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Multilingual Facilitation 多语言的便利
arXiv: Computation and Language Pub Date : 2021-03-24 DOI: 10.31885/9789515150257
Khalid Alnajjar
{"title":"Multilingual Facilitation","authors":"Khalid Alnajjar","doi":"10.31885/9789515150257","DOIUrl":"https://doi.org/10.31885/9789515150257","url":null,"abstract":"This is the Festschrift of Dr. Jack Rueter. The book presents peer-reviewed scientific work from Dr. Rueter’s colleagues related to the latest advances in natural language processing, digital resources and endangered languages in a variety of languages such as historical English, Chukchi, Mansi, Erzya, Komi, Finnish, Apurina, Sign Languages, Sami languages and Japanese. Most of the papers present work on endangered languages or on domains with a limited number of resources available for NLP. This book collects original and insightful papers from well-established researchers in NLP, linguistics, philology and digital humanities. This is a tribute to Dr. Rueter’s long career that is characterized by constant altruistic work towards a greater good in building free and open-source tools and resources for endangered languages. Dr. Rueter is a true pioneer in the field of digital documentation of endangered languages.","PeriodicalId":422005,"journal":{"name":"arXiv: Computation and Language","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131269241","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
Localized Flood Detection With Minimal Labeled Social Media Data Using Transfer Learning 使用迁移学习的最小标记社会媒体数据的局部洪水检测
arXiv: Computation and Language Pub Date : 2020-02-10 DOI: 10.13016/M2OU8G-7L3X
Neha Singh, Nirmalya Roy, A. Gangopadhyay
{"title":"Localized Flood Detection With Minimal Labeled Social Media Data Using Transfer Learning","authors":"Neha Singh, Nirmalya Roy, A. Gangopadhyay","doi":"10.13016/M2OU8G-7L3X","DOIUrl":"https://doi.org/10.13016/M2OU8G-7L3X","url":null,"abstract":"Social media generates an enormous amount of data on a daily basis but it is very challenging to effectively utilize the data without annotating or labeling it according to the target application. We investigate the problem of localized flood detection using the social sensing model (Twitter) in order to provide an efficient, reliable and accurate flood text classification model with minimal labeled data. This study is important since it can immensely help in providing the flood-related updates and notifications to the city officials for emergency decision making, rescue operations, and early warnings, etc. We propose to perform the text classification using the inductive transfer learning method i.e pre-trained language model ULMFiT and fine-tune it in order to effectively classify the flood-related feeds in any new location. Finally, we show that using very little new labeled data in the target domain we can successfully build an efficient and high performing model for flood detection and analysis with human-generated facts and observations from Twitter.","PeriodicalId":422005,"journal":{"name":"arXiv: Computation and Language","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123716948","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
A Neural Approach to Discourse Relation Signal Detection. 基于神经网络的语篇关系信号检测。
arXiv: Computation and Language Pub Date : 2020-01-08 DOI: 10.5087/dad
Amir Zeldes, Yang Liu
{"title":"A Neural Approach to Discourse Relation Signal Detection.","authors":"Amir Zeldes, Yang Liu","doi":"10.5087/dad","DOIUrl":"https://doi.org/10.5087/dad","url":null,"abstract":"Previous data-driven work investigating the types and distributions of discourse relation signals, including discourse markers such as 'however' or phrases such as 'as a result' has focused on the relative frequencies of signal words within and outside text from each discourse relation. Such approaches do not allow us to quantify the signaling strength of individual instances of a signal on a scale (e.g. more or less discourse-relevant instances of 'and'), to assess the distribution of ambiguity for signals, or to identify words that hinder discourse relation identification in context ('anti-signals' or 'distractors'). In this paper we present a data-driven approach to signal detection using a distantly supervised neural network and develop a metric, Delta s (or 'delta-softmax'), to quantify signaling strength. Ranging between -1 and 1 and relying on recent advances in contextualized words embeddings, the metric represents each word's positive or negative contribution to the identifiability of a relation in specific instances in context. Based on an English corpus annotated for discourse relations using Rhetorical Structure Theory and signal type annotations anchored to specific tokens, our analysis examines the reliability of the metric, the places where it overlaps with and differs from human judgments, and the implications for identifying features that neural models may need in order to perform better on automatic discourse relation classification.","PeriodicalId":422005,"journal":{"name":"arXiv: Computation and Language","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133196440","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
