Proceedings of The Fifth Workshop on e-Commerce and NLP (ECNLP 5)最新文献_第2页

Improving Specificity in Review Response Generation with Data-Driven Data Filtering 用数据驱动的数据过滤提高评论反应生成的特异性

Proceedings of The Fifth Workshop on e-Commerce and NLP (ECNLP 5) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.ecnlp-1.15

Tannon Kew, M. Volk

引用次数: 0

Clause Topic Classification in German and English Standard Form Contracts 德文和英文标准格式合同的条款主题分类

Proceedings of The Fifth Workshop on e-Commerce and NLP (ECNLP 5) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.ecnlp-1.23

Daniel Braun, F. Matthes

引用次数: 2

Data Quality Estimation Framework for Faster Tax Code Classification 快速税号分类的数据质量估计框架

Proceedings of The Fifth Workshop on e-Commerce and NLP (ECNLP 5) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.ecnlp-1.4

R. Kondadadi, Allen Williams, Nicolas Nicolov

引用次数: 0

Can Pretrained Language Models Generate Persuasive, Faithful, and Informative Ad Text for Product Descriptions? 预训练的语言模型能生成有说服力的、忠实的、信息丰富的产品描述广告文本吗?

Proceedings of The Fifth Workshop on e-Commerce and NLP (ECNLP 5) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.ecnlp-1.27

Fajri Koto, Jey Han Lau, Timothy Baldwin

引用次数: 7

Structured Extraction of Terms and Conditions from German and English Online Shops 从德语和英语网上商店的条款和条件的结构化提取

Proceedings of The Fifth Workshop on e-Commerce and NLP (ECNLP 5) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.ecnlp-1.21

Tobias Schamel, Daniel Braun, F. Matthes

引用次数: 1

Product Titles-to-Attributes As a Text-to-Text Task 作为文本到文本任务的产品标题到属性

Proceedings of The Fifth Workshop on e-Commerce and NLP (ECNLP 5) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.ecnlp-1.12

Gilad Fuchs, Yoni Acriche

{"title":"Product Titles-to-Attributes As a Text-to-Text Task","authors":"Gilad Fuchs, Yoni Acriche","doi":"10.18653/v1/2022.ecnlp-1.12","DOIUrl":"https://doi.org/10.18653/v1/2022.ecnlp-1.12","url":null,"abstract":"Online marketplaces use attribute-value pairs, such as brand, size, size type, color, etc. to help define important and relevant facts about a listing. These help buyers to curate their search results using attribute filtering and overall create a richer experience. Although their critical importance for listings’ discoverability, getting sellers to input tens of different attribute-value pairs per listing is costly and often results in missing information. This can later translate to the unnecessary removal of relevant listings from the search results when buyers are filtering by attribute values. In this paper we demonstrate using a Text-to-Text hierarchical multi-label ranking model framework to predict the most relevant attributes per listing, along with their expected values, using historic user behavioral data. This solution helps sellers by allowing them to focus on verifying information on attributes that are likely to be used by buyers, and thus, increase the expected recall for their listings. Specifically for eBay’s case we show that using this model can improve the relevancy of the attribute extraction process by 33.2% compared to the current highly-optimized production system. Apart from the empirical contribution, the highly generalized nature of the framework presented in this paper makes it relevant for many high-volume search-driven websites.","PeriodicalId":384006,"journal":{"name":"Proceedings of The Fifth Workshop on e-Commerce and NLP (ECNLP 5)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134342905","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: 1

Utilizing Cross-Modal Contrastive Learning to Improve Item Categorization BERT Model 利用跨模态对比学习改进项目分类BERT模型

Proceedings of The Fifth Workshop on e-Commerce and NLP (ECNLP 5) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.ecnlp-1.25

L. Chen, Houwei Chou

引用次数: 1

Product Answer Generation from Heterogeneous Sources: A New Benchmark and Best Practices 从异构来源生成产品答案:一个新的基准和最佳实践

