International Conference on Language Resources and Evaluation最新文献_第2页

TArC: Tunisian Arabish Corpus, First complete release TArC:突尼斯阿拉伯语语料库，第一次完整发布

International Conference on Language Resources and Evaluation Pub Date : 2022-07-11 DOI: 10.48550/arXiv.2207.04796

Elisa Gugliotta, Marco Dinarelli

引用次数: 5

AsNER - Annotated Dataset and Baseline for Assamese Named Entity recognition 阿萨姆语命名实体识别的注释数据集和基线

International Conference on Language Resources and Evaluation Pub Date : 2022-07-07 DOI: 10.48550/arXiv.2207.03422

Dhrubajyoti Pathak, Sukumar Nandi, Priyankoo Sarmah

引用次数: 4

Building an Endangered Language Resource in the Classroom: Universal Dependencies for Kakataibo 在课堂上建立濒危语言资源:卡卡泰博语的普遍依赖

International Conference on Language Resources and Evaluation Pub Date : 2022-06-21 DOI: 10.48550/arXiv.2206.10343

R. Zariquiey, Claudia Alvarado, Ximena Echevarria, Luisa Gomez, Rosa Gonzales, Mariana Illescas, Sabina Oporto, Frederic Blum, Arturo Oncevay, Javier Vera

引用次数: 1

Misspelling Semantics in Thai 泰语语义拼写错误

International Conference on Language Resources and Evaluation Pub Date : 2022-06-20 DOI: 10.48550/arXiv.2206.09680

Pakawat Nakwijit, Matthew Purver

引用次数: 3

The Makerere Radio Speech Corpus: A Luganda Radio Corpus for Automatic Speech Recognition Makerere无线电语音语料库:用于自动语音识别的卢干达无线电语料库

International Conference on Language Resources and Evaluation Pub Date : 2022-06-20 DOI: 10.48550/arXiv.2206.09790

Jonathan Mukiibi, A. Katumba, J. Nakatumba-Nabende, Ali Hussein, Josh Meyer

引用次数: 5

Estimating Confidence of Predictions of Individual Classifiers and TheirEnsembles for the Genre Classification Task 体裁分类任务中单个分类器及其相似物预测的置信度估计

International Conference on Language Resources and Evaluation Pub Date : 2022-06-15 DOI: 10.48550/arXiv.2206.07427

Mikhail Lepekhin, S. Sharoff

{"title":"Estimating Confidence of Predictions of Individual Classifiers and TheirEnsembles for the Genre Classification Task","authors":"Mikhail Lepekhin, S. Sharoff","doi":"10.48550/arXiv.2206.07427","DOIUrl":"https://doi.org/10.48550/arXiv.2206.07427","url":null,"abstract":"Genre identification is a kind of non-topic text classification. The main difference between this task and topic classification is that genre, unlike topic, usually cannot be expressed just by some keywords and is defined as a functional space. Neural models based on pre-trained transformers, such as BERT or XLM-RoBERTa, demonstrate SOTA results in many NLP tasks, including non-topical classification. However, in many cases, their downstream application to very large corpora, such as those extracted from social media, can lead to unreliable results because of dataset shifts, when some raw texts do not match the profile of the training set. To mitigate this problem, we experiment with individual models as well as with their ensembles. To evaluate the robustness of all models we use a prediction confidence metric, which estimates the reliability of a prediction in the absence of a gold standard label. We can evaluate robustness via the confidence gap between the correctly classified texts and the misclassified ones on a labeled test corpus, higher gaps make it easier to identify whether a text is classified correctly. Our results show that for all of the classifiers tested in this study, there is a confidence gap, but for the ensembles, the gap is wider, meaning that ensembles are more robust than their individual models.","PeriodicalId":260442,"journal":{"name":"International Conference on Language Resources and Evaluation","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116190817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Borrowing or Codeswitching? Annotating for Finer-Grained Distinctions in Language Mixing 借用还是代码转换?语言混合中细粒度区别的注释

International Conference on Language Resources and Evaluation Pub Date : 2022-06-10 DOI: 10.48550/arXiv.2206.04973

Elena Álvarez Mellado, Constantine Lignos

引用次数: 5

Predicting Embedding Reliability in Low-Resource Settings Using Corpus Similarity Measures 利用语料库相似度预测低资源环境下嵌入可靠性

