arXiv subjects

Chenyang Huang

Publications and source records attributed to Chenyang Huang.

33 records · Page 2Linked to original sources

arXiv2022

Switch as a Verifier: Toward Scalable Data Plane Checking via Distributed, On-Device Verification

Data plane verification (DPV) is important for finding network errors. Current DPV tools employ a centralized architecture, where a server collects the data planes of all devices and verifies them. Despite substantial efforts on accelerating DPV, this centralized architecture is inherently unscalable. In this paper, to tackle the scalability challenge of DPV, we circumvent the scalability bottleneck of centralized design and design Coral, a distributed, on-device DPV framework. The key insight of Coral is that DPV can be transformed into a counting problem on a directed acyclic graph, which can be naturally decomposed into lightweight tasks executed at network devices, enabling scalability. Coral consists of (1) a declarative requirement specification language, (2) a planner that employs a novel data structure DVNet to systematically decompose global verification into on-device counting tasks, and (3) a distributed verification (DV) protocol that specifies how on-device verifiers communicate task results efficiently to collaboratively verify the requirements. We implement a prototype of Coral. Extensive experiments with real-world datasets (WAN/LAN/DC) show that Coral consistently achieves scalable DPV under various networks and DPV scenarios, i.e., up to 1250 times speed up in the scenario of burst update, and up to 202 times speed up on 80% quantile of incremental verification, than state-of-the-art DPV tools, with little overhead on commodity network devices.

eess.SY↗

arXiv2022

Learning Non-Autoregressive Models from Search for Unsupervised Sentence Summarization

Text summarization aims to generate a short summary for an input text. In this work, we propose a Non-Autoregressive Unsupervised Summarization (NAUS) approach, which does not require parallel data for training. Our NAUS first performs edit-based search towards a heuristically defined score, and generates a summary as pseudo-groundtruth. Then, we train an encoder-only non-autoregressive Transformer based on the search result. We also propose a dynamic programming approach for length-control decoding, which is important for the summarization task. Experiments on two datasets show that NAUS achieves state-of-the-art performance for unsupervised summarization, yet largely improving inference efficiency. Further, our algorithm is able to perform explicit length-transfer summary generation.

cs.CL↗

arXiv2022

U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection

In this paper, we design a simple yet powerful deep network architecture, U$^2$-Net, for salient object detection (SOD). The architecture of our U$^2$-Net is a two-level nested U-structure. The design has the following advantages: (1) it is able to capture more contextual information from different scales thanks to the mixture of receptive fields of different sizes in our proposed ReSidual U-blocks (RSU), (2) it increases the depth of the whole architecture without significantly increasing the computational cost because of the pooling operations used in these RSU blocks. This architecture enables us to train a deep network from scratch without using backbones from image classification tasks. We instantiate two models of the proposed architecture, U$^2$-Net (176.3 MB, 30 FPS on GTX 1080Ti GPU) and U$^2$-Net$^{\dagger}$ (4.7 MB, 40 FPS), to facilitate the usage in different environments. Both models achieve competitive performance on six SOD datasets. The code is available: https://github.com/NathanUA/U-2-Net.

cs.CV↗

arXiv2021

Long-term Trends of Regolith Movement on the Surface of Small Bodies

This paper studies the long-term migration of disturbed regolith materials on the surface of Solar System small bodies from the viewpoint of nonlinear dynamics. We propose an approximation model for secular mass movement, which combines the complex topography and irregular gravitational field. Choosing asteroid 101955 Bennu as a representative, the global change of the dynamical environment is examined, which presents a division of the creeping-sliding-shedding regions for a spun-up asteroid. In the creeping region, the dynamical equation of disturbed regolith grains is established based on the assumption of "trigger-slide" motion mode. The equilibrium points, local manifolds and large-scale trajectories of the system are calculated to clarify the dynamical characteristics of long-term regolith movement. Generally, we find for a low spin rate, the surface regolith grains flow toward the middle latitudes from the polar/equatorial regions, which is dominated by the gradient of the geopotential. While spun up to a high rate, regolith grains tend to migrate toward the equator, which happens in parallel with a topological shift of the local equilibria at low latitudes. From a long-term perspective, we find the equilibrium points dominate the global trends of regolith movements. Using the methodology developed in this paper, we give a prospect or retrospect to the secular motion of regolith materials during the spin-up process, and the results reveal a significant regulatory role of the equilibria. Through a detailed look at the dynamical scheme under different spin rates, we achieve a macro forecast of the global trends of regolith motion during the spin-up process, which explains the global geologic evolution driven by the long-term movements of regolith materials.

