The FinSim-2 is a second edition of FinSim Shared Task on Learning Semantic Similarities for the Financial Domain, colocated with the FinWeb workshop. FinSim-2 proposed the challenge to automatically learn effective and precise semantic models for the financial domain. The second edition of the FinSim offered an enriched dataset in terms of volume and quality, and interested in systems which make creative use of relevant resources such as ontologies and lexica, as well as systems which make use of contextual word embeddings such as BERT[4]. Going beyond the mere representation of words is a key step to industrial applications that make use of Natural Language Processing (NLP). This is typically addressed using either unsupervised corpus-derived representations like word embeddings, which are typically opaque to human understanding but very useful in NLP applications or manually created resources such as taxonomies and ontologies, which typically have low coverage and contain inconsistencies, but provide a deeper understanding of the target domain. Finsim is inspired from previous endeavours in the Semeval community, which organized several competitions on semantic/lexical relation extraction between concepts/words. This year, 18 system runs were submitted by 7 teams and systems were ranked according to 2 metrics, Accuracy and Mean rank. All the systems beat our baseline 1 model by over 15 points and the best systems beat the baseline 2 by over 1 ∼ 3 points in accuracy.
{"title":"The FinSim-2 2021 Shared Task: Learning Semantic Similarities for the Financial Domain","authors":"Youness Mansar, Juyeon Kang, Ismaïl El Maarouf","doi":"10.1145/3442442.3451381","DOIUrl":"https://doi.org/10.1145/3442442.3451381","url":null,"abstract":"The FinSim-2 is a second edition of FinSim Shared Task on Learning Semantic Similarities for the Financial Domain, colocated with the FinWeb workshop. FinSim-2 proposed the challenge to automatically learn effective and precise semantic models for the financial domain. The second edition of the FinSim offered an enriched dataset in terms of volume and quality, and interested in systems which make creative use of relevant resources such as ontologies and lexica, as well as systems which make use of contextual word embeddings such as BERT[4]. Going beyond the mere representation of words is a key step to industrial applications that make use of Natural Language Processing (NLP). This is typically addressed using either unsupervised corpus-derived representations like word embeddings, which are typically opaque to human understanding but very useful in NLP applications or manually created resources such as taxonomies and ontologies, which typically have low coverage and contain inconsistencies, but provide a deeper understanding of the target domain. Finsim is inspired from previous endeavours in the Semeval community, which organized several competitions on semantic/lexical relation extraction between concepts/words. This year, 18 system runs were submitted by 7 teams and systems were ranked according to 2 metrics, Accuracy and Mean rank. All the systems beat our baseline 1 model by over 15 points and the best systems beat the baseline 2 by over 1 ∼ 3 points in accuracy.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116895307","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}
Evangelos Maliaroudakis, K. Boland, S. Dietze, Konstantin Todorov, Yannis Tzitzikas, P. Fafalios
We present ClaimLinker, a Web service and API that links arbitrary text to a knowledge graph of fact-checked claims, offering a novel kind of semantic annotation of unstructured content. Given a text, ClaimLinker matches parts of it to fact-checked claims mined from popular fact-checking sites and integrated into a rich knowledge graph, thus allowing the further exploration of the linked claims and their associations. The application is based on a scalable, fully unsupervised and modular approach that does not require training or tuning and which can serve high quality results at real time (outperforming existing unsupervised methods). This allows its easy deployment for different contexts and application scenarios.
