AACL Bioflux最新文献_第3页

Underspecification in Scene Description-to-Depiction Tasks 场景描述到描述任务的规格不足

AACL Bioflux Pub Date : 2022-10-11 DOI: 10.48550/arXiv.2210.05815

B. Hutchinson, Jason Baldridge, Vinodkumar Prabhakaran

引用次数: 21

CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking CSS:结合自我训练和自我监督学习的少镜头对话状态跟踪

AACL Bioflux Pub Date : 2022-10-11 DOI: 10.48550/arXiv.2210.05146

Haoning Zhang, Junwei Bao, Haipeng Sun, Huaishao Luo, Wenye Li, Shuguang Cui

引用次数: 1

BanglaParaphrase: A High-Quality Bangla Paraphrase Dataset 高质量孟加拉语释义数据集

AACL Bioflux Pub Date : 2022-10-11 DOI: 10.48550/arXiv.2210.05109

Ajwad Akil, Najrin Sultana, Abhik Bhattacharjee, Rifat Shahriyar

引用次数: 4

Transformer-based Localization from Embodied Dialog with Large-scale Pre-training 基于变压器的大规模预训练嵌入对话定位

AACL Bioflux Pub Date : 2022-10-10 DOI: 10.48550/arXiv.2210.04864

Meera Hahn, James M. Rehg

引用次数: 1

CrowdChecked: Detecting Previously Fact-Checked Claims in Social Media CrowdChecked:检测社交媒体中先前经过事实核查的言论

AACL Bioflux Pub Date : 2022-10-10 DOI: 10.48550/arXiv.2210.04447

Momchil Hardalov, Anton Chernyavskiy, Ivan Koychev, Dmitry I. Ilvovsky, Preslav Nakov

{"title":"CrowdChecked: Detecting Previously Fact-Checked Claims in Social Media","authors":"Momchil Hardalov, Anton Chernyavskiy, Ivan Koychev, Dmitry I. Ilvovsky, Preslav Nakov","doi":"10.48550/arXiv.2210.04447","DOIUrl":"https://doi.org/10.48550/arXiv.2210.04447","url":null,"abstract":"While there has been substantial progress in developing systems to automate fact-checking, they still lack credibility in the eyes of the users. Thus, an interesting approach has emerged: to perform automatic fact-checking by verifying whether an input claim has been previously fact-checked by professional fact-checkers and to return back an article that explains their decision. This is a sensible approach as people trust manual fact-checking, and as many claims are repeated multiple times. Yet, a major issue when building such systems is the small number of known tweet–verifying article pairs available for training. Here, we aim to bridge this gap by making use of crowd fact-checking, i.e., mining claims in social media for which users have responded with a link to a fact-checking article. In particular, we mine a large-scale collection of 330,000 tweets paired with a corresponding fact-checking article. We further propose an end-to-end framework to learn from this noisy data based on modified self-adaptive training, in a distant supervision scenario. Our experiments on the CLEF’21 CheckThat! test set show improvements over the state of the art by two points absolute. Our code and datasets are available at https://github.com/mhardalov/crowdchecked-claims","PeriodicalId":39298,"journal":{"name":"AACL Bioflux","volume":"49 1","pages":"266-285"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73628095","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: 4

Named Entity Recognition in Twitter: A Dataset and Analysis on Short-Term Temporal Shifts Twitter中的命名实体识别:短期时间变化的数据集和分析

AACL Bioflux Pub Date : 2022-10-07 DOI: 10.48550/arXiv.2210.03797

Asahi Ushio, Leonardo Neves, V'itor Silva, Francesco Barbieri, José Camacho-Collados

{"title":"Named Entity Recognition in Twitter: A Dataset and Analysis on Short-Term Temporal Shifts","authors":"Asahi Ushio, Leonardo Neves, V'itor Silva, Francesco Barbieri, José Camacho-Collados","doi":"10.48550/arXiv.2210.03797","DOIUrl":"https://doi.org/10.48550/arXiv.2210.03797","url":null,"abstract":"Recent progress in language model pre-training has led to important improvements in Named Entity Recognition (NER). Nonetheless, this progress has been mainly tested in well-formatted documents such as news, Wikipedia, or scientific articles. In social media the landscape is different, in which it adds another layer of complexity due to its noisy and dynamic nature. In this paper, we focus on NER in Twitter, one of the largest social media platforms, and construct a new NER dataset, TweetNER7, which contains seven entity types annotated over 11,382 tweets from September 2019 to August 2021. The dataset was constructed by carefully distributing the tweets over time and taking representative trends as a basis. Along with the dataset, we provide a set of language model baselines and perform an analysis on the language model performance on the task, especially analyzing the impact of different time periods. In particular, we focus on three important temporal aspects in our analysis: short-term degradation of NER models over time, strategies to fine-tune a language model over different periods, and self-labeling as an alternative to lack of recently-labeled data. TweetNER7 is released publicly (https://huggingface.co/datasets/tner/tweetner7) along with the models fine-tuned on it (NER models have been integrated into TweetNLP and can be found at https://github.com/asahi417/tner/tree/master/examples/tweetner7_paper).","PeriodicalId":39298,"journal":{"name":"AACL Bioflux","volume":"2 1","pages":"309-319"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84424063","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

