We present a method for extracting a multilingual sentiment annotated dialog data set from Fallout New Vegas. The game developers have preannotated every line of dialog in the game in one of the 8 different sentiments: \textit{anger, disgust, fear, happy, neutral, pained, sad } and \textit{surprised}. The game has been translated into English, Spanish, German, French and Italian. We conduct experiments on multilingual, multilabel sentiment analysis on the extracted data set using multilingual BERT, XLMRoBERTa and language specific BERT models. In our experiments, multilingual BERT outperformed XLMRoBERTa for most of the languages, also language specific models were slightly better than multilingual BERT for most of the languages. The best overall accuracy was 54\% and it was achieved by using multilingual BERT on Spanish data. The extracted data set presents a challenging task for sentiment analysis. We have released the data, including the testing and training splits, openly on Zenodo. The data set has been shuffled for copyright reasons.

角色扮演游戏（RPG）在视频游戏对话中具有相当多的文本。游戏开发人员经常将此文本半通知。在本文中，我们从几个RPG中提取了有说服力对话的多语言数据集。我们使用称为BERT的自然语言处理（NLP）模型来显示该数据在构建说服检测系统中的生存能力。我们认为，作为各种NLP任务的数据源，视频游戏具有许多未使用的潜力。本文中描述的代码和数据可在Zenodo上找到。

这项研究提供了对僧伽罗文本分类的预训练语言模型的性能的首次全面分析。我们测试了一组不同的Sinhala文本分类任务，我们的分析表明，在包括Sinhala（XLM-R，Labse和Laser）的预训练的多语言模型中，XLM-R是迄今为止Sinhala文本的最佳模型分类。我们还预先培训了两种基于罗伯塔的单语僧伽罗模型，它们远远优于僧伽罗的现有预训练的语言模型。我们表明，在微调时，这些预训练的语言模型为僧伽罗文本分类树立了非常强大的基线，并且在标记数据不足以进行微调的情况下非常强大。我们进一步提供了一组建议，用于使用预训练的模型进行Sinhala文本分类。我们还介绍了新的注释数据集，可用于僧伽罗文本分类的未来研究，并公开发布我们的预培训模型。

Pre-trained transformers are now the de facto models in Natural Language Processing given their state-of-the-art results in many tasks and languages. However, most of the current models have been trained on languages for which large text resources are already available (such as English, French, Arabic, etc.). Therefore, there are still a number of low-resource languages that need more attention from the community. In this paper, we study the Algerian dialect which has several specificities that make the use of Arabic or multilingual models inappropriate. To address this issue, we collected more than one million Algerian tweets, and pre-trained the first Algerian language model: DziriBERT. When compared with existing models, DziriBERT achieves better results, especially when dealing with the Roman script. The obtained results show that pre-training a dedicated model on a small dataset (150 MB) can outperform existing models that have been trained on much more data (hundreds of GB). Finally, our model is publicly available to the community.

文本分类是具有各种有趣应用程序的典型自然语言处理或计算语言学任务。随着社交媒体平台上的用户数量的增加，数据加速促进了有关社交媒体文本分类（SMTC）或社交媒体文本挖掘的新兴研究。与英语相比，越南人是低资源语言之一，仍然没有集中精力并彻底利用。受胶水成功的启发，我们介绍了社交媒体文本分类评估（SMTCE）基准，作为各种SMTC任务的数据集和模型的集合。借助拟议的基准，我们实施和分析了各种基于BERT的模型（Mbert，XLM-R和Distilmbert）和基于单语的BERT模型（Phobert，Vibert，Vibert，Velectra和Vibert4news）的有效性SMTCE基准。单语模型优于多语言模型，并实现所有文本分类任务的最新结果。它提供了基于基准的多语言和单语言模型的客观评估，该模型将使越南语言中有关贝尔特兰的未来研究有利。