{"title":"ClaimLinker: Linking Text to a Knowledge Graph of Fact-checked Claims","authors":"Evangelos Maliaroudakis, K. Boland, S. Dietze, Konstantin Todorov, Yannis Tzitzikas, P. Fafalios","doi":"10.1145/3442442.3458601","DOIUrl":"https://doi.org/10.1145/3442442.3458601","url":null,"abstract":"We present ClaimLinker, a Web service and API that links arbitrary text to a knowledge graph of fact-checked claims, offering a novel kind of semantic annotation of unstructured content. Given a text, ClaimLinker matches parts of it to fact-checked claims mined from popular fact-checking sites and integrated into a rich knowledge graph, thus allowing the further exploration of the linked claims and their associations. The application is based on a scalable, fully unsupervised and modular approach that does not require training or tuning and which can serve high quality results at real time (outperforming existing unsupervised methods). This allows its easy deployment for different contexts and application scenarios.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121123010","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}
Formulation of carpooling schemes for mutual cost benefits between the driver and the passengers has a long history. However, the convenience of driving alone, especially under the current COVID-19 pandemic, the increase of car ownership and the difficulties in finding travelers with matching schedule and route keeps car occupancy low. The technology is a key enabler of online platforms which facilitate the ride matching process and lead the increase of carpooling services. The aim of this work-in-progress article is to clarify the value proposition of carpooling platforms in smart cities, especially under conditions like the pandemic. Thus, an extensive bibliometric analysis of three separate specialized literature collections using the bibliometrix R-Tool combined with a systematic literature review of selected papers is performed. It is identified that smart carpooling platforms could generate additional value for participants and smart cities with real-time ride matching, interconnection with public transportation and other city services, secure transactions, reputation-based services and closed organization carpooling schemes. To deliver this value to a smart city, a multi-sided platform business model is proposed, suitable for a carpooling service provider with multiple customer segments and partners.
{"title":"Carpooling Platforms in Smart Cities for COVID-19 Pandemic: A Bibliometric Analysis","authors":"Leonidas G. Anthopoulos, Dimitrios Tzimos","doi":"10.1145/3442442.3453471","DOIUrl":"https://doi.org/10.1145/3442442.3453471","url":null,"abstract":"Formulation of carpooling schemes for mutual cost benefits between the driver and the passengers has a long history. However, the convenience of driving alone, especially under the current COVID-19 pandemic, the increase of car ownership and the difficulties in finding travelers with matching schedule and route keeps car occupancy low. The technology is a key enabler of online platforms which facilitate the ride matching process and lead the increase of carpooling services. The aim of this work-in-progress article is to clarify the value proposition of carpooling platforms in smart cities, especially under conditions like the pandemic. Thus, an extensive bibliometric analysis of three separate specialized literature collections using the bibliometrix R-Tool combined with a systematic literature review of selected papers is performed. It is identified that smart carpooling platforms could generate additional value for participants and smart cities with real-time ride matching, interconnection with public transportation and other city services, secure transactions, reputation-based services and closed organization carpooling schemes. To deliver this value to a smart city, a multi-sided platform business model is proposed, suitable for a carpooling service provider with multiple customer segments and partners.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123791248","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}
The rise of deep learning methods has transformed the research area of natural language processing beyond recognition. New benchmark performances are reported on a daily basis ranging from machine translation to question-answering. Yet, some of the unsolved practical research questions are not in the spotlight and this includes, for example, issues arising at the interface between spoken and written language processing. We identify sentence boundary detection and speaker change detection applied to automatically transcribed texts as two NLP problems that have not yet received much attention but are nevertheless of practical relevance. We frame both problems as binary tagging tasks that can be addressed by fine-tuning a transformer model and we report promising results.
