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Found 22 papers, 13 papers with code
SmartBullets: A Cloud-Assisted Bullet Screen Filter based on Deep Learning
Although the bullet-screen video websites have provided filter functions based on regular expression, bad bullets can still easily pass the filter through making a small modification.
ConAML: Constrained Adversarial Machine Learning for Cyber-Physical Systems
We study the potential vulnerabilities of ML applied in CPSs by proposing Constrained Adversarial Machine Learning (ConAML), which generates adversarial examples that satisfy the intrinsic constraints of the physical systems.
SearchFromFree: Adversarial Measurements for Machine Learning-based Energy Theft Detection
Energy theft causes large economic losses to utility companies around the world.
Exploiting Vulnerabilities of Deep Learning-based Energy Theft Detection in AMI through Adversarial Attacks
In this work, we study the vulnerabilities of DL-based energy theft detection through adversarial attacks, including single-step attacks and iterative attacks.
Learning Class-Transductive Intent Representations for Zero-shot Intent Detection
A critical problem behind these limitations is that the representations of unseen intents cannot be learned in the training stage.
A Hierarchical Transformer with Speaker Modeling for Emotion Recognition in Conversation
It can be regarded as a personalized and interactive emotion recognition task, which is supposed to consider not only the semantic information of text but also the influences from speakers.
Ranked #34 on
Emotion Recognition in Conversation
on IEMOCAP
Towards Adversarial-Resilient Deep Neural Networks for False Data Injection Attack Detection in Power Grids
False data injection attacks (FDIAs) pose a significant security threat to power system state estimation.
Neutral Utterances are Also Causes: Enhancing Conversational Causal Emotion Entailment with Social Commonsense Knowledge
Conversational Causal Emotion Entailment aims to detect causal utterances for a non-neutral targeted utterance from a conversation.
Ranked #1 on
Causal Emotion Entailment
on RECCON
A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models
In response to the efficiency problem, recent studies show that dense PLMs can be replaced with sparse subnetworks without hurting the performance.
Empathetic Dialogue Generation via Sensitive Emotion Recognition and Sensible Knowledge Selection
We use a fine-grained encoding strategy which is more sensitive to the emotion dynamics (emotion flow) in the conversations to predict the emotion-intent characteristic of response.
Question-Interlocutor Scope Realized Graph Modeling over Key Utterances for Dialogue Reading Comprehension
Although these tasks are effective, there are still urging problems: (1) randomly masking speakers regardless of the question cannot map the speaker mentioned in the question to the corresponding speaker in the dialogue, and ignores the speaker-centric nature of utterances.
Personality Understanding of Fictional Characters during Book Reading
As readers engage with a story, their understanding of a character evolves based on new events and information; and multiple fine-grained aspects of personalities can be perceived.
Separate and Locate: Rethink the Text in Text-based Visual Question Answering
To tackle these problems, we propose a novel method named Separate and Locate (SaL) that explores text contextual cues and designs spatial position embedding to construct spatial relations between OCR texts.
Multi-level Adaptive Contrastive Learning for Knowledge Internalization in Dialogue Generation
Knowledge-grounded dialogue generation aims to mitigate the issue of text degeneration by incorporating external knowledge to supplement the context.
Plot Retrieval as an Assessment of Abstract Semantic Association
Readers usually only give an abstract and vague description as the query based on their own understanding, summaries, or speculations of the plot, which requires the retrieval model to have a strong ability to estimate the abstract semantic associations between the query and candidate plots.
Enhancing Empathetic and Emotion Support Dialogue Generation with Prophetic Commonsense Inference
The interest in Empathetic and Emotional Support conversations among the public has significantly increased.
SIG: Speaker Identification in Literature via Prompt-Based Generation
Identifying speakers of quotations in narratives is an important task in literary analysis, with challenging scenarios including the out-of-domain inference for unseen speakers, and non-explicit cases where there are no speaker mentions in surrounding context.
Previously on the Stories: Recap Snippet Identification for Story Reading
Similar to the "previously-on" scenes in TV shows, recaps can help book reading by recalling the readers' memory about the important elements in previous texts to better understand the ongoing plot.
Graph Representation of Narrative Context: Coherence Dependency via Retrospective Questions
This work introduces a novel and practical paradigm for narrative comprehension, stemming from the observation that individual passages within narratives are often cohesively related than being isolated.
TAKE: Topic-shift Aware Knowledge sElection for Dialogue Generation
The knowledge selector generally constructs a query based on the dialogue context and selects the most appropriate knowledge to help response generation.
Target Really Matters: Target-aware Contrastive Learning and Consistency Regularization for Few-shot Stance Detection
Despite the significant progress on this task, it is extremely time-consuming and budget-unfriendly to collect sufficient high-quality labeled data for every new target under fully-supervised learning, whereas unlabeled data can be collected easier.
Past, Present, and Future: Conversational Emotion Recognition through Structural Modeling of Psychological Knowledge
Furthermore, we utilize CSK to enrich edges with knowledge representations and process the SKAIG with a graph transformer.
Ranked #9 on
Emotion Recognition in Conversation
on DailyDialog
