Text similarity measurement is the basis of natural language processing tasks, which play an
important role in information retrieval, automatic question answering, machine translation …

Recent work has shown that either (1) increasing the input length or (2) increasing model
size can improve the performance of Transformer-based neural models. In this paper, we …

The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in
response to a query. Although the most common formulation of text ranking is search …

Representation learning is a critical ingredient for natural language processing systems.
Recent Transformer language models like BERT learn powerful textual representations, but …

For evaluating machine-generated texts, automatic methods hold the promise of avoiding
collection of human judgments, which can be expensive and time-consuming. The most …

Introduction Moving evidence-based practices into the hands of practitioners requires the
synthesis and translation of research literature. However, the growing pace of scientific …

Text style transfer rephrases a text from a source style (eg, informal) to a target style (eg,
formal) while keeping its original meaning. Despite the success existing works have …

The goal of this paper is to provide an overview of the methods that allow text
representations with a focus on embeddings for text of different lengths, specifically on works …

The ability to measure similarity between documents enables intelligent summarization and
analysis of large corpora. Past distances between documents suffer from either an inability …

Using the frequency of keywords is a classic approach in the formal analysis of text, but has
the drawback of glossing over the relationality of word meanings. Word embedding models …