{"title":"Making Sense of Subtitles: Sentence Boundary Detection and Speaker Change Detection in Unpunctuated Texts","authors":"Udo Kruschwitz, Gregor Donabauer, D. Corney","doi":"10.1145/3442442.3451894","DOIUrl":"https://doi.org/10.1145/3442442.3451894","url":null,"abstract":"The rise of deep learning methods has transformed the research area of natural language processing beyond recognition. New benchmark performances are reported on a daily basis ranging from machine translation to question-answering. Yet, some of the unsolved practical research questions are not in the spotlight and this includes, for example, issues arising at the interface between spoken and written language processing. We identify sentence boundary detection and speaker change detection applied to automatically transcribed texts as two NLP problems that have not yet received much attention but are nevertheless of practical relevance. We frame both problems as binary tagging tasks that can be addressed by fine-tuning a transformer model and we report promising results.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116100759","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}
Kuan-Chieh Lo, Shih-Chieh Dai, Aiping Xiong, Jing Jiang, Lun-Wei Ku
An echo chamber effect refers to the phenomena that online users revealed selective exposure and ideological segregation on political issues. Prior studies indicate the connection between the spread of misinformation and online echo chambers. In this paper, to help users escape from an echo chamber, we propose a novel news-analysis platform that provides a panoramic view of stances towards a particular event from different news media sources. Moreover, to help users better recognize the stances of news sources which published these news articles, we adopt a news stance classification model to categorize their stances into “agree”, “disagree”, “discuss”, or “unrelated” to a relevant claim for specified events with political stances. Finally, we proposed two ways showing the echo chamber effects: 1) visualizing the event and the associated pieces of news; and 2) visualizing the stance distribution of news from news sources of different political ideology. By making the echo chamber effect explicit, we expect online users will become exposed to more diverse perspectives toward a specific event. The demo video of our platform is available on youtube1.
{"title":"Escape from An Echo Chamber","authors":"Kuan-Chieh Lo, Shih-Chieh Dai, Aiping Xiong, Jing Jiang, Lun-Wei Ku","doi":"10.1145/3442442.3458613","DOIUrl":"https://doi.org/10.1145/3442442.3458613","url":null,"abstract":"An echo chamber effect refers to the phenomena that online users revealed selective exposure and ideological segregation on political issues. Prior studies indicate the connection between the spread of misinformation and online echo chambers. In this paper, to help users escape from an echo chamber, we propose a novel news-analysis platform that provides a panoramic view of stances towards a particular event from different news media sources. Moreover, to help users better recognize the stances of news sources which published these news articles, we adopt a news stance classification model to categorize their stances into “agree”, “disagree”, “discuss”, or “unrelated” to a relevant claim for specified events with political stances. Finally, we proposed two ways showing the echo chamber effects: 1) visualizing the event and the associated pieces of news; and 2) visualizing the stance distribution of news from news sources of different political ideology. By making the echo chamber effect explicit, we expect online users will become exposed to more diverse perspectives toward a specific event. The demo video of our platform is available on youtube1.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122847789","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}
{"title":"Wikidata Logical Rules and Where to Find Them","authors":"N. Ahmadi, Paolo Papotti","doi":"10.1145/3442442.3452343","DOIUrl":"https://doi.org/10.1145/3442442.3452343","url":null,"abstract":"","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122195850","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}
News recommendation is very crucial for online news services to improve user experience and alleviate information overload. Precisely learning representations of news and users is the core problem in news recommendation. Existing models usually focus on implicit text information to learn corresponding representations, which may be insufficient for modeling user interests. Even if entity information is considered from external knowledge, it may still not be used explicitly and effectively for user modeling. In this paper, we propose a novel news recommendation approach, which combine explicit entity graph with implicit text information. The entity graph consists of two types of nodes and three kinds of edges, which represent chronological order, related and affiliation relationship. Then graph neural network is utilized for reasoning on these nodes. Extensive experiments on a real-world dataset, Microsoft News Dataset (MIND), validate the effectiveness of our proposed approach.