DLGNet: A Transformer-based Model for Dialogue Response Generation DLGNet:基于变压器的对话响应生成模型
arXiv: Computation and Language Pub Date : 2019-07-26 DOI: 10.18653/v1/2020.nlp4convai-1.7
Olabiyi Oluwatobi, Erik T. Mueller
{"title":"DLGNet: A Transformer-based Model for Dialogue Response Generation","authors":"Olabiyi Oluwatobi, Erik T. Mueller","doi":"10.18653/v1/2020.nlp4convai-1.7","DOIUrl":"https://doi.org/10.18653/v1/2020.nlp4convai-1.7","url":null,"abstract":"Neural dialogue models, despite their successes, still suffer from lack of relevance, diversity, and in many cases coherence in their generated responses. On the other hand, transformer-based models such as GPT-2 have demonstrated an excellent ability to capture long-range structures in language modeling tasks. In this paper, we present DLGNet, a transformer-based model for dialogue modeling. We specifically examine the use of DLGNet for multi-turn dialogue response generation. In our experiments, we evaluate DLGNet on the open-domain Movie Triples dataset and the closed-domain Ubuntu Dialogue dataset. DLGNet models, although trained with only the maximum likelihood objective, achieve significant improvements over state-of-the-art multi-turn dialogue models. They also produce best performance to date on the two datasets based on several metrics, including BLEU, ROUGE, and distinct n-gram. Our analysis shows that the performance improvement is mostly due to the combination of (1) the long-range transformer architecture with (2) the injection of random informative paddings. Other contributing factors include the joint modeling of dialogue context and response, and the 100% tokenization coverage from the byte pair encoding (BPE).","PeriodicalId":422005,"journal":{"name":"arXiv: Computation and Language","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132063533","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: 25
Functorial Question Answering 功能问答
arXiv: Computation and Language Pub Date : 2019-05-17 DOI: 10.4204/EPTCS.323.6
G. Felice, K. Meichanetzidis, Alexis Toumi
{"title":"Functorial Question Answering","authors":"G. Felice, K. Meichanetzidis, Alexis Toumi","doi":"10.4204/EPTCS.323.6","DOIUrl":"https://doi.org/10.4204/EPTCS.323.6","url":null,"abstract":"We study the relational variant of the categorical compositional distributional (DisCoCat) models of Coecke et al, where we replace vector spaces and linear maps by sets and relations. We show that RelCoCat models factorise through Cartesian bicategories, as a corollary we get logspace reductions from semantics and entailment to evaluation and containment of conjunctive queries respectively. Finally, we define question answering as an NP-complete problem.","PeriodicalId":422005,"journal":{"name":"arXiv: Computation and Language","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114363918","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
Sentiment Analysis, Basic Tasks of 情感分析，基本任务
arXiv: Computation and Language Pub Date : 2017-10-18 DOI: 10.1007/978-1-4614-7163-9_110159-1
I. Chaturvedi, Soujanya Poria, E. Cambria
{"title":"Sentiment Analysis, Basic Tasks of","authors":"I. Chaturvedi, Soujanya Poria, E. Cambria","doi":"10.1007/978-1-4614-7163-9_110159-1","DOIUrl":"https://doi.org/10.1007/978-1-4614-7163-9_110159-1","url":null,"abstract":"","PeriodicalId":422005,"journal":{"name":"arXiv: Computation and Language","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114564934","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
Learning Context-Sensitive Convolutional Filters for Text Processing 学习上下文敏感的卷积过滤器文本处理
arXiv: Computation and Language Pub Date : 2017-09-25 DOI: 10.18653/v1/D18-1210
Dinghan Shen, Martin Renqiang Min, Yitong Li, L. Carin
{"title":"Learning Context-Sensitive Convolutional Filters for Text Processing","authors":"Dinghan Shen, Martin Renqiang Min, Yitong Li, L. Carin","doi":"10.18653/v1/D18-1210","DOIUrl":"https://doi.org/10.18653/v1/D18-1210","url":null,"abstract":"Convolutional neural networks (CNNs) have recently emerged as a popular building block for natural language processing (NLP). Despite their success, most existing CNN models employed in NLP share the same learned (and static) set of filters for all input sentences. In this paper, we consider an approach of using a small meta network to learn context-sensitive convolutional filters for text processing. The role of meta network is to abstract the contextual information of a sentence or document into a set of input-sensitive filters. We further generalize