in the input text and their interpretability for conducting multi-hop reasoning remains uncertain. Previous adversarial attack works usually edit the whole question sentence, which has limited effect on testing the entity-based multi-hop inference ability. In this paper, we propose a multi-hop reasoning chain based adversarial attack method. We formulate the multi-hop reasoning chains starting from the query entity to the answer entity in the constructed graph, which allows us to align the question to each reasoning hop and thus attack any hop. We categorize the questions into different reasoning types and adversarially modify part of the question corresponding to the selected reasoning hop to generate the distracting sentence. We test our adversarial scheme on three QA models on HotpotQA dataset. The results demonstrate significant performance reduction on both answer and supporting facts prediction, verifying the effectiveness of our reasoning chain based attack method for multi-hop reasoning models and the vulnerability of them. Our adversarial re-training further improves the performance and robustness of these models.

cs.CL↗

arXiv2021

Human-In-The-Loop Document Layout Analysis

Document layout analysis (DLA) aims to divide a document image into different types of regions. DLA plays an important role in the document content understanding and information extraction systems. Exploring a method that can use less data for effective training contributes to the development of DLA. We consider a Human-in-the-loop (HITL) collaborative intelligence in the DLA. Our approach was inspired by the fact that the HITL push the model to learn from the unknown problems by adding a small amount of data based on knowledge. The HITL select key samples by using confidence. However, using confidence to find key samples is not suitable for DLA tasks. We propose the Key Samples Selection (KSS) method to find key samples in high-level tasks (semantic segmentation) more accurately through agent collaboration, effectively reducing costs. Once selected, these key samples are passed to human beings for active labeling, then the model will be updated with the labeled samples. Hence, we revisited the learning system from reinforcement learning and designed a sample-based agent update strategy, which effectively improves the agent's ability to accept new samples. It achieves significant improvement results in two benchmarks (DSSE-200 (from 77.1% to 86.3%) and CS-150 (from 88.0% to 95.6%)) by using 10% of labeled data.

cs.CV↗

arXiv2021

Large Magnetoresistance and Nontrivial Berry Phase in Nb3Sb Crystals with A15 Structure

Compounds with the A15 structure have attracted extensive attention due to their superconductivity and nontrivial topological band structure. We have successfully grown Nb$_3$Sb single crystals with a A15 structure and systematically measured the longitudinal resistivity, Hall resistivity and quantum oscillations in magnetization. Similar to other topological trivial/nontrivial semimetals, Nb$_3$Sb, exhibits large magnetoresistance (MR) at low temperatures (717$\%$, 2 K and 9 T), unsaturating quadratic field dependence of MR and up-turn behavior in $ρ_{xx}$(\emph{T}) curves under magnetic field, which is considered to result from a perfect hole-electron compensation, as evidenced by the Hall resistivity measurements. The nonzero Berry phase obtained from the de-Hass van Alphen (dHvA) oscillations demonstrates that Nb$_3$Sb is topologically nontrivial. These results indicate that Nb$_{3}$Sb superconductor is also a semimetal with large MR and nontrivial Berry phase, indicating that Nb$_{3}$Sb may be another platform to search for Majorana zero-energy mode.

cond-mat.supr-con↗

arXiv2021

Extremely large magnetoresistance in the "ordinary" metal ReO3

The extremely large magnetoresistance (XMR) observed in many topologically nontrivial and trivial semimetals has attracted much attention in relation to its underlying physical mechanism. In this paper, by combining the band structure and Fermi surface (FS) calculations with the Hall resistivity and de Haas-Van Alphen (dHvA) oscillation measurements, we studied the anisotropy of magnetoresistance (MR) of ReO$_3$ with a simple cubic structure, an "ordinary" nonmagnetic metal considered previously. We found that ReO$_3$ exhibits almost all the characteristics of XMR semimetals: the nearly quadratic field dependence of MR, a field-induced upturn in resistivity followed by a plateau at low temperatures, high mobilities of charge carriers. It was found that for magnetic field \emph{H} applied along the \emph{c} axis, the MR exhibits an unsaturated \emph{H}$^{1.75}$ dependence, which was argued to arise from the complete carrier compensation supported by the Hall resistivity measurements. For \emph{H} applied along the direction of 15$^\circ$ relative to the \emph{c} axis, an unsaturated \emph{H}$^{1.90}$ dependence of MR up to 9.43~$\times$~$10^3$$\%$ at 10~K and 9~T was observed, which was explained by the existence of electron open orbits extending along the $k_{x}$ direction. Two mechanisms responsible for XMR observed usually in the semimetals occur also in the simple metal ReO$_3$ due to its peculiar FS (two closed electron pockets and one open electron pocket), once again indicating that the details of FS topology are a key factor for the observed XMR in materials.

