NLP datasets annotated with human judgments are rife with disagreements
between the judges. This is especially true for tasks depending on subjective
judgments such as sentiment analysis or offensive language detection.
Particularly in these latter cases, the NLP community has come to realize that
the approach of 'reconciling' these different subjective interpretations is
inappropriate. Many NLP researchers have therefore concluded that rather than
eliminating disagreements from annotated corpora, we should preserve
them-indeed, some argue that corpora should aim to preserve all annotator
judgments. But this approach to corpus creation for NLP has not yet been widely
accepted. The objective of the LeWiDi series of shared tasks is to promote this
approach to developing NLP models by providing a unified framework for training
and evaluating with such datasets. We report on the second LeWiDi shared task,
which differs from the first edition in three crucial respects: (i) it focuses
entirely on NLP, instead of both NLP and computer vision tasks in its first
edition; (ii) it focuses on subjective tasks, instead of covering different
types of disagreements-as training with aggregated labels for subjective NLP
tasks is a particularly obvious misrepresentation of the data; and (iii) for
the evaluation, we concentrate on soft approaches to evaluation. This second
edition of LeWiDi attracted a wide array of participants resulting in 13 shared
task submission papers.

本文报告第二届 LeWiDi 共享任务，其集中于争议性任务。为了训练和评估 NLP 模型，提出了一种保留所有注释者判断的新的语料库创建方法，并介绍了敦促 NLP 研究人员采用这种方法的 LeWiDi 系列。

SemEval-2023 任务 11：学习中的不一致性（LeWiDi）

SemEval-2023 Task 11: Learning With Disagreements (LeWiDi)

We present a survey covering the state of the art in low-resource machine
translation research. There are currently around 7000 languages spoken in the
world and almost all language pairs lack significant resources for training
machine translation models. There has been increasing interest in research
addressing the challenge of producing useful translation models when very
little translated training data is available. We present a summary of this
topical research field and provide a description of the techniques evaluated by
researchers in several recent shared tasks in low-resource MT.

介绍了低资源机器翻译研究领域的现状，重点是在少量翻译训练数据的情况下制备有用的翻译模型的挑战以及在近期几个共享任务中评估研究者的技术的描述。

低资源机器翻译调查

Survey of Low-Resource Machine Translation

We present the IIT Bombay English-Hindi Parallel Corpus. The corpus is a
compilation of parallel corpora previously available in the public domain as
well as new parallel corpora we collected. The corpus contains 1.49 million
parallel segments, of which 694k segments were not previously available in the
public domain. The corpus has been pre-processed for machine translation, and
we report baseline phrase-based SMT and NMT translation results on this corpus.
This corpus has been used in two editions of shared tasks at the Workshop on
Asian Language Translation (2016 and 2017). The corpus is freely available for
non-commercial research. To the best of our knowledge, this is the largest
publicly available English-Hindi parallel corpus.

本文介绍了 IIT Bombay 英印双语平行语料库，包含超过 140 万个平行句段，其中有 69.4k 个句段从未公开发布，是目前公开发布的最大的英印平行语料库之一，并已被用于机器翻译的基准测试和分享任务。此语料库可供非商业研究免费使用。