AACL Bioflux最新文献_第2页

Enhancing Tabular Reasoning with Pattern Exploiting Training 利用模式挖掘训练提高表格推理能力

AACL Bioflux Pub Date : 2022-10-21 DOI: 10.48550/arXiv.2210.12259

Abhilash Shankarampeta, Vivek Gupta, Shuo Zhang

引用次数: 4

AugCSE: Contrastive Sentence Embedding with Diverse Augmentations 不同增词的对比句嵌入

AACL Bioflux Pub Date : 2022-10-20 DOI: 10.48550/arXiv.2210.13749

Zilu Tang, Muhammed Yusuf Kocyigit, D. Wijaya

引用次数: 4

GCDT: A Chinese RST Treebank for Multigenre and Multilingual Discourse Parsing 面向多体裁、多语言语篇分析的汉语RST树库

AACL Bioflux Pub Date : 2022-10-19 DOI: 10.48550/arXiv.2210.10449

Siyao Peng, Yang Janet Liu, Amir Zeldes

引用次数: 5

Domain Specific Sub-network for Multi-Domain Neural Machine Translation 多领域神经机器翻译的领域特定子网络

AACL Bioflux Pub Date : 2022-10-18 DOI: 10.48550/arXiv.2210.09805

Amr Hendy, M. Abdelghaffar, M. Afify, Ahmed Tawfik

引用次数: 0

Systematic Evaluation of Predictive Fairness 预测公平性的系统评价

AACL Bioflux Pub Date : 2022-10-17 DOI: 10.48550/arXiv.2210.08758

Xudong Han, Aili Shen, Trevor Cohn, Timothy Baldwin, Lea Frermann

引用次数: 3

Some Languages are More Equal than Others: Probing Deeper into the Linguistic Disparity in the NLP World 有些语言比其他语言更平等:对NLP世界语言差异的深入探讨

AACL Bioflux Pub Date : 2022-10-16 DOI: 10.48550/arXiv.2210.08523

Surangika Ranathunga, Nisansa de Silva

引用次数: 10

COFAR: Commonsense and Factual Reasoning in Image Search COFAR:图像搜索中的常识和事实推理

AACL Bioflux Pub Date : 2022-10-16 DOI: 10.48550/arXiv.2210.08554

Prajwal Gatti, A. S. Penamakuri, Revant Teotia, Anand Mishra, Shubhashis Sengupta, Roshni Ramnani

{"title":"COFAR: Commonsense and Factual Reasoning in Image Search","authors":"Prajwal Gatti, A. S. Penamakuri, Revant Teotia, Anand Mishra, Shubhashis Sengupta, Roshni Ramnani","doi":"10.48550/arXiv.2210.08554","DOIUrl":"https://doi.org/10.48550/arXiv.2210.08554","url":null,"abstract":"One characteristic that makes humans superior to modern artificially intelligent models is the ability to interpret images beyond what is visually apparent. Consider the following two natural language search queries – (i) “a queue of customers patiently waiting to buy ice cream” and (ii) “a queue of tourists going to see a famous Mughal architecture in India”. Interpreting these queries requires one to reason with (i) Commonsense such as interpreting people as customers or tourists, actions as waiting to buy or going to see; and (ii) Fact or world knowledge associated with named visual entities, for example, whether the store in the image sells ice cream or whether the landmark in the image is a Mughal architecture located in India. Such reasoning goes beyond just visual recognition. To enable both commonsense and factual reasoning in the image search, we present a unified framework namely Knowledge Retrieval-Augmented Multimodal Transformer (KRAMT) that treats the named visual entities in an image as a gateway to encyclopedic knowledge and leverages them along with natural language query to ground relevant knowledge. Further, KRAMT seamlessly integrates visual content and grounded knowledge to learn alignment between images and search queries. This unified framework is then used to perform image search requiring commonsense and factual reasoning. The retrieval performance of KRAMT is evaluated and compared with related approaches on a new dataset we introduce – namely COFAR.","PeriodicalId":39298,"journal":{"name":"AACL Bioflux","volume":"44 1","pages":"1185-1199"},"PeriodicalIF":0.0,"publicationDate":"2022-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79742140","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: 3

Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation 法律案件文书摘要:抽取与抽象方法及其评价

AACL Bioflux Pub Date : 2022-10-14 DOI: 10.48550/arXiv.2210.07544

A. Shukla, Paheli Bhattacharya, Soham Poddar, Rajdeep Mukherjee, Kripabandhu Ghosh, Pawan Goyal, Saptarshi Ghosh

引用次数: 13

Improving Graph-Based Text Representations with Character and Word Level N-grams 用字符和词级n -图改进基于图的文本表示

AACL Bioflux Pub Date : 2022-10-12 DOI: 10.48550/arXiv.2210.05999

Wenzhe Li, Nikolaos Aletras

引用次数: 1

How Well Do Multi-hop Reading Comprehension Models Understand Date Information? 多跳阅读理解模型对日期信息的理解程度如何?

AACL Bioflux Pub Date : 2022-10-11 DOI: 10.48550/arXiv.2210.05208

Xanh Ho, Saku Sugawara, Akiko Aizawa

{"title":"How Well Do Multi-hop Reading Comprehension Models Understand Date Information?","authors":"Xanh Ho, Saku Sugawara, Akiko Aizawa","doi":"10.48550/arXiv.2210.05208","DOIUrl":"https://doi.org/10.48550/arXiv.2210.05208","url":null,"abstract":"Several multi-hop reading comprehension datasets have been proposed to resolve the issue of reasoning shortcuts by which questions can be answered without performing multi-hop reasoning. However, the ability of multi-hop models to perform step-by-step reasoning when finding an answer to a comparison question remains unclear. It is also unclear how questions about the internal reasoning process are useful for training and evaluating question-answering (QA) systems. To evaluate the model precisely in a hierarchical manner, we first propose a dataset, HieraDate, with three probing tasks in addition to the main question: extraction, reasoning, and robustness. Our dataset is created by enhancing two previous multi-hop datasets, HotpotQA and 2WikiMultiHopQA, focusing on multi-hop questions on date information that involve both comparison and numerical reasoning. We then evaluate the ability of existing models to understand date information. Our experimental results reveal that the multi-hop models do not have the ability to subtract two dates even when they perform well in date comparison and number subtraction tasks. Other results reveal that our probing questions can help to improve the performance of the models (e.g., by +10.3 F1) on the main QA task and our dataset can be used for data augmentation to improve the robustness of the models.","PeriodicalId":39298,"journal":{"name":"AACL Bioflux","volume":"375 1","pages":"470-479"},"PeriodicalIF":0.0,"publicationDate":"2022-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75513246","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