Complex Word Identification (CWI) is the task of identifying which words or
phrases in a sentence are difficult to understand by a target audience. The
latest CWI Shared Task released data for two settings: monolingual (i.e. train
and test in the same language) and cross-lingual (i.e. test in a language not
seen during training). The best monolingual models relied on language-dependent
features, which do not generalise in the cross-lingual setting, while the best
cross-lingual model used neural networks with multi-task learning. In this
paper, we present monolingual and cross-lingual CWI models that perform as well
as (or better than) most models submitted to the latest CWI Shared Task. We
show that carefully selected features and simple learning models can achieve
state-of-the-art performance, and result in strong baselines for future
development in this area. Finally, we discuss how inconsistencies in the
annotation of the data can explain some of the results obtained.

本文介绍了关于复杂词汇辨识（CWI）的研究，包括单语和跨语言的模型，并使用神经网络实现了多任务学习，取得了可比和更好的成果，此外还分析了数据标注不一致的原因。

跨多语言的复杂词辨识强基线

Strong Baselines for Complex Word Identification across Multiple  Languages

We report the findings of the second Complex Word Identification (CWI) shared
task organized as part of the BEA workshop co-located with NAACL-HLT'2018. The
second CWI shared task featured multilingual and multi-genre datasets divided
into four tracks: English monolingual, German monolingual, Spanish monolingual,
and a multilingual track with a French test set, and two tasks: binary
classification and probabilistic classification. A total of 12 teams submitted
their results in different task/track combinations and 11 of them wrote system
description papers that are referred to in this report and appear in the BEA
workshop proceedings.

本篇论文报告了第二次 Complex Word Identification (CWI) shared task 的结果，这是 BEA 与 NAACL-HLT'2018 会议合作的一部分。该任务旨在识别复杂词语，分为 4 个轨道，包括英语、德语、西班牙语和多语种，涵盖了两种任务：二元分类和概率分类。11 个团队提交了论文，详细描述了其结果和方法。