cond-mat.str-el↗

arXiv2021

Full-color nanorouter for high-resolution imaging

Pixel scaling effects have been a major issue for the development of high-resolution color image sensors due to the reduced photoelectric signal and the color crosstalk. Various structural color techniques have been proposed and demonstrated the large freedom in color manipulation by the structure design. However, the optical efficiency and the color distortion limit the practical applications due to its intrinsic filtering mechanism. Instead, on-chip full-color routing is quite charming for improving the signal-to-noise ratio. In this paper, a single-layer quick response code like nanorouter is proposed for the full-color light routing in a pixel level of image sensors. It shows much higher routing efficiency than various planar lens schemes for signal wavelength focusing. Moreover, over 60% signal enhancement with robust polarization insensitivity is obtained in all three primary color bands with a same nanorouter by a multi-objective optimization method. Negligible color distortion is observed from the reconstructed color image. Such a simple nanorouter structure is promising for the development of image sensor, photovoltaics and display.

physics.optics↗

arXiv2020

Magnetoresistance and Kondo effect in the nodal-line semimetal VAs$_2$

We performed calculations of the electronic band structure and the Fermi surface as well as measured the longitudinal resistivity $ρ_{xx}(T,H)$, Hall resistivity $ρ_{xy}(T,H)$, and magnetic susceptibility as a function of temperature and various magnetic fields for VAs$_2$ with a monoclinic crystal structure. The band structure calculations show that VAs$_2$ is a nodal-line semimetal when spin-orbit coupling is ignored. The emergence of a minimum at around 11 K in $ρ_{xx}(T)$ measured at $H$ = 0 demonstrates that an additional magnetic impurity (V$^{4+}$, $S$ = 1/2) occurs in VAs$_2$ single crystals, evidenced by both the fitting of $ρ_{xx}(T)$ data and the susceptibility measurements. It was found that a large positive magnetoresistance (MR) reaching 649\% at 10 K and 9 T, its nearly quadratic field dependence, and a field-induced up-turn behavior of $ρ_{xx}(T)$ emerge also in VAs$_2$, although MR is not so large due to the existence of additional scattering compared with other topological nontrival/trival semimetals. The observed properties are attributed to a perfect charge-carrier compensation, which is evidenced by both calculations relying on the Fermi surface and the Hall resistivity measurements. These results indicate that the compounds containing V ($3d^3 4s^2$) element as a platform for studying the influence of magnetic impurities to the topological properties.

cond-mat.str-el↗

arXiv2020

Bulk Superconductivity in the Dirac Semimetal TlSb

A feasible strategy to realize the Majorana fermions is searching for a simple compound with both bulk superconductivity and Dirac surface states. In this paper, we performed calculations of electronic band structure, the Fermi surface and surface states, as well as measured the resistivity, magnetization, specific heat for TlSb compound with a CsCl-type structure. The band structure calculations show that TlSb is a Dirac semimetal when spin-orbit coupling is taken into account. Meanwhile, we first found that TlSb is a type-II superconductor with $T_c$ = 4.38 K, $H_{c1}$(0) = 148 Oe, $H_{c2}$(0) = 1.12 T and $κ_{GL}$ = 10.6, and confirmed it to be a moderately coupled s-wave superconductor. Although we can not determine which bands near the Fermi level $E_F$ to be responsible for superconductivity, its coexistence with the topological surface states implies that TlSb compound may be a simple material platform to realize the fault-tolerant quantum computations.

cond-mat.supr-con↗

arXiv2020

Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement

We propose a new matching-based framework for semi-supervised video object segmentation (VOS). Recently, state-of-the-art VOS performance has been achieved by matching-based algorithms, in which feature banks are created to store features for region matching and classification. However, how to effectively organize information in the continuously growing feature bank remains under-explored, and this leads to inefficient design of the bank. We introduce an adaptive feature bank update scheme to dynamically absorb new features and discard obsolete features. We also design a new confidence loss and a fine-grained segmentation module to enhance the segmentation accuracy in uncertain regions. On public benchmarks, our algorithm outperforms existing state-of-the-arts.