{"title":"Combining Explicit Entity Graph with Implicit Text Information for News Recommendation","authors":"Xuanyu Zhang, Qing Yang, Dongliang Xu","doi":"10.1145/3442442.3452329","DOIUrl":"https://doi.org/10.1145/3442442.3452329","url":null,"abstract":"News recommendation is very crucial for online news services to improve user experience and alleviate information overload. Precisely learning representations of news and users is the core problem in news recommendation. Existing models usually focus on implicit text information to learn corresponding representations, which may be insufficient for modeling user interests. Even if entity information is considered from external knowledge, it may still not be used explicitly and effectively for user modeling. In this paper, we propose a novel news recommendation approach, which combine explicit entity graph with implicit text information. The entity graph consists of two types of nodes and three kinds of edges, which represent chronological order, related and affiliation relationship. Then graph neural network is utilized for reasoning on these nodes. Extensive experiments on a real-world dataset, Microsoft News Dataset (MIND), validate the effectiveness of our proposed approach.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129536429","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}
The visual analysis of temporal network data is often hindered by the cognitively demanding nature of dynamic graphic visualizations. Addressing this issue, the graph visualization tool HOTVis generates time-aware static network visualizations that highlight the causal topology of temporal networks, i.e. which nodes can directly and indirectly influence each other, and are thus considerably easier to interpret than state-of-the-art dynamic graph visualizations.
{"title":"Visualisation of Temporal Network Data via Time-Aware Static Representations with HOTVis","authors":"Vincenzo Perri, Ingo Scholtes","doi":"10.1145/3442442.3452053","DOIUrl":"https://doi.org/10.1145/3442442.3452053","url":null,"abstract":"The visual analysis of temporal network data is often hindered by the cognitively demanding nature of dynamic graphic visualizations. Addressing this issue, the graph visualization tool HOTVis generates time-aware static network visualizations that highlight the causal topology of temporal networks, i.e. which nodes can directly and indirectly influence each other, and are thus considerably easier to interpret than state-of-the-art dynamic graph visualizations.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130558410","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}
Robert West, Smriti Bhagat, Paul Groth, M. Zitnik, Francisco M. Couto, Pasquale Lisena, Albert Meroño-Peñuela, Xiangyu Zhao, Wenqi Fan, Dawei Yin, Jiliang Tang, Linjun Shou, Ming Gong, J. Pei, Xiubo Geng, Xingjie Zhou, Daxin Jiang, B. Ricaud, Nicolas Aspert, Volodymyr Miz, Jennifer G. Dy, Stratis Ioannidis, Ilkay Yildiz, R. Rezapour, Samin Aref, Ly Dinh, J. Diesner, Alexey Drutsa, Dmitry Ustalov, N. Popov, Daria Baidakova, Shubhanshu Mishra, Arjun Gopalan, Da-Cheng Juan, Cesar Ilharco Magalhaes, Chun-Sung Ferng, Allan Heydon, Chun-Ta Lu, Philip Pham, George Yu, Yicheng Fan, Yueqi Wang, Florian Laurent, Yanick Schraner, C. Scheller, S. Mohanty, Jiawei Chen, Xiang Wang, Fuli Feng, Xiangnan He, Irene Teinemaa, Javier Albert, Dmitri Goldenberg, Flavian Vasile, D. Rohde, Olivier Jeunen, Amine Benhalloum, Otmane Sakhi, Yu Rong, Wen-bing Huang, Tingyang Xu, Yatao Bian, Hongying Cheng, Fuchun Sun, Junzhou Huang, Shobeir Fakhraei, C. Faloutsos, Onur Çelebi, Martin Müller, Manuel Schneider, Olesia Altunina, Wolfram
This report summarizes the 23 tutorials hosted at The Web Conference 2021: nine lecture-style tutorials and 14 hands-on tutorials.
