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C16-3001 | Compositional Distributional Models of Meaning | Compositional distributional models of meaning (CDMs) provide a function that produces a vectorial representation for a phrase or a sentence by composing the vectors of its words. Being the natural evolution of the traditional and well-studied distributional models at the word level, CDMs are steadily evolving to a pop... | 2,016 | https://aclanthology.org/C16-3001/ | COLING | [{'id': 8360910, 'paperId': '37efe2ef1b9d27cc598361a8013ec888a6f7c4d8', 'title': 'Nouns are Vectors, Adjectives are Matrices: Representing Adjective-Noun Constructions in Semantic Space', 'authors': [{'authorId': '145283199', 'name': 'Marco Baroni'}, {'authorId': '2713535', 'name': 'Roberto Zamparelli'}], 'venue': 'Con... |
P19-4004 | Computational Analysis of Political Texts: Bridging Research Efforts Across Communities | In the last twenty years, political scientists started adopting and developing natural language processing (NLP) methods more actively in order to exploit text as an additional source of data in their analyses. Over the last decade the usage of computational methods for analysis of political texts has drastically expan... | 2,019 | https://aclanthology.org/P19-4004/ | ACL | [{'id': 16196219, 'paperId': 'b9921fb4d1448058642897797e77bdaf8f444404', 'title': 'Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts', 'authors': [{'authorId': '2361828', 'name': 'Justin Grimmer'}, {'authorId': '28924497', 'name': 'Brandon M Stewart'}], 'venue': 'Political... |
2020.acl-tutorials.1 | Interpretability and Analysis in Neural NLP | While deep learning has transformed the natural language processing (NLP) field and impacted the larger computational linguistics community, the rise of neural networks is stained by their opaque nature: It is challenging to interpret the inner workings of neural network models, and explicate their behavior. Therefore,... | 2,020 | https://aclanthology.org/2020.acl-tutorials.1 | ACL | [{'id': 56657817, 'paperId': '668f42a4d4094f0a66d402a16087e14269b31a1f', 'title': 'Analysis Methods in Neural Language Processing: A Survey', 'authors': [{'authorId': '2083259', 'name': 'Yonatan Belinkov'}, {'authorId': '145898106', 'name': 'James R. Glass'}], 'venue': 'Transactions of the Association for Computational... |
2020.acl-tutorials.2 | Integrating Ethics into the NLP Curriculum | To raise awareness among future NLP practitioners and prevent inertia in the field, we need to place ethics in the curriculum for all NLP students—not as an elective, but as a core part of their education. Our goal in this tutorial is to empower NLP researchers and practitioners with tools and resources to teach others... | 2,020 | https://aclanthology.org/2020.acl-tutorials.2 | ACL | [{'id': 26039972, 'paperId': '0e661bd2cfe94ed58e4e2abc1409c75b98c2582c', 'title': 'Dual use and the ethical responsibility of scientists', 'authors': [{'authorId': '3920554', 'name': 'Hans-Jörg Ehni'}], 'venue': 'Archivum Immunologiae et Therapiae Experimentalis', 'abstract': 'The main normative problem in the context ... |
2020.acl-tutorials.3 | Achieving Common Ground in Multi-modal Dialogue | All communication aims at achieving common ground (grounding): interlocutors can work together effectively only with mutual beliefs about what the state of the world is, about what their goals are, and about how they plan to make their goals a reality. Computational dialogue research offers some classic results on grou... | 2,020 | https://aclanthology.org/2020.acl-tutorials.3 | ACL | [{'id': 153811205, 'paperId': '5a9cac54de14e58697d0315fe3c01f3dbe69c186', 'title': 'Grounding in communication', 'authors': [{'authorId': '29224904', 'name': 'H. H. Clark'}, {'authorId': '71463834', 'name': 'S. Brennan'}], 'venue': 'Perspectives on socially shared cognition', 'abstract': "GROUNDING It takes two people ... |
2020.acl-tutorials.4 | Reviewing Natural Language Processing Research | This tutorial will cover the theory and practice of reviewing research in natural language processing. Heavy reviewing burdens on natural language processing researchers have made it clear that our community needs to increase the size of our pool of potential reviewers. Simultaneously, notable “false negatives”—rejecti... | 2,020 | https://aclanthology.org/2020.acl-tutorials.4 | ACL | [{'id': 154339, 'paperId': '33ff45f364dac785b8bd4e3bf70fb169dc1d39b4', 'title': "Who's afraid of peer review?", 'authors': [{'authorId': '145179131', 'name': 'J. Bohannon'}], 'venue': 'Science', 'abstract': 'Dozens of open-access journals targeted in an elaborate Science sting accepted a spoof research article, raising... |