cs.CV↗

arXiv2020

Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition

Knowledge of a disease includes information of various aspects of the disease, such as signs and symptoms, diagnosis and treatment. This disease knowledge is critical for many health-related and biomedical tasks, including consumer health question answering, medical language inference and disease name recognition. While pre-trained language models like BERT have shown success in capturing syntactic, semantic, and world knowledge from text, we find they can be further complemented by specific information like knowledge of symptoms, diagnoses, treatments, and other disease aspects. Hence, we integrate BERT with disease knowledge for improving these important tasks. Specifically, we propose a new disease knowledge infusion training procedure and evaluate it on a suite of BERT models including BERT, BioBERT, SciBERT, ClinicalBERT, BlueBERT, and ALBERT. Experiments over the three tasks show that these models can be enhanced in nearly all cases, demonstrating the viability of disease knowledge infusion. For example, accuracy of BioBERT on consumer health question answering is improved from 68.29% to 72.09%, while new SOTA results are observed in two datasets. We make our data and code freely available.

cs.CL↗

arXiv2020

Linear and quadratic magnetoresistance in the semimetal SiP2

Multiple mechanisms for extremely large magnetoresistance (XMR) found in many topologically nontrivial/trivial semimetals have been theoretically proposed, but experimentally it is unclear which mechanism is responsible in a particular sample. In this article, by the combination of band structure calculations, numerical simulations of magnetoresistance (MR), Hall resistivity and de Haas-van Alphen (dHvA) oscillation measurements, we studied the MR anisotropy of SiP$_{2}$ which is verified to be a topologically trivial, incomplete compensation semimetal. It was found that as magnetic field, $H$, is applied along the $a$ axis, the MR exhibits an unsaturated nearly linear $H$ dependence, which was argued to arise from incomplete carriers compensation. For the $H$ $\parallel$ [101] orientation, an unsaturated nearly quadratic $H$ dependence of MR up to 5.88 $\times$ 10$^{4}$$\%$ (at 1.8 K, 31.2 T) and field-induced up-turn behavior in resistivity were observed, which was suggested due to the existence of hole open orbits extending along the $k_{x}$ direction. Good agreement of the experimental results with the simulations based on the calculated Fermi surface (FS) indicates that the topology of FS plays an important role in its MR.

cond-mat.mtrl-sci↗

arXiv2020

Large magnetoresistance and non-zero Berry phase in the nodal-line semimetal MoO2

We performed calculations of the electronic band structure and the Fermi surface as well as measured the longitudinal resistivity rhoxx(T,H), Hall resistivity rhoxy(T,H) and quantum oscillations of the magnetization as a function of temperature at various magnetic fields for MoO2 with monoclinic crystal structure. The band structure calculations show that MoO2 is a nodal-line semimetal when spin-orbit coupling is ignored. It was found that a large magnetoresistance reaching 5.03x10^4% at 2 K and 9 T, its nearly quadratic field dependence and a field-induced up-turn behavior of rhoxx(T), the characteristics common for many topologically non-trivial as well as trivial semimetals, emerge also in MoO2. The observed properties are attributed to a perfect charge-carrier compensation, evidenced by both calculations relying on the Fermi surface topology and the Hall resistivity measurements. Both the observation of negative magnetoresistance for magnetic field along the current direction and the non-zero Berry phase in de Haas-van Alphen measurements indicate that pairs of Weyl points appear in MoO2, which may be due to the crystal symmetry breaking. These results highlight MoO2 as a new platform materials for studying the topological properties of oxides.

cond-mat.mtrl-sci↗

arXiv2018

Extremely large magnetoresistance in topologically trivial semimetal $α$-WP$_2$

Extremely large magnetoresistance (XMR) was recently discovered in many non-magnetic materials, while its underlying mechanism remains poorly understood due to the complex electronic structure of these materials. Here, we report an investigation of the $α$-phase WP$_2$, a topologically trivial semimetal with monoclinic crystal structure (C2/m), which contrasts to the recently discovered robust type-II Weyl semimetal phase in $β$-WP$_2$. We found that $α$-WP$_2$ exhibits almost all the characteristics of XMR materials: the near-quadratic field dependence of MR, a field-induced up-turn in resistivity following by a plateau at low temperature, which can be understood by the compensation effect, and high mobility of carriers confirmed by our Hall effect measurements. It was also found that the normalized MRs under different magnetic fields has the same temperature dependence in $α$-WP$_2$, the Kohler scaling law can describe the MR data in a wide temperature range, and there is no obvious change in the anisotropic parameter $γ$ value with temperature. The resistance polar diagram has a peanut shape when field is rotated in $\textit{ac}$ plane, which can be understood by the anisotropy of Fermi surface. These results indicate that both field-induced-gap and temperature-induced Lifshitz transition are not the origin of up-turn in resistivity in the $α$-WP$_2$ semimetal. Our findings establish $α$-WP$_2$ as a new reference material for exploring the XMR phenomena.

cond-mat.str-el↗