Recent years have seen the paradigm shift of Named Entity Recognition (NER) systems
from sequence labeling to span prediction. Despite its preliminary effectiveness, the span …

One of the most striking findings in modern research on large language models (LLMs) is
that scaling up compute during training leads to better results. However, less attention has …

With the rapid development of NLP research, leaderboards have emerged as one tool to
track the performance of various systems on various NLP tasks. They are effective in this …

Minimum Bayes Risk (MBR) decoding is a method for choosing the outputs of a machine
learning system based not on the output with the highest probability, but the output with the …

Although some recent works show potential complementarity among different state-of-the-art
systems, few works try to investigate this problem in text summarization. Researchers in …

In this article we address the issue of generating diversified translation systems from a single
Statistical Machine Translation (SMT) engine for system combination. Unlike traditional …

E-commerce platforms present products using titles that summarize product information.
These titles cannot be created by hand, therefore an algorithmic solution is required. The …

Even though the rise of the neural machine translation (NMT) paradigm has brought a great
deal of improvement to the field of machine translation (MT), the current translation results …

For extended periods of time, sequence generation models rely on beam search algorithm
to generate output sequence. However, the correctness of beam search degrades when the …

To overcome the scarceness of bilingual corpora for some language pairs in machine
translation, pivot-based SMT uses pivot language as a" bridge" to generate source-target …