Reviewerly/exHarmony huggingface dataset:
Refinements in Motion and Appearance for Online Multi-Object Tracking

Modern multi-object tracking (MOT) system usually involves separated modules, such as motion model for location and appearance model for data association. However, the compatible problems within both motion and appearance models are always ignored. In this paper, a general architecture named as MIF is presented by seamlessly blending the Motion integration, three-dimensional(3D) Integral image and adaptive appearance feature Fusion. Since the uncertain pedestrian and camera motions are usually handled separately, the integrated motion model is designed using our defined intension of camera motion. Specifically, a 3D integral image based spatial blocking method is presented to efficiently cut useless connections between trajectories and candidates with spatial constraints. Then the appearance model and visibility prediction are jointly built. Considering scale, pose and visibility, the appearance features are adaptively fused to overcome the feature misalignment problem. Our MIF based tracker (MIFT) achieves the state-of-the-art accuracy with 60.1 MOTA on both MOT16&17 challenges.
Automatically Detecting Reduced-formed English Pronunciations by Using Deep Learning

Reduced form pronunciations are widely used by native English speakers, especially in casual conversations. Second language (L2) learners have difficulty in processing reduced form pronunciations in listening comprehension and face challenges in production too. Meanwhile, training applications dedicated to reduced forms are still few. To solve this issue, we report on our first effort of using deep learning to evaluate L2 learners’ reduced form pronunciations. Compared with a baseline solution that uses an ASR to determine regular or reduced-formed pronunciations, a classifier that learns representative features via a convolution neural network (CNN) on low-level acoustic features, yields higher detection performance. F-1 metric has been increased from $0.690$ to $0.757$ on the reduction task. Furthermore, adding word entities to compute attention weights to better adjust the features learned by the CNN model helps increasing F-1 to $0.763$.
Narrative Detection and Feature Analysis in Online Health Communities

Narratives have been shown to be an effective way to communicate health risks and promote health behavior change, and given the growing amount of health information being shared on social media, it is crucial to study healthrelated narratives in social media.However, expert identification of a large number of narrative texts is a time consuming process, and larger scale studies on the use of narratives may be enabled through automatic text classification approaches.Prior work has demonstrated that automatic narrative detection is possible, but modern deep learning approaches have not been used for this task in the domain of online health communities.Therefore, in this paper, we explore the use of deep learning methods to automatically classify the presence of narratives in social media posts, finding that they outperform previously proposed approaches.We also find that in many cases, these models generalize well across posts from different health organizations.Finally, in order to better understand the increase in performance achieved by deep learning models, we use feature analysis techniques to explore the features that most contribute to narrative detection for posts in online health communities.
An exploratory data analysis: the performance differences of a medical code prediction system on different demographic groups

Recent studies show that neural natural processing models for medical code prediction suffer from a label imbalance issue.This study aims to investigate further imbalance in a medical code prediction dataset in terms of demographic variables and analyse performance differences in demographic groups.We use sample-based metrics to correctly evaluate the performance in terms of the data subject.Also, a simple label distance metric is proposed to quantify the difference in the label distribution between a group and the entire data.Our analysis results reveal that the model performs differently towards different demographic groups: significant differences between age groups and between insurance types are observed.Interestingly, we found a weak positive correlation between the number of training data of the group and the performance of the group.However, a strong negative correlation between the label distance of the group and the performance of the group is observed.This result suggests that the model tends to perform poorly in the group whose label distribution is different from the global label distribution of the training data set.Further analysis of the model performance is required to identify the cause of these differences and to improve the model building.
ECNU_ICA at SemEval-2022 Task 10: A Simple and Unified Model for Monolingual and Crosslingual Structured Sentiment Analysis

In this paper, we focus on the structured sentiment analysis task that is released on SemEval-2022 Task 10.The task aims to extract the structured sentiment information (e.g., holder, target, expression and sentiment polarity) in a text.We propose a simple and unified model for both the monolingual and crosslingual structured sentiment analysis tasks.We translate this task into an event extraction task by regarding the expression as the trigger word and the other elements as the arguments of the event.Particularly, we first extract the expression by judging its start and end indices.Then, to consider the expression, we design a conditional layer normalization algorithm to extract the holder and target based on the extracted expression.Finally, we infer the sentiment polarity based on the extracted structured information.We conduct the experiments on seven datasets in five languages.It attracted 233 submissions in monolingual subtask and crosslingual subtask from 32 teams.Finally, we obtain the top 5 place on crosslingual tasks.
Gated Mechanism for Attention Based Multimodal Sentiment Analysis