International Conference on Language Resources and Evaluation Pub Date : 2022-06-09 DOI: 10.48550/arXiv.2206.04330

Jonathan Dunn, Haipeng Li, Damian Sastre

引用次数: 3

MentSum: A Resource for Exploring Summarization of Mental Health Online Posts MentSum:一个探索心理健康在线帖子摘要的资源

International Conference on Language Resources and Evaluation Pub Date : 2022-06-02 DOI: 10.48550/arXiv.2206.00856

Sajad Sotudeh, Nazli Goharian, Zachary Young

{"title":"MentSum: A Resource for Exploring Summarization of Mental Health Online Posts","authors":"Sajad Sotudeh, Nazli Goharian, Zachary Young","doi":"10.48550/arXiv.2206.00856","DOIUrl":"https://doi.org/10.48550/arXiv.2206.00856","url":null,"abstract":"Mental health remains a significant challenge of public health worldwide. With increasing popularity of online platforms, many use the platforms to share their mental health conditions, express their feelings, and seek help from the community and counselors. Some of these platforms, such as Reachout, are dedicated forums where the users register to seek help. Others such as Reddit provide subreddits where the users publicly but anonymously post their mental health distress. Although posts are of varying length, it is beneficial to provide a short, but informative summary for fast processing by the counselors. To facilitate research in summarization of mental health online posts, we introduce Mental Health Summarization dataset, MentSum, containing over 24k carefully selected user posts from Reddit, along with their short user-written summary (called TLDR) in English from 43 mental health subreddits. This domain-specific dataset could be of interest not only for generating short summaries on Reddit, but also for generating summaries of posts on the dedicated mental health forums such as Reachout. We further evaluate both extractive and abstractive state-of-the-art summarization baselines in terms of Rouge scores, and finally conduct an in-depth human evaluation study of both user-written and system-generated summaries, highlighting challenges in this research.","PeriodicalId":260442,"journal":{"name":"International Conference on Language Resources and Evaluation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128653292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

BD-SHS: A Benchmark Dataset for Learning to Detect Online Bangla Hate Speech in Different Social Contexts BD-SHS:学习在不同社会背景下检测在线孟加拉语仇恨言论的基准数据集

International Conference on Language Resources and Evaluation Pub Date : 2022-06-01 DOI: 10.48550/arXiv.2206.00372

Nauros Romim, M. Ahmed, Md. Saiful Islam, Arnab Sen Sharma, Hriteshwar Talukder, M. R. Amin

{"title":"BD-SHS: A Benchmark Dataset for Learning to Detect Online Bangla Hate Speech in Different Social Contexts","authors":"Nauros Romim, M. Ahmed, Md. Saiful Islam, Arnab Sen Sharma, Hriteshwar Talukder, M. R. Amin","doi":"10.48550/arXiv.2206.00372","DOIUrl":"https://doi.org/10.48550/arXiv.2206.00372","url":null,"abstract":"Social media platforms and online streaming services have spawned a new breed of Hate Speech (HS). Due to the massive amount of user-generated content on these sites, modern machine learning techniques are found to be feasible and cost-effective to tackle this problem. However, linguistically diverse datasets covering different social contexts in which offensive language is typically used are required to train generalizable models. In this paper, we identify the shortcomings of existing Bangla HS datasets and introduce a large manually labeled dataset BD-SHS that includes HS in different social contexts. The labeling criteria were prepared following a hierarchical annotation process, which is the first of its kind in Bangla HS to the best of our knowledge. The dataset includes more than 50,200 offensive comments crawled from online social networking sites and is at least 60% larger than any existing Bangla HS datasets. We present the benchmark result of our dataset by training different NLP models resulting in the best one achieving an F1-score of 91.0%. In our experiments, we found that a word embedding trained exclusively using 1.47 million comments from social media and streaming sites consistently resulted in better modeling of HS detection in comparison to other pre-trained embeddings. Our dataset and all accompanying codes is publicly available at github.com/naurosromim/hate-speech-dataset-for-Bengali-social-media","PeriodicalId":260442,"journal":{"name":"International Conference on Language Resources and Evaluation","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115236786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7