LatinX in AI at North American Chapter of the Association for Computational Linguistics Conference 2022最新文献

Incorporating Natural Language Processing models in Mexico City's 311 Locatel 墨西哥城311 Locatel的自然语言处理模型

LatinX in AI at North American Chapter of the Association for Computational Linguistics Conference 2022 Pub Date : 2022-07-10 DOI: 10.52591/lxai202207101

Alejandro Molina-Villegas, Edwin Aldana-Bibadilla, O. Siordia, Jorge Pérez

引用次数: 0

Automatic multi-modal processing of language and vision to assist people with visual impairments 语言和视觉的自动多模态处理，以帮助有视觉障碍的人

LatinX in AI at North American Chapter of the Association for Computational Linguistics Conference 2022 Pub Date : 2022-07-10 DOI: 10.52591/lxai202207104

{"title":"Automatic multi-modal processing of language and vision to assist people with visual impairments","authors":"","doi":"10.52591/lxai202207104","DOIUrl":"https://doi.org/10.52591/lxai202207104","url":null,"abstract":"In recent years, the study of the intersection between vision and language modalities, specifically in visual question answering (VQA) models, has gained significant appeal due to its great potential in assistive applications for people with visual disabilities. Despite this, to date, many of the existing VQA models are nor applicable to this goal for at least three reasons. To begin with, they are designed to respond to a single question. That is, they are not able to give feedback to incomplete or incremental questions. Secondly, they only consider a single image which is neither blurred, nor poorly focused, nor poorly framed. All these problems are directly related to the loss of the visual capacity. People with visual disabilities may have trouble interacting with a visual user interface for asking questions and for taking adequate photographs. They also frequently need to read text captured by the images, and most current VQA systems fall short in this task. This work presents a PhD proposal with four lines of research that will be carried out until December 2025. It investigates techniques that increase the robustness of the VQA models. In particular we propose the integration of dialogue history, the analysis of more than one input image, and the incorporation of text recognition capabilities to the models. All of these contributions are motivated to assist people with vision problems with their day-to-day tasks.","PeriodicalId":350984,"journal":{"name":"LatinX in AI at North American Chapter of the Association for Computational Linguistics Conference 2022","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130157118","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: 0

Distributed Text Representations Using Transformers for Noisy Written Language 使用变压器对有噪声的书面语言进行分布式文本表示

LatinX in AI at North American Chapter of the Association for Computational Linguistics Conference 2022 Pub Date : 2022-07-10 DOI: 10.52591/lxai202207102

A. Rodriguez, Pablo Rivas, G. Bejarano

引用次数: 0

Study of Question Answering on Legal Software Document using BERT based models 基于BERT模型的法律软件文档问答研究

LatinX in AI at North American Chapter of the Association for Computational Linguistics Conference 2022 Pub Date : 2022-07-10 DOI: 10.52591/lxai202207103

Ernesto Quevedo Caballero, Mushfika Rahman, T. Cerný, Pablo Rivas, G. Bejarano

{"title":"Study of Question Answering on Legal Software Document using BERT based models","authors":"Ernesto Quevedo Caballero, Mushfika Rahman, T. Cerný, Pablo Rivas, G. Bejarano","doi":"10.52591/lxai202207103","DOIUrl":"https://doi.org/10.52591/lxai202207103","url":null,"abstract":"The transformer-based architectures have achieved remarkable success in several Natural Language Processing tasks, such as the Question Answering domain. Our research focuses on different transformer-based language models’ performance in software development legal domain specialized datasets for the Question Answering task. It compares the performance with the general-purpose Question Answering task. We have experimented with the PolicyQA dataset and conformed to documents regarding users’ data handling policies, which fall into the software legal domain. We used as base encoders BERT, ALBERT, RoBERTa, DistilBERT and LEGAL-BERT and compare their performance on the Question answering benchmark dataset SQuAD V2.0 and PolicyQA. Our results indicate that the performance of these models as contextual embeddings encoders in the PolicyQA dataset is significantly lower than in the SQuAD V2.0. Furthermore, we showed that surprisingly general domain BERT-based models like ALBERT and BERT obtain better performance than a more domain-specific trained model like LEGAL-BERT.","PeriodicalId":350984,"journal":{"name":"LatinX in AI at North American Chapter of the Association for Computational Linguistics Conference 2022","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115545463","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

Improving Language Model Fine-tuning with Information Gain Filtration 利用信息增益过滤改进语言模型微调

LatinX in AI at North American Chapter of the Association for Computational Linguistics Conference 2022 Pub Date : 2022-07-10 DOI: 10.52591/lxai202207105

Javier Turek, Richard Antonello, Nicole M. Beckage, Alexander G. Huth

引用次数: 0