Multimodal sentiment analysis has recently gained popularity because of its relevance to social media posts, customer service calls and video blogs. In this paper, we address three aspects of multimodal sentiment analysis; 1. Cross modal interaction learning, i.e. how multiple modalities contribute to the sentiment, 2. Learning long-term dependencies in multimodal interactions and 3. Fusion of unimodal and cross modal cues. Out of these three, we find that learning cross modal interactions is beneficial for this problem. We perform experiments on two benchmark datasets, CMU Multimodal Opinion level Sentiment Intensity (CMU-MOSI) and CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) corpus. Our approach on both these tasks yields accuracies of 83.9% and 81.1% respectively, which is 1.6% and 1.34% absolute improvement over current state-of-the-art.
ResiliNet: Failure-Resilient Inference in Distributed Neural Networks

Federated Learning aims to train distributed deep models without sharing the raw data with the centralized server. Similarly, in distributed inference of neural networks, by partitioning the network and distributing it across several physical nodes, activations and gradients are exchanged between physical nodes, rather than raw data. Nevertheless, when a neural network is partitioned and distributed among physical nodes, failure of physical nodes causes the failure of the neural units that are placed on those nodes, which results in a significant performance drop. Current approaches focus on resiliency of training in distributed neural networks. However, resiliency of inference in distributed neural networks is less explored. We introduce ResiliNet, a scheme for making inference in distributed neural networks resilient to physical node failures. ResiliNet combines two concepts to provide resiliency: skip hyperconnection, a concept for skipping nodes in distributed neural networks similar to skip connection in resnets, and a novel technique called failout, which is introduced in this paper. Failout simulates physical node failure conditions during training using dropout, and is specifically designed to improve the resiliency of distributed neural networks. The results of the experiments and ablation studies using three datasets confirm the ability of ResiliNet to provide inference resiliency for distributed neural networks.
Generating diverse and natural text-to-speech samples using a quantized  fine-grained VAE and auto-regressive prosody prior

Recent neural text-to-speech (TTS) models with fine-grained latent features enable precise control of the prosody of synthesized speech. Such models typically incorporate a fine-grained variational autoencoder (VAE) structure, extracting latent features at each input token (e.g., phonemes). However, generating samples with the standard VAE prior often results in unnatural and discontinuous speech, with dramatic prosodic variation between tokens. This paper proposes a sequential prior in a discrete latent space which can generate more naturally sounding samples. This is accomplished by discretizing the latent features using vector quantization (VQ), and separately training an autoregressive (AR) prior model over the result. We evaluate the approach using listening tests, objective metrics of automatic speech recognition (ASR) performance, and measurements of prosody attributes. Experimental results show that the proposed model significantly improves the naturalness in random sample generation. Furthermore, initial experiments demonstrate that randomly sampling from the proposed model can be used as data augmentation to improve the ASR performance.
Quantum approximate algorithm for NP optimization problems with  constraints

The Quantum Approximate Optimization Algorithm (QAOA) is an algorithmic framework for finding approximate solutions to combinatorial optimization problems, derived from an approximation to the Quantum Adiabatic Algorithm (QAA). In solving combinatorial optimization problems with constraints in the context of QAOA or QAA, one needs to find a way to encode problem constraints into the scheme. In this paper, we formalize different constraint types to linear equalities, linear inequalities, and arbitrary form. Based on this, we propose constraint-encoding schemes well-fitting into the QAOA framework for solving NP combinatorial optimization problems. The implemented algorithms demonstrate the effectiveness and efficiency of the proposed scheme by the testing results of varied instances of some well-known NP optimization problems. We argue that our work leads to a generalized framework for finding, in the context of QAOA, high-quality approximate solutions to combinatorial problems with various types of constraints.
All Information is Valuable: Question Matching over Full Information Transmission Network

Question matching is the task of identifying whether two questions have the same intent.For better reasoning the relationship between questions, existing studies adopt multiple interaction modules and perform multi-round reasoning via deep neural networks.In this process, there are two kinds of critical information that are commonly employed: the representation information of original questions and the interactive information between pairs of questions.However, previous studies tend to transmit only one kind of information, while failing to utilize both kinds of information simultaneously.To address this problem, in this paper, we propose a Full Information Transmission Network (FITN) that can transmit both representation and interactive information together in a simultaneous fashion.More specifically, we employ a novel memory-based attention for keeping and transmitting the interactive information through a global interaction matrix.Besides, we apply an original-average mixed connection method to effectively transmit the representation information between different reasoning rounds, which helps to preserve the original representation features of questions along with the historical hidden features.Experiments on two standard benchmarks demonstrate that our approach outperforms strong baseline models.
Identifying Human Needs through Social Media: A study on Indian cities during COVID-19