2020.acl-tutorials.6 | Multi-modal Information Extraction from Text, Semi-structured, and Tabular Data on the Web | The World Wide Web contains vast quantities of textual information in several forms: unstructured text, template-based semi-structured webpages (which present data in key-value pairs and lists), and tables. Methods for extracting information from these sources and converting it to a structured form have been a target o... | 2,020 | https://aclanthology.org/2020.acl-tutorials.6 | ACL | [{'id': 13091007, 'paperId': '7a12502ba5b9686e37b0ec9d86a2dc7f4b7022ac', 'title': 'Web-scale information extraction with vertex', 'authors': [{'authorId': '2627799', 'name': 'P. Gulhane'}, {'authorId': '2136102', 'name': 'Amit Madaan'}, {'authorId': '3259494', 'name': 'Rupesh R. Mehta'}, {'authorId': '2311735', 'name':... |
2020.acl-tutorials.7 | Commonsense Reasoning for Natural Language Processing | Commonsense knowledge, such as knowing that “bumping into people annoys them” or “rain makes the road slippery”, helps humans navigate everyday situations seamlessly. Yet, endowing machines with such human-like commonsense reasoning capabilities has remained an elusive goal of artificial intelligence research for decad... | 2,020 | https://aclanthology.org/2020.acl-tutorials.7 | ACL | [{'id': 91184338, 'paperId': 'b1832b749528755dfcbe462717f4f5afc07243b8', 'title': 'Commonsense Reasoning for Natural Language Understanding: A Survey of Benchmarks, Resources, and Approaches', 'authors': [{'authorId': '89093987', 'name': 'Shane Storks'}, {'authorId': '3193409', 'name': 'Qiaozi Gao'}, {'authorId': '1707... |
2020.coling-tutorials.1 | Cross-lingual Semantic Representation for NLP with UCCA | This is an introductory tutorial to UCCA (Universal Conceptual Cognitive Annotation), a cross-linguistically applicable framework for semantic representation, with corpora annotated in English, German and French, and ongoing annotation in Russian and Hebrew. UCCA builds on extensive typological work and supports rapid ... | 2,020 | https://aclanthology.org/2020.coling-tutorials.1/ | COLING | [{'id': 60742189, 'paperId': '54ffc8f1cb11ec21eb14a6706b8b6d9b192a1b32', 'title': 'A Semantic Approach to English Grammar', 'authors': [{'authorId': '34256957', 'name': 'R. Dixon'}], 'venue': '', 'abstract': 'This book shows how grammar helps people communicate and looks at the ways grammar and meaning interrelate. The... |
2020.coling-tutorials.2 | Embeddings in Natural Language Processing | Embeddings have been one of the most important topics of interest in NLP for the past decade. Representing knowledge through a low-dimensional vector which is easily integrable in modern machine learning models has played a central role in the development of the field. Embedding techniques initially focused on words bu... | 2,020 | https://aclanthology.org/2020.coling-tutorials.2/ | COLING | [{'id': 15829786, 'paperId': 'e569d99f3a0fcfa038631dda2b44c73a6e8e97b8', 'title': 'Dimensions of meaning', 'authors': [{'authorId': '144418438', 'name': 'Hinrich Schütze'}], 'venue': "Supercomputing '92", 'abstract': 'The representation of documents and queries as vectors in a high-dimensional space is well-established... |
2020.coling-tutorials.5 | A guide to the dataset explosion in QA, NLI, and commonsense reasoning | Question answering, natural language inference and commonsense reasoning are increasingly popular as general NLP system benchmarks, driving both modeling and dataset work. Only for question answering we already have over 100 datasets, with over 40 published after 2018. However, most new datasets get “solved” soon after... | 2,020 | https://aclanthology.org/2020.coling-tutorials.5/ | COLING | [{'id': 182952898, 'paperId': 'a1f000b88e81f02b2a0d7a4097171428364af8c7', 'title': 'A Survey on Neural Machine Reading Comprehension', 'authors': [{'authorId': '2064466537', 'name': 'Boyu Qiu'}, {'authorId': '2118183867', 'name': 'Xu Chen'}, {'authorId': '2073589', 'name': 'Jungang Xu'}, {'authorId': '46676156', 'name'... |
