To Share or not to Share: Predicting Sets of Sources for Model Transfer Learning

A Machine Learning Approach for Predicting Human Preference for Graph Layouts

Understanding what graph layout human prefer and why they prefer is significant and challenging due to the highly complex visual perception and cognition system in human brain. In this paper, we present the first machine learning approach for predicting human preference for graph layouts. In general, the data sets with human preference labels are limited and insufficient for training deep networks. To address this, we train our deep learning model by employing the transfer learning method, e.g., exploiting the quality metrics, such as shape-based metrics, edge crossing and stress, which are sh…

Predicting the Future Behavior of a Time-Varying Probability Distribution

We study the problem of predicting the future, though only in the probabilistic sense of estimating a future state of a time-varying probability distribution. This is not only an interesting academic problem, but solving this extrapolation problem also has many practical application, e.g. for training classifiers that have to operate under time-varying conditions. Our main contribution is a method for predicting the next step of the time-varying distribution from a given sequence of sample sets from earlier time steps. For this we rely on two recent machine learning techniques: embedding proba…

Evaluating Entity Disambiguation and the Role of Popularity in Retrieval-Based NLP

Retrieval is a core component for open-domain NLP tasks. In open-domain tasks, multiple entities can share a name, making disambiguation an inherent yet under-explored problem. We propose an evaluation benchmark for assessing the entity disambiguation capabilities of these retrievers, which we call Ambiguous Entity Retrieval (AmbER) sets. We define an AmbER set as a collection of entities that share a name along with queries about those entities. By covering the set of entities for polysemous names, AmbER sets act as a challenging test of entity disambiguation. We create AmbER sets for three p…

工业界中的多领域多语言情感分析：预测基于方面的意见四元组

Multi-domain Multilingual Sentiment Analysis in Industry: Predicting Aspect-based Opinion Quadruples

本文探讨使用大语言模型进行基于方面的多领域多语言四元组意见提取，证明单一微调多领域模型性能可媲美专门模型且降低复杂性，并分享了结构化预测任务中的实践经验。

利用参照翻译机预测语境中的词语相似度

Predicting Word Similarity in Context with Referential Translation Machines

本研究将语境下的英语词语相似度判定转化为机器翻译性能预测任务，采用支持统一表示与堆叠模型的参照翻译机（RTMs），在语境分级词语相似度（GWSC）任务中取得最优结果。

DUSK：不要遗忘共享知识

DUSK: Do Not Unlearn Shared Knowledge

提出DUSK基准，评估数据重叠场景下的机器遗忘方法，发现现有方法难以在移除独有内容时保留共享事实。