Named Entity Recognition(NER) is a task of recognizing entities at a token
level in a sentence. This paper focuses on solving NER tasks in a multilingual
setting for complex named entities. Our team, LLM-RM participated in the
recently organized SemEval 2023 task, Task 2: MultiCoNER II,Multilingual
Complex Named Entity Recognition. We approach the problem by leveraging
cross-lingual representation provided by fine-tuning XLM-Roberta base model on
datasets of all of the 12 languages provided -- Bangla, Chinese, English,
Farsi, French, German, Hindi, Italian, Portuguese, Spanish, Swedish and
Ukrainian

通过在所有提供的 12 种语言数据集上微调 XLM-Roberta 基础模型，利用跨语言表示方法解决 NER 多语言复杂命名实体识别任务，达到 SemEval 2023 任务 2 的最佳表现。

LLM-RM 在 SemEval-2023 任务 2 中的应用：利用 XLM-RoBERTa 进行多语言复杂命名实体识别

LLM-RM at SemEval-2023 Task 2: Multilingual Complex NER using  XLM-RoBERTa

In this paper, we propose a unified pre-training approach called UniSpeech to
learn speech representations with both unlabeled and labeled data, in which
supervised phonetic CTC learning and phonetically-aware contrastive
self-supervised learning are conducted in a multi-task learning manner. The
resultant representations can capture information more correlated with phonetic
structures and improve the generalization across languages and domains. We
evaluate the effectiveness of UniSpeech for cross-lingual representation
learning on public CommonVoice corpus. The results show that UniSpeech
outperforms self-supervised pretraining and supervised transfer learning for
speech recognition by a maximum of 13.4% and 17.8% relative phone error rate
reductions respectively (averaged over all testing languages). The
transferability of UniSpeech is also demonstrated on a domain-shift speech
recognition task, i.e., a relative word error rate reduction of 6% against the
previous approach.

本文提出了一种名为 UniSpeech 的统一预训练方法，利用有标签和无标签数据学习语音表示，其中针对语音结构的有监督 CTC 学习和语音感知对比自监督学习以多任务学习的方式进行。实验结果表明，UniSpeech 在跨语言表征学习方面的有效性超过了自监督预训练和有监督转移学习。