this framework to model sentence pairs, where a bidirectional filter generation mechanism is introduced to encapsulate co-dependent sentence representations. In our benchmarks on four different tasks, including ontology classification, sentiment analysis, answer sentence selection, and paraphrase identification, our proposed model, a modified CNN with context-sensitive filters, consistently outperforms the standard CNN and attention-based CNN baselines. By visualizing the learned context-sensitive filters, we further validate and rationalize the effectiveness of proposed framework.","PeriodicalId":422005,"journal":{"name":"arXiv: Computation and Language","volume":"174 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122998193","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: 21
Improving Named Entity Recognition for Chinese Social Media with Word Segmentation Representation Learning 基于分词表示学习的中文社交媒体命名实体识别
arXiv: Computation and Language Pub Date : 2016-03-01 DOI: 10.18653/v1/P16-2025
Nanyun Peng, Mark Dredze
{"title":"Improving Named Entity Recognition for Chinese Social Media with Word Segmentation Representation Learning","authors":"Nanyun Peng, Mark Dredze","doi":"10.18653/v1/P16-2025","DOIUrl":"https://doi.org/10.18653/v1/P16-2025","url":null,"abstract":"Named entity recognition, and other information extraction tasks, frequently use linguistic features such as part of speech tags or chunkings. For languages where word boundaries are not readily identified in text, word segmentation is a key first step to generating features for an NER system. While using word boundary tags as features are helpful, the signals that aid in identifying these boundaries may provide richer information for an NER system. New state-of-the-art word segmentation systems use neural models to learn representations for predicting word boundaries. We show that these same representations, jointly trained with an NER system, yield significant improvements in NER for Chinese social media. In our experiments, jointly training NER and word segmentation with an LSTM-CRF model yields nearly 5% absolute improvement over previously published results.","PeriodicalId":422005,"journal":{"name":"arXiv: Computation and Language","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121694351","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: 172
Modeling Coverage for Neural Machine Translation 神经机器翻译的建模覆盖
arXiv: Computation and Language Pub Date : 2016-01-19 DOI: 10.18653/v1/P16-1008
Zhaopeng Tu, Zhengdong Lu, Yang Liu, Xiaohua Liu, Hang Li
{"title":"Modeling Coverage for Neural Machine Translation","authors":"Zhaopeng Tu, Zhengdong Lu, Yang Liu, Xiaohua Liu, Hang Li","doi":"10.18653/v1/P16-1008","DOIUrl":"https://doi.org/10.18653/v1/P16-1008","url":null,"abstract":"Attention mechanism has enhanced state-of-the-art Neural Machine Translation (NMT) by jointly learning to align and translate. It tends to ignore past alignment information, however, which often leads to over-translation and under-translation. To address this problem, we propose coverage-based NMT in this paper. We maintain a coverage vector to keep track of the attention history. The coverage vector is fed to the attention model to help adjust future attention, which lets NMT system to consider more about untranslated source words. Experiments show that the proposed approach significantly improves both translation quality and alignment quality over standard attention-based NMT.","PeriodicalId":422005,"journal":{"name":"arXiv: Computation and Language","volume":"272 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133974141","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: 703
Natural Language Inference by Tree-Based Convolution and Heuristic Matching 基于树的卷积和启发式匹配的自然语言推理
arXiv: Computation and Language Pub Date : 2015-12-28 DOI: 10.18653/v1/P16-2022
Lili Mou, Rui Men, Ge Li, Yan Xu, Lu Zhang, Rui Yan, Zhi Jin
{"title":"Natural Language Inference by Tree-Based Convolution and Heuristic Matching","authors":"Lili Mou, Rui Men, Ge Li, Yan Xu, Lu Zhang, Rui Yan, Zhi Jin","doi":"10.18653/v1/P16-2022","DOIUrl":"https://doi.org/10.18653/v1/P16-2022","url":null,"abstract":"In this paper, we propose the TBCNN-pair model to recognize entailment and contradiction between two sentences. In our model, a tree-based convolutional neural network (TBCNN) captures sentence-level semantics; then heuristic matching layers like concatenation, element-wise product/difference combine the information in individual sentences. Experimental results show that our model outperforms existing sentence encoding-based approaches by a large margin.","PeriodicalId":422005,"journal":{"name":"arXiv: Computation and Language","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131055900","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: 334
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