Embedding & MLP has become a paradigm for modern large-scale recommendation
system. However, this paradigm suffers from the cold-start problem which will seriously …

Finding node correspondence across networks, namely multi-network alignment, is an
essential prerequisite for joint learning on multiple networks. Despite great success in …

Answering complex queries on knowledge graphs is important but particularly challenging
because of the data incompleteness. Query embedding methods address this issue by …

The task of document-level text simplification is very similar to summarization with the
additional difficulty of reducing complexity. We introduce a newly collected data set of …

Bilingual lexicon induction induces the word translations by aligning independently trained
word embeddings in two languages. Existing approaches generally focus on minimizing the …

Sufficient dimension reduction is used pervasively as a supervised dimension reduction
approach. Most existing sufficient dimension reduction methods are developed for data with …

Bilingual lexicons form a critical component of various natural language processing
applications, including unsupervised and semisupervised machine translation and …

Abstract Measuring Sentence Textual Similarity (STS) is a classic task that can be applied to
many downstream NLP applications such as text generation and retrieval. In this paper, we …

Entity Alignment (EA) aims to identify pairs of entities from two distinct language knowledge
graphs (KGs) that represent the same real-world objects. Current EA methods have …

Cross-lingual word alignment is the task for word translation between monolingual word
embedding spaces of two different languages. Recent work is mostly based on supervised …