astro-ph.EP↗

arXiv2021

Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision

How do we perform efficient inference while retaining high translation quality? Existing neural machine translation models, such as Transformer, achieve high performance, but they decode words one by one, which is inefficient. Recent non-autoregressive translation models speed up the inference, but their quality is still inferior. In this work, we propose DSLP, a highly efficient and high-performance model for machine translation. The key insight is to train a non-autoregressive Transformer with Deep Supervision and feed additional Layer-wise Predictions. We conducted extensive experiments on four translation tasks (both directions of WMT'14 EN-DE and WMT'16 EN-RO). Results show that our approach consistently improves the BLEU scores compared with respective base models. Specifically, our best variant outperforms the autoregressive model on three translation tasks, while being 14.8 times more efficient in inference.

cs.CL↗

arXiv2021

Simulated Annealing for Emotional Dialogue Systems

Explicitly modeling emotions in dialogue generation has important applications, such as building empathetic personal companions. In this study, we consider the task of expressing a specific emotion for dialogue generation. Previous approaches take the emotion as an input signal, which may be ignored during inference. We instead propose a search-based emotional dialogue system by simulated annealing (SA). Specifically, we first define a scoring function that combines contextual coherence and emotional correctness. Then, SA iteratively edits a general response and searches for a sentence with a higher score, enforcing the presence of the desired emotion. We evaluate our system on the NLPCC2017 dataset. Our proposed method shows 12% improvements in emotion accuracy compared with the previous state-of-the-art method, without hurting the generation quality (measured by BLEU).

cs.CL↗

arXiv2021

Basic and Depression Specific Emotion Identification in Tweets: Multi-label Classification Experiments

In this paper, we present empirical analysis on basic and depression specific multi-emotion mining in Tweets with the help of state of the art multi-label classifiers. We choose our basic emotions from a hybrid emotion model consisting of the common emotions from four highly regarded psychological models of emotions. Moreover, we augment that emotion model with new emotion categories because of their importance in the analysis of depression. Most of those additional emotions have not been used in previous emotion mining research. Our experimental analyses show that a cost sensitive RankSVM algorithm and a Deep Learning model are both robust, measured by both Macro F-measures and Micro F-measures. This suggests that these algorithms are superior in addressing the widely known data imbalance problem in multi-label learning. Moreover, our application of Deep Learning performs the best, giving it an edge in modeling deep semantic features of our extended emotional categories.

cs.LG↗

arXiv2021

A Globally Normalized Neural Model for Semantic Parsing

In this paper, we propose a globally normalized model for context-free grammar (CFG)-based semantic parsing. Instead of predicting a probability, our model predicts a real-valued score at each step and does not suffer from the label bias problem. Experiments show that our approach outperforms locally normalized models on small datasets, but it does not yield improvement on a large dataset.

cs.CL↗

arXiv2021

Optimizing Deeper Transformers on Small Datasets

It is a common belief that training deep transformers from scratch requires large datasets. Consequently, for small datasets, people usually use shallow and simple additional layers on top of pre-trained models during fine-tuning. This work shows that this does not always need to be the case: with proper initialization and optimization, the benefits of very deep transformers can carry over to challenging tasks with small datasets, including Text-to-SQL semantic parsing and logical reading comprehension. In particular, we successfully train $48$ layers of transformers, comprising $24$ fine-tuned layers from pre-trained RoBERTa and $24$ relation-aware layers trained from scratch. With fewer training steps and no task-specific pre-training, we obtain the state-of-the-art performance on the challenging cross-domain Text-to-SQL parsing benchmark Spider. We achieve this by deriving a novel Data-dependent Transformer Fixed-update initialization scheme (DT-Fixup), inspired by the prior T-Fixup work. Further error analysis shows that increasing depth can help improve generalization on small datasets for hard cases that require reasoning and structural understanding.

cs.CL↗

arXiv2021

Sand Creep Motion in Slow Spin-up Experiment: An Analogue of Regolith Migration on Asteroids

We studied the creep motion of granular materials in a gradient potential field that is created using a slow spin-up experiment device. Natural sand confined in the acrylic box is spun up by a controlled turntable and the surface flows are captured using video-based measurements. Various spin-up accelerations were considered to understand the responses of creep motion on different accelerating paths. Convergent behaviors in the morphological change of sand surface were observed in the final steady state. To quantify the quasi-static spin-up process, we examined the net flux and the surface slope as a function of the spin rate and offset from the rotation axis. Evolution of sand creep motion demonstrated behaviors similar to regolith migration in numeric simulations, showing intermittency like general sheared granular systems. We noticed the sand surface approaches criticality as the spin-up proceeding, consistent with the observation that top-shaped asteroids near limiting spin rate take on critical shape. Comparisons to large-scale numeric simulations and analytical solutions reveal underlying similarities between our experiments and the million-year evolution of asteroid regolith under YORP acceleration, which raises the possibility of studying asteroid surface processes in laboratory analogue experiments.