本报告总结了在the Web Conference 2021上举办的23个教程:9个讲座式教程和14个动手教程。
{"title":"Summary of Tutorials at The Web Conference 2021","authors":"Robert West, Smriti Bhagat, Paul Groth, M. Zitnik, Francisco M. Couto, Pasquale Lisena, Albert Meroño-Peñuela, Xiangyu Zhao, Wenqi Fan, Dawei Yin, Jiliang Tang, Linjun Shou, Ming Gong, J. Pei, Xiubo Geng, Xingjie Zhou, Daxin Jiang, B. Ricaud, Nicolas Aspert, Volodymyr Miz, Jennifer G. Dy, Stratis Ioannidis, Ilkay Yildiz, R. Rezapour, Samin Aref, Ly Dinh, J. Diesner, Alexey Drutsa, Dmitry Ustalov, N. Popov, Daria Baidakova, Shubhanshu Mishra, Arjun Gopalan, Da-Cheng Juan, Cesar Ilharco Magalhaes, Chun-Sung Ferng, Allan Heydon, Chun-Ta Lu, Philip Pham, George Yu, Yicheng Fan, Yueqi Wang, Florian Laurent, Yanick Schraner, C. Scheller, S. Mohanty, Jiawei Chen, Xiang Wang, Fuli Feng, Xiangnan He, Irene Teinemaa, Javier Albert, Dmitri Goldenberg, Flavian Vasile, D. Rohde, Olivier Jeunen, Amine Benhalloum, Otmane Sakhi, Yu Rong, Wen-bing Huang, Tingyang Xu, Yatao Bian, Hongying Cheng, Fuchun Sun, Junzhou Huang, Shobeir Fakhraei, C. Faloutsos, Onur Çelebi, Martin Müller, Manuel Schneider, Olesia Altunina, Wolfram","doi":"10.1145/3442442.3453701","DOIUrl":"https://doi.org/10.1145/3442442.3453701","url":null,"abstract":"This report summarizes the 23 tutorials hosted at The Web Conference 2021: nine lecture-style tutorials and 14 hands-on tutorials.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126226859","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}
Human knowledge and use of language is inextricably connected to perception, action and the organization of the brain, yet natural language processing is still dominated by text! More research involving language-including speech-in the context of other modalities and environments is needed, and there has never been a better time to do it. Without ever invoking the worn-out, overblown phrase ”how babies learn” in the talk, I’ll cover three of my team’s efforts involving language, vision and action. First: our work on speech-image representation learning and retrieval, where we demonstrate settings in which directly encoding speech outperforms the hard-to-beat strategy of using automatic speech recognition and strong text encoders. Second: two models for text-to-image generation: a multi-stage model which exploits user-guidance in the form of mouse traces and a single-stage one which uses cross-modal contrastive losses. Third: Room-across-Room, a multilingual dataset for vision-and-language navigation, for which we collected spoken navigation instructions, high-quality text transcriptions, and fine-grained alignments between words and pixels in high-definition 360-degree panoramas. I’ll wrap up with some thoughts on how work on computational language grounding more broadly presents new opportunities to enhance and advance our scientific understanding of language and its fundamental role in human intelligence.
{"title":"Language, Vision and Action are Better Together","authors":"Jason Baldridge","doi":"10.1145/3442442.3451897","DOIUrl":"https://doi.org/10.1145/3442442.3451897","url":null,"abstract":"Human knowledge and use of language is inextricably connected to perception, action and the organization of the brain, yet natural language processing is still dominated by text! More research involving language-including speech-in the context of other modalities and environments is needed, and there has never been a better time to do it. Without ever invoking the worn-out, overblown phrase ”how babies learn” in the talk, I’ll cover three of my team’s efforts involving language, vision and action. First: our work on speech-image representation learning and retrieval, where we demonstrate settings in which directly encoding speech outperforms the hard-to-beat strategy of using automatic speech recognition and strong text encoders. Second: two models for text-to-image generation: a multi-stage model which exploits user-guidance in the form of mouse traces and a single-stage one which uses cross-modal contrastive losses. Third: Room-across-Room, a multilingual dataset for vision-and-language navigation, for which we collected spoken navigation instructions, high-quality text transcriptions, and fine-grained alignments between words and pixels in high-definition 360-degree panoramas. I’ll wrap up with some thoughts on how work on computational language grounding more broadly presents new opportunities to enhance and advance our scientific understanding of language and its fundamental role in human intelligence.","PeriodicalId":129420,"journal":{"name":"Companion Proceedings of the Web Conference 2021","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131751506","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}
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