Proceedings of The Fifth Workshop on e-Commerce and NLP (ECNLP 5) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.ecnlp-1.13

Xiaoyu Shen, Gianni Barlacchi, Marco Del Tredici, Weiwei Cheng, B. Byrne, A. Gispert

{"title":"Product Answer Generation from Heterogeneous Sources: A New Benchmark and Best Practices","authors":"Xiaoyu Shen, Gianni Barlacchi, Marco Del Tredici, Weiwei Cheng, B. Byrne, A. Gispert","doi":"10.18653/v1/2022.ecnlp-1.13","DOIUrl":"https://doi.org/10.18653/v1/2022.ecnlp-1.13","url":null,"abstract":"It is of great value to answer product questions based on heterogeneous information sources available on web product pages, e.g., semi-structured attributes, text descriptions, user-provided contents, etc. However, these sources have different structures and writing styles, which poses challenges for (1) evidence ranking, (2) source selection, and (3) answer generation. In this paper, we build a benchmark with annotations for both evidence selection and answer generation covering 6 information sources. Based on this benchmark, we conduct a comprehensive study and present a set of best practices. We show that all sources are important and contribute to answering questions. Handling all sources within one single model can produce comparable confidence scores across sources and combining multiple sources for training always helps, even for sources with totally different structures. We further propose a novel data augmentation method to iteratively create training samples for answer generation, which achieves close-to-human performance with only a few thousandannotations. Finally, we perform an in-depth error analysis of model predictions and highlight the challenges for future research.","PeriodicalId":384006,"journal":{"name":"Proceedings of The Fifth Workshop on e-Commerce and NLP (ECNLP 5)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124157004","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

OpenBrand: Open Brand Value Extraction from Product Descriptions OpenBrand:从产品描述中提取开放式品牌价值

Proceedings of The Fifth Workshop on e-Commerce and NLP (ECNLP 5) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.ecnlp-1.19

Kassem Sabeh, Mouna Kacimi, J. Gamper

{"title":"OpenBrand: Open Brand Value Extraction from Product Descriptions","authors":"Kassem Sabeh, Mouna Kacimi, J. Gamper","doi":"10.18653/v1/2022.ecnlp-1.19","DOIUrl":"https://doi.org/10.18653/v1/2022.ecnlp-1.19","url":null,"abstract":"Extracting attribute-value information from unstructured product descriptions continue to be of a vital importance in e-commerce applications. One of the most important product attributes is the brand which highly influences costumers’ purchasing behaviour. Thus, it is crucial to accurately extract brand information dealing with the main challenge of discovering new brand names. Under the open world assumption, several approaches have adopted deep learning models to extract attribute-values using sequence tagging paradigm. However, they did not employ finer grained data representations such as character level embeddings which improve generalizability. In this paper, we introduce OpenBrand, a novel approach for discovering brand names. OpenBrand is a BiLSTM-CRF-Attention model with embeddings at different granularities. Such embeddings are learned using CNN and LSTM architectures to provide more accurate representations. We further propose a new dataset for brand value extraction, with a very challenging task on zero-shot extraction. We have tested our approach, through extensive experiments, and shown that it outperforms state-of-the-art models in brand name discovery.","PeriodicalId":384006,"journal":{"name":"Proceedings of The Fifth Workshop on e-Commerce and NLP (ECNLP 5)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124200142","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: 1

Domain-specific knowledge distillation yields smaller and better models for conversational commerce 特定领域的知识精馏为会话式商务产生更小、更好的模型

Proceedings of The Fifth Workshop on e-Commerce and NLP (ECNLP 5) Pub Date : 1900-01-01 DOI: 10.18653/v1/2022.ecnlp-1.18

Kristen Howell, Jian Wang, Akshay Hazare, Joe Bradley, Chris Brew, Xi Chen, Matthew Dunn, Beth-Ann Hockey, Andrew Maurer, D. Widdows

引用次数: 2