astro-ph.EP↗

arXiv2021

Boundary-Aware Segmentation Network for Mobile and Web Applications

Although deep models have greatly improved the accuracy and robustness of image segmentation, obtaining segmentation results with highly accurate boundaries and fine structures is still a challenging problem. In this paper, we propose a simple yet powerful Boundary-Aware Segmentation Network (BASNet), which comprises a predict-refine architecture and a hybrid loss, for highly accurate image segmentation. The predict-refine architecture consists of a densely supervised encoder-decoder network and a residual refinement module, which are respectively used to predict and refine a segmentation probability map. The hybrid loss is a combination of the binary cross entropy, structural similarity and intersection-over-union losses, which guide the network to learn three-level (ie, pixel-, patch- and map- level) hierarchy representations. We evaluate our BASNet on two reverse tasks including salient object segmentation, camouflaged object segmentation, showing that it achieves very competitive performance with sharp segmentation boundaries. Importantly, BASNet runs at over 70 fps on a single GPU which benefits many potential real applications. Based on BASNet, we further developed two (close to) commercial applications: AR COPY & PASTE, in which BASNet is integrated with augmented reality for "COPYING" and "PASTING" real-world objects, and OBJECT CUT, which is a web-based tool for automatic object background removal. Both applications have already drawn huge amount of attention and have important real-world impacts. The code and two applications will be publicly available at: https://github.com/NathanUA/BASNet.

cs.CV↗

arXiv2020

ANA at SemEval-2020 Task 4: mUlti-task learNIng for cOmmonsense reasoNing (UNION)

In this paper, we describe our mUlti-task learNIng for cOmmonsense reasoNing (UNION) system submitted for Task C of the SemEval2020 Task 4, which is to generate a reason explaining why a given false statement is non-sensical. However, we found in the early experiments that simple adaptations such as fine-tuning GPT2 often yield dull and non-informative generations (e.g. simple negations). In order to generate more meaningful explanations, we propose UNION, a unified end-to-end framework, to utilize several existing commonsense datasets so that it allows a model to learn more dynamics under the scope of commonsense reasoning. In order to perform model selection efficiently, accurately and promptly, we also propose a couple of auxiliary automatic evaluation metrics so that we can extensively compare the models from different perspectives. Our submitted system not only results in a good performance in the proposed metrics but also outperforms its competitors with the highest achieved score of 2.10 for human evaluation while remaining a BLEU score of 15.7. Our code is made publicly available at GitHub.

cs.CL↗

arXiv2019

Seq2Emo for Multi-label Emotion Classification Based on Latent Variable Chains Transformation

Emotion detection in text is an important task in NLP and is essential in many applications. Most of the existing methods treat this task as a problem of single-label multi-class text classification. To predict multiple emotions for one instance, most of the existing works regard it as a general Multi-label Classification (MLC) problem, where they usually either apply a manually determined threshold on the last output layer of their neural network models or train multiple binary classifiers and make predictions in the fashion of one-vs-all. However, compared to labels in the general MLC datasets, the number of emotion categories are much fewer (less than 10). Additionally, emotions tend to have more correlations with each other. For example, the human usually does not express "joy" and "anger" at the same time, but it is very likely to have "joy" and "love" expressed together. Given this intuition, in this paper, we propose a Latent Variable Chain (LVC) transformation and a tailored model -- Seq2Emo model that not only naturally predicts multiple emotion labels but also takes into consideration their correlations. We perform the experiments on the existing multi-label emotion datasets as well as on our newly collected datasets. The results show that our model compares favorably with existing state-of-the-art methods.

cs.CL↗

arXiv2019

ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT

This paper describes the system submitted by ANA Team for the SemEval-2019 Task 3: EmoContext. We propose a novel Hierarchical LSTMs for Contextual Emotion Detection (HRLCE) model. It classifies the emotion of an utterance given its conversational context. The results show that, in this task, our HRCLE outperforms the most recent state-of-the-art text classification framework: BERT. We combine the results generated by BERT and HRCLE to achieve an overall score of 0.7709 which ranked 5th on the final leader board of the competition among 165 Teams.

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arXiv2018

Generating Responses Expressing Emotion in an Open-domain Dialogue System

Neural network-based Open-ended conversational agents automatically generate responses based on predictive models learned from a large number of pairs of utterances. The generated responses are typically acceptable as a sentence but are often dull, generic, and certainly devoid of any emotion. In this paper, we present neural models that learn to express a given emotion in the generated response. We propose four models and evaluate them against 3 baselines. An encoder-decoder framework-based model with multiple attention layers provides the best overall performance in terms of expressing the required emotion. While it does not outperform other models on all emotions, it presents promising results in most cases.

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