In this paper, we present a minimallysupervised approach to identify human needs expressed in tweets.Taking inspiration from Frustration-Aggression theory, we trained RoBERTa model to classify tweets expressing frustration which serves as an indicator of unmet needs.Although the notion of frustration is highly subjective and complex, the findings support the use of pretrained language model in identifying tweets with unmet needs.Our study reveals the major causes behind feeling frustrated during the lockdown and the second wave of the COVID-19 pandemic in India.Our proposed approach can be useful in timely identification and prioritization of emerging human needs in the event of a crisis.
Chilean Perspectives on Educational Experiences and Innovations in Emergency Contexts

Abstract Emergency remote teaching began suddenly and widely by March 2020 after the World Health Organization declared the coronavirus pandemics Chilean education, as many other countries around the globe, adopted the methodology of remote teaching with little no previous experience and a lack of adequate and pertinent administrator, teacher, student, and parent preparation. This chapter presents, analyzes, and discusses the Chilean higher education experiences and innovations in emergency contexts during the COVID-19 pandemic. Actual and future perspectives are discussed on the basis of education in a country with frequent emergency contexts such as earthquakes, tsunamis, floods, fires and now a pandemic. We applied a systematic review research method to the higher education data and information that emerged during the coronavirus pandemic in Chile during the last year. As a result, we ended up drawing some trends, issues, and perspectives on Chilean higher education teaching and learning activities and innovation in emergency contexts. Finally, considering the results obtained, we discuss the main themes that emerged, and some lessons learned by Chilean education in times of pandemic, in order to be better prepared and equipped to face emergency situations with less disruption and detriment to teaching and learning in the future.
Dynamic Multistep Reasoning based on Video Scene Graph for Video Question Answering

Existing video question answering (video QA) models lack the capacity for deep video understanding and flexible multistep reasoning.We propose for video QA a novel model which performs dynamic multistep reasoning between questions and videos.It creates video semantic representation based on the video scene graph composed of semantic elements of the video and semantic relations among these elements.Then, it performs multistep reasoning for better answer decision between the representations of the question and the video, and dynamically integrate the reasoning results.Experiments show the significant advantage of the proposed model against previous methods in accuracy and interpretability.Against the existing stateof-the-art model, the proposed model dramatically improves more than 4%/3.1%/2% on the three widely used video QA datasets, MSRVTT-QA, MSRVTT multi-choice, and TGIF-QA, and displays better interpretability by backtracing along with the attention mechanisms to the video scene graphs.* This is joint work of CASICT and Baidu.
The Performance Optimization of Big Data Processing by Adaptive MapReduce Workflow

The discussion context of this paper is big data processing of MapReduce by volunteer computing in dynamic and opportunistic environments. This paper conducts a series of simulations to explore the relationship between the overall performance of volunteer overlays responding to different workload of big data problems. The discovery from the simulations includes some optimization points in overlay size, going over which by adding more volunteers brings little benefit for the overall performance. Based on the discovery of optimization points, this paper proposes a bootstrapping protocol, which can adapt volunteers into variable-sizes overlays, enabling workflow of single-round MapReduce or multiple-round MapReduce, and a single or multiple overlays for each round. The variable overlays aim to create adaptive workflow during MapReduce processing, so that the optimization points can be caught. As another benefit, the unnecessary computing-capacities can be released during computing when the optimization points are reached. The case study shows a few optimization workflows that are formed by the proposed bootstrapping protocol to process the big data cases. The workflows lead to the optimization points and dynamically balance the workload at the same time. The experiment results have demonstrated that the optimization strategies have either achieved 36% or 71% higher performance than the plain MapReduce workflow and minimized the use of computing resources by releasing 12.5% to 75% volunteers during computing, where the original plain MapReduce must hold all the volunteers to the end of computing. The extensibility of the simulation parameterization to more diverse real-world applications have been clarified.
Self-supervised Product Title Rewrite for Product Listing Ads

Xue Zhao, Dayiheng Liu, Junwei Ding, Liang Yao, Mahone Yan, Huibo Wang, Wenqing Yao. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track. 2022.
GateNLP-UShef at SemEval-2022 Task 8: Entity-Enriched Siamese Transformer for Multilingual News Article Similarity