The Lifecycle of “Facts”: A Survey of Social Bias in Knowledge Graphs “事实”的生命周期:知识图谱中的社会偏见调查

AACL Bioflux Pub Date : 2022-10-07 DOI: 10.48550/arXiv.2210.03353

Angelie Kraft, Ricardo Usbeck

引用次数: 3

Missing Modality meets Meta Sampling (M3S): An Efficient Universal Approach for Multimodal Sentiment Analysis with Missing Modality 情态缺失与元抽样(M3S):一种有效的通用的情态缺失多模态情感分析方法

AACL Bioflux Pub Date : 2022-10-07 DOI: 10.48550/arXiv.2210.03428

Haozhe Chi, Minghua Yang, Junhao Zhu, Guanhong Wang, Gaoang Wang

引用次数: 0

Hate Speech and Offensive Language Detection in Bengali 孟加拉语中的仇恨言论和攻击性语言检测

AACL Bioflux Pub Date : 2022-10-07 DOI: 10.48550/arXiv.2210.03479

Mithun Das, Somnath Banerjee, Punyajoy Saha, Animesh Mukherjee

{"title":"Hate Speech and Offensive Language Detection in Bengali","authors":"Mithun Das, Somnath Banerjee, Punyajoy Saha, Animesh Mukherjee","doi":"10.48550/arXiv.2210.03479","DOIUrl":"https://doi.org/10.48550/arXiv.2210.03479","url":null,"abstract":"Social media often serves as a breeding ground for various hateful and offensive content. Identifying such content on social media is crucial due to its impact on the race, gender, or religion in an unprejudiced society. However, while there is extensive research in hate speech detection in English, there is a gap in hateful content detection in low-resource languages like Bengali. Besides, a current trend on social media is the use of Romanized Bengali for regular interactions. To overcome the existing research’s limitations, in this study, we develop an annotated dataset of 10K Bengali posts consisting of 5K actual and 5K Romanized Bengali tweets. We implement several baseline models for the classification of such hateful posts. We further explore the interlingual transfer mechanism to boost classification performance. Finally, we perform an in-depth error analysis by looking into the misclassified posts by the models. While training actual and Romanized datasets separately, we observe that XLM-Roberta performs the best. Further, we witness that on joint training and few-shot training, MuRIL outperforms other models by interpreting the semantic expressions better. We make our code and dataset public for others.","PeriodicalId":39298,"journal":{"name":"AACL Bioflux","volume":"20 1","pages":"286-296"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78374235","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: 10

Not another Negation Benchmark: The NaN-NLI Test Suite for Sub-clausal Negation 不是另一个否定基准:子条款否定的NaN-NLI测试套件

AACL Bioflux Pub Date : 2022-10-06 DOI: 10.48550/arXiv.2210.03256

Thinh Hung Truong, Yulia Otmakhova, Tim Baldwin, Trevor Cohn, Karin M. Verspoor, Jey Han Lau

{"title":"Not another Negation Benchmark: The NaN-NLI Test Suite for Sub-clausal Negation","authors":"Thinh Hung Truong, Yulia Otmakhova, Tim Baldwin, Trevor Cohn, Karin M. Verspoor, Jey Han Lau","doi":"10.48550/arXiv.2210.03256","DOIUrl":"https://doi.org/10.48550/arXiv.2210.03256","url":null,"abstract":"Negation is poorly captured by current language models, although the extent of this problem is not widely understood. We introduce a natural language inference (NLI) test suite to enable probing the capabilities of NLP methods, with the aim of understanding sub-clausal negation. The test suite contains premise–hypothesis pairs where the premise contains sub-clausal negation and the hypothesis is constructed by making minimal modifications to the premise in order to reflect different possible interpretations. Aside from adopting standard NLI labels, our test suite is systematically constructed under a rigorous linguistic framework. It includes annotation of negation types and constructions grounded in linguistic theory, as well as the operations used to construct hypotheses. This facilitates fine-grained analysis of model performance. We conduct experiments using pre-trained language models to demonstrate that our test suite is more challenging than existing benchmarks focused on negation, and show how our annotation supports a deeper understanding of the current NLI capabilities in terms of negation and quantification.","PeriodicalId":39298,"journal":{"name":"AACL Bioflux","volume":"11 1","pages":"883-894"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82194821","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: 11