大型预用屏蔽语言模型已成为许多NLP问题的最先进的解决方案。虽然研究表明，单晶模型产生比多语言模型产生更好的结果，但训练数据集必须足够大。我们培训了立陶宛，拉脱维亚语和英语的三种语言Litlat Bert样模型，以及爱沙尼亚的单语Est-Roberta模型。我们在四个下游任务中评估它们的性能：命名实体识别，依赖解析，词语标记和单词类比。为了分析对单一语言的重要性以及大型培训集的重要性，我们将创建的模型与爱沙尼亚，拉脱维亚和立陶宛人进行了现有的单语和多语言伯特模型。结果表明，新创建的Litlat Bert和Est-Roberta模型在大多数情况下改善了所有测试任务的现有模型的结果。

作为世界上第四大语言家庭，Dravidian语言已成为自然语言处理（NLP）的研究热点。虽然Dravidian语言包含大量语言，但有相对较少的公众可用资源。此外，文本分类任务是自然语言处理的基本任务，如何将其与Dravidian语言中的多种语言相结合，仍然是Dravidian自然语言处理的主要困难。因此，为了解决这些问题，我们为Dravidian语言提出了一个多语言文本分类框架。一方面，该框架使用Labse预先训练的模型作为基础模型。针对多任务学习中文本信息偏见的问题，我们建议使用MLM策略选择语言特定的单词，并使用对抗训练来扰乱它们。另一方面，鉴于模型无法识别和利用语言之间的相关性的问题，我们进一步提出了一种特定于语言的表示模块，以丰富模型的语义信息。实验结果表明，我们提出的框架在多语言文本分类任务中具有重要性能，每个策略实现某些改进。

Automatic term extraction plays an essential role in domain language understanding and several natural language processing downstream tasks. In this paper, we propose a comparative study on the predictive power of Transformers-based pretrained language models toward term extraction in a multi-language cross-domain setting. Besides evaluating the ability of monolingual models to extract single- and multi-word terms, we also experiment with ensembles of mono- and multilingual models by conducting the intersection or union on the term output sets of different language models. Our experiments have been conducted on the ACTER corpus covering four specialized domains (Corruption, Wind energy, Equitation, and Heart failure) and three languages (English, French, and Dutch), and on the RSDO5 Slovenian corpus covering four additional domains (Biomechanics, Chemistry, Veterinary, and Linguistics). The results show that the strategy of employing monolingual models outperforms the state-of-the-art approaches from the related work leveraging multilingual models, regarding all the languages except Dutch and French if the term extraction task excludes the extraction of named entity terms. Furthermore, by combining the outputs of the two best performing models, we achieve significant improvements.

句子嵌入通常用于文本聚类和语义检索任务中。最先进的句子表示方法基于大量手动标记句子对集合的人工神经网络。高资源语言（例如英语或中文）可以使用足够数量的注释数据。在不太受欢迎的语言中，必须使用多语言模型，从而提供较低的性能。在本出版物中，我们通过提出一种培训有效的语言特定句子编码的方法来解决此问题，而无需手动标记数据。我们的方法是从句子对准双语文本语料库中自动构建释义对数据集。然后，我们使用收集的数据来微调具有附加复发池层的变压器语言模型。我们的句子编码器可以在不到一天的时间内在一张图形卡上进行培训，从而在各种句子级的任务上实现高性能。我们在波兰语中评估了八个语言任务的方法，并将其与最佳可用多语言句子编码器进行比较。

自然语言推理（NLI）和语义文本相似性（STS）是广泛使用的基准任务，用于对预训练的语言模型进行组成评估。尽管对语言普遍性的兴趣越来越大，但大多数NLI/STS研究几乎完全集中在英语上。特别是，日语中没有可用的多语言NLI/STS数据集，它在类型上与英语不同，并且可以阐明语言模型当前有争议的行为，例如对单词顺序和案例粒子的敏感性。在此背景下，我们介绍了日本NLI/STS数据集Jsick，该数据集是从英语数据集病中手动翻译的。我们还提出了一个用于组成推断的应力测试数据集，该数据集是通过转换JSick中句子的句法结构来研究语言模型是否对单词顺序和案例粒子敏感的。我们在不同的预训练语言模型上进行基线实验，并比较应用于日语和其他语言时多语言模型的性能。应力测试实验的结果表明，当前的预训练的语言模型对单词顺序和案例标记不敏感。

