Automated Essay Scoring (AES) is a critical text regression task that automatically assigns scores to essays based on their writing quality. Recently, the performance of sentence prediction tasks has been largely improved by using Pre-trained Language Models via fusing representations from different layers, constructing an auxiliary sentence, multi-task learning, etc. However, solve AES task, previous works utilize shallow neural networks learn essay and constrain calculated with loss or...

Yiwei Wei, Shaozu Yuan, Ruosong Yang, Lei Shen, Zhangmeizhi Li, Longbiao Wang, Meng Chen. Proceedings of the 61st Annual Meeting Association for Computational Linguistics (Volume 1: Long Papers). 2023.

To provide consistent emotional interaction with users, dialog systems should be capable to automatically select appropriate emotions for responses like humans.However, most existing works focus on rendering specified in or empathetically respond the emotion of yet individual difference expression is overlooked.This may lead inconsistent expressions and disinterest users.To tackle this issue, we propose equip system personality enable it by simulating transition humans conversation.In...

Multimodal sarcasm detection, aiming to detect the ironic sentiment within multimodal social data, has gained substantial popularity in both natural language processing and computer vision communities. Recently, graph-based studies by drawing sentimental relations have made notable advancements. However, they neglected exploiting global semantic congruity from existing instances facilitate prediction, which ultimately hinders model's performance. In this paper, we introduce a new inference...

Writing a survey paper on one research topic usually needs to cover the salient content from numerous related papers, which can be modeled as multi-document summarization (MDS) task. Existing MDS datasets focus producing structureless summary covering few input documents. Meanwhile, previous structured generation works summarizing single document into multi-section summary. These existing and methods cannot meet requirements of academic papers To deal with scarcity available data, we propose...

A microstrip low-pass filter (LPF) using reformative stepped impedance resonator (SIR) and defected ground structure (DGS) is proposed in this paper. The not only possesses the advantage of high frequency selectivity SIR hairpin LPF with internal coupling, but also large stop-band (SB) bandwidth by adjusting number area DGS units. paper properties miniaturization, wide SB, selectivity, low pass-band ripple (PBR) simultaneously. characteristic parameters that: (PB) 0~2 GHz, PBR 0.5 dB, SB...

Abstractive summarization aims to generate a concise summary covering the input document's salient information. Within report document, information can be scattered in textual and non-textual content. However, existing document datasets methods usually focus on text filter out Missing tabular data limit produced summaries' informativeness, especially when summaries require quantitative descriptions of critical metrics tables. Existing cannot meet requirements summarizing long dozens tables...

Generating appropriate emotions for responses is essential dialogue systems to provide human-like interaction in various application scenarios. Most previous tried achieve this goal by learning empathetic manners from anonymous conversational data. However, emotional generated those methods may be inconsistent, which will decrease user engagement and service quality. Psychological findings suggest that the expressions of humans are rooted personality traits. Therefore, we propose a new task,...

In recent years, mobile sensing data are widely used for analyzing human's activities, usage patterns, emotions, health conditions and social relationships. order to understand analyze behaviors, several frameworks have been proposed collect data. this paper we extend previous works design StarLog, which is a distributed energy-configurable framework both collecting analyzing. It collects fine-grained of five categories, reflecting user's locations, interactions with smart phone, contacts...

In this paper, we study about the problem of how to recognize user emotion based on smartphone data more really. With single used in previous studies, it cannot make a comprehensive response behavior patterns. So collected fine-grained sensing which could reflect daily fully from multiple dimensions smartphone, and then multidimensional feature fusion method six classification methods such as Support Vector Machine (SVM) Random Forests. Finally, carried out contrast experiment with twelve...

Abstractive multi-document summarization aims to generate a comprehensive summary covering salient content from multiple input documents.Compared with previous RNNbased models, the Transformer-based models employ self-attention mechanism capture dependencies in documents and can better summaries.Existing works have not considered key phrases determining attention weights of self-attention.Consequently, some tokens within only receive small weights.It affect completely encoding that convey...

Automatic document summarization aims to produce a concise summary covering the input document’s salient information. Within report document, information can be scattered in textual and non-textual content. However, existing datasets methods usually focus on text filter out Missing tabular data limit produced summaries’ informativeness, especially when summaries require quantitative descriptions of critical metrics tables. Existing cannot meet requirements summarizing long multiple tables...

Nowadays is the big data era. A large amount of are generated which can be valuable for business, healthcare, transportation, etc. To promote dissemination data, researchers have been trying to design and develop sharing platforms. However, existing platforms fail address at least one three issues: trustworthiness, heterogeneity, authenticability. this end, we propose TSAR, a fully-distributed Trustless ShARing platform. In detail, architect TSAR on Blockchain remove dependency reliable...

Common law courts need to refer similar precedents' judgments inform their current decisions. Generating high-quality summaries of court judgment documents can facilitate legal practitioners efficiently review previous cases and assist the general public in accessing how operate is applied. Previous summarization research focuses on civil or a particular jurisdiction's judgments. However, judges from all common jurisdictions. Current datasets are insufficient satisfy demands summarizing...

Personality Recognition in Conversation (PRC) aims to identify the personality traits of speakers through textual dialogue content. It is essential for providing personalized services various applications Human-Computer Interaction (HCI), such as AI-based mental therapy and companion robots elderly. Most recent studies analyze dialog content classification yet overlook two major concerns that hinder their performance. First, crucial implicit factors contained conversation, emotions reflect...

Personality Recognition in Conversation (PRC) aims to identify the personality traits of speakers through textual dialogue content. It is essential for providing personalized services various applications Human-Computer Interaction (HCI), such as AI-based mental therapy and companion robots elderly. Most recent studies analyze dialog content classification yet overlook two major concerns that hinder their performance. First, crucial implicit factors contained conversation, emotions reflect...

Generating appropriate emotions for responses is essential dialog systems to provide human-like interaction in various application scenarios. Most previous tried achieve this goal by learning empathetic manners from anonymous conversational data. However, emotional generated those methods may be inconsistent, which will decrease user engagement and service quality. Psychological findings suggest that the expressions of humans are rooted personality traits. Therefore, we propose a new task,...

Nowadays GPS embedded in mobile device such as smartphones can easily identify people's physical locations. However, daily life people are more concerned about semantic locations (such dormitories, laboratories, shopping malls, etc.). Usually positioning uses continuous sampling method, which results a lot of semantically independent sample points. We call these points outliers. How to remove outliers from data and thereby cluster meaningful places is research challenge current field...