This paper describes the second-placed system on the leaderboard of SemEval-2022 Task 8: Multilingual News Article Similarity.We propose an entity-enriched Siamese Transformer which computes news article similarity based on different sub-dimensions, such as the shared narrative, entities, location and time of the event discussed in the news article.Our system exploits a Siamese network architecture using a Transformer encoder to learn document-level representations for the purpose of capturing the narrative together with the auxiliary entitybased features extracted from the news articles.The intuition behind using all these features together is to capture the similarity between news articles at different granularity levels and to assess the extent to which different news outlets write about "the same events".Our experimental results and detailed ablation study demonstrate the effectiveness and the validity of our proposed method.
LingJing at SemEval-2022 Task 3: Applying DeBERTa to Lexical-level Presupposed Relation Taxonomy with Knowledge Transfer

This paper presents the results and main findings of our system on SemEval-2022 Task 3 Presupposed Taxonomies: Evaluating Neural Network Semantics (PreTENS) 1 .This task aims at semantic competence with specific attention on the evaluation of language models, which is a task with respect to the recognition of appropriate taxonomic relations between two nominal arguments.Two sub-tasks including binary classification and regression are designed for the evaluation.For the classification sub-task, we adopt the DeBERTa-v3 pre-trained model for fine-tuning datasets of different languages.Due to the small size of the training datasets of the regression subtask, we transfer the knowledge of classification model (i.e., model parameters) to the regression task.The experimental results show that the proposed method achieves the best results on both sub-tasks.Meanwhile, we also report negative results of multiple training strategies for further discussion.
Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching

Previous studies have proved that cross-lingual knowledge distillation can significantly improve the performance of pre-trained models for cross-lingual similarity matching tasks.However, the student model needs to be large in this operation.Otherwise, its performance will drop sharply, thus making it impractical to be deployed to memory-limited devices.To address this issue, we delve into cross-lingual knowledge distillation and propose a multistage distillation framework for constructing a small-size but high-performance cross-lingual model.In our framework, contrastive learning, bottleneck, and parameter recurrent strategies are combined to prevent performance from being compromised during the compression process.The experimental results demonstrate that our method can compress the size of XLM-R and MiniLM by more than 50%, while the performance is only reduced by about 1%.
A Type and Scope Safe Universe of Syntaxes with Binding: Their Semantics and Proofs

Almost every programming language's syntax includes a notion of binder and corresponding bound occurrences, along with the accompanying notions of $\alpha$-equivalence, capture-avoiding substitution, typing contexts, runtime environments, and so on. In the past, implementing and reasoning about programming languages required careful handling to maintain the correct behaviour of bound variables. Modern programming languages include features that enable constraints like scope safety to be expressed in types. Nevertheless, the programmer is still forced to write the same boilerplate over again for each new implementation of a scope safe operation (e.g., renaming, substitution, desugaring, printing, etc.), and then again for correctness proofs. We present an expressive universe of syntaxes with binding and demonstrate how to (1) implement scope safe traversals once and for all by generic programming; and (2) how to derive properties of these traversals by generic proving. Our universe description, generic traversals and proofs, and our examples have all been formalised in Agda and are available in the accompanying material available online at https://github.com/gallais/generic-syntax.
Learning To Detect Keyword Parts And Whole By Smoothed Max Pooling

We propose smoothed max pooling loss and its application to keyword spotting systems. The proposed approach jointly trains an encoder (to detect keyword parts) and a decoder (to detect whole keyword) in a semi-supervised manner. The proposed new loss function allows training a model to detect parts and whole of a keyword, without strictly depending on frame-level labeling from LVCSR (Large vocabulary continuous speech recognition), making further optimization possible. The proposed system outperforms the baseline keyword spotting model in [1] due to increased optimizability. Further, it can be more easily adapted for on-device learning applications due to reduced dependency on LVCSR.
Concurrently Extrapolating and Interpolating Networks for Continuous  Model Generation

Most deep image smoothing operators are always trained repetitively when different explicit structure-texture pairs are employed as label images for each algorithm configured with different parameters. This kind of training strategy often takes a long time and spends equipment resources in a costly manner. To address this challenging issue, we generalize continuous network interpolation as a more powerful model generation tool, and then propose a simple yet effective model generation strategy to form a sequence of models that only requires a set of specific-effect label images. To precisely learn image smoothing operators, we present a double-state aggregation (DSA) module, which can be easily inserted into most of current network architecture. Based on this module, we design a double-state aggregation neural network structure with a local feature aggregation block and a nonlocal feature aggregation block to obtain operators with large expression capacity. Through the evaluation of many objective and visual experimental results, we show that the proposed method is capable of producing a series of continuous models and achieves better performance than that of several state-of-the-art methods for image smoothing.