Search Results for author:
Found 18 papers, 13 papers with code
Textual Relationship Modeling for Cross-Modal Information Retrieval
A joint neural model is proposed to learn feature representation individually in each modality.
Multimedia
OpenHowNet: An Open Sememe-based Lexical Knowledge Base
In this paper, we present an open sememe-based lexical knowledge base OpenHowNet.
COS960: A Chinese Word Similarity Dataset of 960 Word Pairs
Word similarity computation is a widely recognized task in the field of lexical semantics.
Modeling Semantic Compositionality with Sememe Knowledge
In this paper, we verify the effectiveness of sememes, the minimum semantic units of human languages, in modeling SC by a confirmatory experiment.
multi-word expression embedding
multi-word expression sememe prediction
Word-level Textual Adversarial Attacking as Combinatorial Optimization
Also, further experiments show our model has higher transferability and can bring more robustness enhancement to victim models by adversarial training.
Enhancing Transformer with Sememe Knowledge
While large-scale pretraining has achieved great success in many NLP tasks, it has not been fully studied whether external linguistic knowledge can improve data-driven models.
Frustratingly Hard Evidence Retrieval for QA Over Books
A lot of progress has been made to improve question answering (QA) in recent years, but the special problem of QA over narrative book stories has not been explored in-depth.
Weakly-Supervised Methods for Suicide Risk Assessment: Role of Related Domains
Social media has become a valuable resource for the study of suicidal ideation and the assessment of suicide risk.
Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study
Recent advancements in open-domain question answering (ODQA), i. e., finding answers from large open-domain corpus like Wikipedia, have led to human-level performance on many datasets.
Transformer Embeddings of Irregularly Spaced Events and Their Participants
The neural Hawkes process (Mei & Eisner, 2017) is a generative model of irregularly spaced sequences of discrete events.
The prediction of the quality of results in Logic Synthesis using Transformer and Graph Neural Networks
Furthermore, the Transformer and GNNs are adopted as a joint learning policy for the QoR prediction of the unseen circuit-optimization sequences.
Improving Stability of Fine-Tuning Pretrained Language Models via Component-Wise Gradient Norm Clipping
Despite its superior performance, such fine-tuning can be unstable, resulting in significant variance in performance and potential risks for practical applications.
ReCode: Robustness Evaluation of Code Generation Models
Most existing works on robustness in text or code tasks have focused on classification, while robustness in generation tasks is an uncharted area and to date there is no comprehensive benchmark for robustness in code generation.
Efficient Shapley Values Estimation by Amortization for Text Classification
In practice, Shapley Values are often estimated with a small number of stochastic model evaluations.
Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints
The increasing capabilities of large language models (LLMs) raise opportunities for artificial general intelligence but concurrently amplify safety concerns, such as potential misuse of AI systems, necessitating effective AI alignment.
Can You Follow Me? Testing Situational Understanding in ChatGPT
Understanding sentence meanings and updating information states appropriately across time -- what we call "situational understanding" (SU) -- is a critical ability for human-like AI agents.
Identifying Self-Disclosures of Use, Misuse and Addiction in Community-based Social Media Posts
In the last decade, the United States has lost more than 500, 000 people from an overdose involving prescription and illicit opioids making it a national public health emergency (USDHHS, 2017).
When Hindsight is Not 20/20: Testing Limits on Reflective Thinking in Large Language Models
In this paper, we set out to clarify these capabilities under a more stringent evaluation setting in which we disallow any kind of external feedback.