Understanding customer feedback is becoming a necessity for companies to identify problems and improve their products and services. Text classification and sentiment analysis can play a major role in analyzing this data by using a variety of machine and deep learning approaches. In this work, different transformer-based models are utilized to explore how efficient these models are when working with a German customer feedback dataset. In addition, these pre-trained models are further analyzed to determine if adapting them to a specific domain using unlabeled data can yield better results than off-the-shelf pre-trained models. To evaluate the models, two downstream tasks from the GermEval 2017 are considered. The experimental results show that transformer-based models can reach significant improvements compared to a fastText baseline and outperform the published scores and previous models. For the subtask Relevance Classification, the best models achieve a micro-averaged $F1$-Score of 96.1 % on the first test set and 95.9 % on the second one, and a score of 85.1 % and 85.3 % for the subtask Polarity Classification.

情感分析是NLP中研究最广泛的应用程序之一，但大多数工作都集中在具有大量数据的语言上。我们介绍了尼日利亚的四种口语最广泛的语言（Hausa，Igbo，Nigerian-Pidgin和Yor \'ub \'a）的第一个大规模的人类通知的Twitter情感数据集，该数据集由大约30,000个注释的推文组成（以及每种语言的大约30,000个）（以及14,000尼日利亚猎人），其中包括大量的代码混合推文。我们提出了文本收集，过滤，处理和标记方法，使我们能够为这些低资源语言创建数据集。我们评估了数据集上的预训练模型和转移策略。我们发现特定于语言的模型和语言适应性芬通常表现最好。我们将数据集，训练的模型，情感词典和代码释放到激励措施中，以代表性不足的语言进行情感分析。

在本文中，我们介绍了TweetNLP，这是社交媒体中自然语言处理（NLP）的集成平台。TweetNLP支持一套多样化的NLP任务，包括诸如情感分析和命名实体识别的通用重点领域，以及社交媒体特定的任务，例如表情符号预测和进攻性语言识别。特定于任务的系统由专门用于社交媒体文本的合理大小的基于变压器的语言模型（尤其是Twitter）提供动力，无需专用硬件或云服务即可运行。TweetNLP的主要贡献是：（1）使用适合社会领域的各种特定于任务的模型，用于支持社交媒体分析的现代工具包的集成python库；（2）使用我们的模型进行无编码实验的交互式在线演示；（3）涵盖各种典型社交媒体应用的教程。

Given the impact of language models on the field of Natural Language Processing, a number of Spanish encoder-only masked language models (aka BERTs) have been trained and released. These models were developed either within large projects using very large private corpora or by means of smaller scale academic efforts leveraging freely available data. In this paper we present a comprehensive head-to-head comparison of language models for Spanish with the following results: (i) Previously ignored multilingual models from large companies fare better than monolingual models, substantially changing the evaluation landscape of language models in Spanish; (ii) Results across the monolingual models are not conclusive, with supposedly smaller and inferior models performing competitively. Based on these empirical results, we argue for the need of more research to understand the factors underlying them. In this sense, the effect of corpus size, quality and pre-training techniques need to be further investigated to be able to obtain Spanish monolingual models significantly better than the multilingual ones released by large private companies, specially in the face of rapid ongoing progress in the field. The recent activity in the development of language technology for Spanish is to be welcomed, but our results show that building language models remains an open, resource-heavy problem which requires to marry resources (monetary and/or computational) with the best research expertise and practice.

JamPatoisNLI provides the first dataset for natural language inference in a creole language, Jamaican Patois. Many of the most-spoken low-resource languages are creoles. These languages commonly have a lexicon derived from a major world language and a distinctive grammar reflecting the languages of the original speakers and the process of language birth by creolization. This gives them a distinctive place in exploring the effectiveness of transfer from large monolingual or multilingual pretrained models. While our work, along with previous work, shows that transfer from these models to low-resource languages that are unrelated to languages in their training set is not very effective, we would expect stronger results from transfer to creoles. Indeed, our experiments show considerably better results from few-shot learning of JamPatoisNLI than for such unrelated languages, and help us begin to understand how the unique relationship between creoles and their high-resource base languages affect cross-lingual transfer. JamPatoisNLI, which consists of naturally-occurring premises and expert-written hypotheses, is a step towards steering research into a traditionally underserved language and a useful benchmark for understanding cross-lingual NLP.

与标准命名实体识别（NER）相比，在历史文本中识别人，位置和组织是一个巨大的挑战。为了获得机器可读的语料库，通常需要扫描历史文本，并且需要执行光学特征识别（OCR）。结果，历史文献包含错误。此外，位置或组织等实体可以随着时间的推移而改变，这构成了另一个挑战。总体而言，历史文本带有几种特殊性，这些特殊性与现代文本有很大不同，并且在该领域几乎无法使用训练神经标记器的大型标记的Corpora。在这项工作中，我们通过培训大型历史语言模型来解决历史，英语，法语，瑞典语和芬兰语的历史文献。我们通过使用未标记的数据预处理语言模型来规避大量标记数据的需求。我们提出了Hmbert，这是一种历史多语言基于BERT的语言模型，并以多种不同大小的版本发布该模型。此外，我们通过解决下游NER作为今年HIPE-2022共享任务的一部分来评估HMBERT的能力，并提供详细的分析和见解。对于多种语言的经典评论粗粒ner挑战，我们的标记者Histeria的表现优于其他团队的三种语言中的其他团队的模型。

The BERT family of neural language models have become highly popular due to their ability to provide sequences of text with rich context-sensitive token encodings which are able to generalise well to many NLP tasks. We introduce gaBERT, a monolingual BERT model for the Irish language. We compare our gaBERT model to multilingual BERT and the monolingual Irish WikiBERT, and we show that gaBERT provides better representations for a downstream parsing task. We also show how different filtering criteria, vocabulary size and the choice of subword tokenisation model affect downstream performance. We compare the results of fine-tuning a gaBERT model with an mBERT model for the task of identifying verbal multiword expressions, and show that the fine-tuned gaBERT model also performs better at this task. We release gaBERT and related code to the community.

We present a DialGPT based model for generating creative dialog responses that are conditioned based on one of the following emotions: anger, disgust, fear, happiness, pain, sadness and surprise. Our model is capable of producing a contextually apt response given an input sentence and a desired emotion label. Our model is capable of expressing the desired emotion with an accuracy of 0.6. The best performing emotions are neutral, fear and disgust. When measuring the strength of the expressed emotion, we find that anger, fear and disgust are expressed in the most strong fashion by the model.

多语言预训练的语言模型（PLM）在高资源和低资源语言的下游任务上表现出令人印象深刻的表现。但是，在预培训期间，尤其是非洲语言中，看不见的语言仍然有很大的表现。适应新语言的最有效方法之一是\ textit {语言自适应微调}（LAFT） - 使用预训练目标对单语言的多语言PLM进行微调。但是，适应目标语言会单独使用大磁盘空间，并限制了由此产生的模型的跨语言转移能力，因为它们已经专门用于单语言。在本文中，我们对17种最重要的非洲语言和其他三种在非洲大陆上广泛使用的高资源语言对17种最具资源的非洲语言进行\ Textit {多语言自适应微调}，以鼓励跨语性转移学习。为了进一步专注于多语言PLM，我们从嵌入式层中删除了与MAFT之前的非非洲写作脚本相对应的词汇令牌，从而将模型大小降低了约50％。我们对两个多语言PLM（Afriberta和XLM-R）和三个NLP任务（NER，新闻主题分类和情感分类）的评估表明，我们的方法可以在单个语言上应用LAFT，同时需要较小的磁盘空间。此外，我们表明我们的适应性PLM还提高了参数有效微调方法的零击跨语性转移能力。

对于自然语言处理应用可能是有问题的，因为它们的含义不能从其构成词语推断出来。缺乏成功的方法方法和足够大的数据集防止了用于检测成语的机器学习方法的开发，特别是对于在训练集中不发生的表达式。我们提出了一种叫做小鼠的方法，它使用上下文嵌入来实现此目的。我们展示了一个新的多字表达式数据集，具有文字和惯用含义，并使用它根据两个最先进的上下文单词嵌入式培训分类器：Elmo和Bert。我们表明，使用两个嵌入式的深度神经网络比现有方法更好地执行，并且能够检测惯用词使用，即使对于训练集中不存在的表达式。我们展示了开发模型的交叉传输，并分析了所需数据集的大小。