2020.coling-tutorials.6 | A Crash Course in Automatic Grammatical Error Correction | Grammatical Error Correction (GEC) is the task of automatically detecting and correcting all types of errors in written text. Although most research has focused on correcting errors in the context of English as a Second Language (ESL), GEC can also be applied to other languages and native text. The main application of ... | 2,020 | https://aclanthology.org/2020.coling-tutorials.6/ | COLING | [{'id': 219306476, 'paperId': '20499f3c6fe9f84a12c9def941e2e12846a00c77', 'title': 'The CoNLL-2014 Shared Task on Grammatical Error Correction', 'authors': [{'authorId': '34789794', 'name': 'H. Ng'}, {'authorId': '2069266', 'name': 'S. Wu'}, {'authorId': '145693410', 'name': 'Ted Briscoe'}, {'authorId': '3271719', 'nam... |
2020.coling-tutorials.7 | Endangered Languages meet Modern NLP | This tutorial will focus on NLP for endangered languages documentation and revitalization. First, we will acquaint the attendees with the process and the challenges of language documentation, showing how the needs of the language communities and the documentary linguists map to specific NLP tasks. We will then present ... | 2,020 | https://aclanthology.org/2020.coling-tutorials.7/ | COLING | [{'id': 48356442, 'paperId': 'b4aa5354e88564b2e4eeee3019ed04e5388042f3', 'title': 'Challenges of language technologies for the indigenous languages of the Americas', 'authors': [{'authorId': '153151470', 'name': 'Manuel Mager'}, {'authorId': '1409305289', 'name': 'Ximena Gutierrez-Vasques'}, {'authorId': '32889164', 'n... |
2020.emnlp-tutorials.1 | Machine Reasoning: Technology, Dilemma and Future | Machine reasoning research aims to build interpretable AI systems that can solve problems or draw conclusions from what they are told (i.e. facts and observations) and already know (i.e. models, common sense and knowledge) under certain constraints. In this tutorial, we will (1) describe the motivation of this tutorial... | 2,020 | https://aclanthology.org/2020.emnlp-tutorials.1 | EMNLP | [{'id': 63671278, 'paperId': '20394c89e24d9060ecc69b8a58bdab7833c5b5bd', 'title': 'Markov Logic: A Unifying Framework for Statistical Relational Learning', 'authors': [{'authorId': '1746034', 'name': 'L. Getoor'}, {'authorId': '1685978', 'name': 'B. Taskar'}], 'venue': '', 'abstract': 'This chapter contains sections ti... |
2020.emnlp-tutorials.2 | Fact-Checking, Fake News, Propaganda, and Media Bias: Truth Seeking in the Post-Truth Era | The rise of social media has democratized content creation and has made it easy for everybody to share and spread information online. On the positive side, this has given rise to citizen journalism, thus enabling much faster dissemination of information compared to what was possible with newspapers, radio, and TV. On t... | 2,020 | https://aclanthology.org/2020.emnlp-tutorials.2 | EMNLP | [{'id': 207718082, 'paperId': 'cb40a5e6d4fc0290452345791bb91040aed76961', 'title': 'Fake News Detection on Social Media: A Data Mining Perspective', 'authors': [{'authorId': '145800151', 'name': 'Kai Shu'}, {'authorId': '2880010', 'name': 'A. Sliva'}, {'authorId': '2893721', 'name': 'Suhang Wang'}, {'authorId': '173663... |
2020.emnlp-tutorials.3 | Interpreting Predictions of NLP Models | Although neural NLP models are highly expressive and empirically successful, they also systematically fail in counterintuitive ways and are opaque in their decision-making process. This tutorial will provide a background on interpretation techniques, i.e., methods for explaining the predictions of NLP models. We will f... | 2,020 | https://aclanthology.org/2020.emnlp-tutorials.3 | EMNLP | [{'id': 11319376, 'paperId': '5c39e37022661f81f79e481240ed9b175dec6513', 'title': 'Towards A Rigorous Science of Interpretable Machine Learning', 'authors': [{'authorId': '1388372395', 'name': 'F. Doshi-Velez'}, {'authorId': '3351164', 'name': 'Been Kim'}], 'venue': '', 'abstract': 'As machine learning systems become u... |
2020.emnlp-tutorials.5 | Representation, Learning and Reasoning on Spatial Language for Downstream NLP Tasks | Understating spatial semantics expressed in natural language can become highly complex in real-world applications. This includes applications of language grounding, navigation, visual question answering, and more generic human-machine interaction and dialogue systems. In many of such downstream tasks, explicit represen... | 2,020 | https://aclanthology.org/2020.emnlp-tutorials.5 | EMNLP | [{'id': 5705211, 'paperId': '946dabbc13f06070f7618cd4ca6733a95b4b03c3', 'title': 'A linguistic ontology of space for natural language processing', 'authors': [{'authorId': '2242089300', 'name': 'John A. Bateman'}, {'authorId': '3176893', 'name': 'J. Hois'}, {'authorId': '2323115674', 'name': 'Robert J. Ross'}, {'author... |
2020.emnlp-tutorials.6 | Simultaneous Translation | Simultaneous translation, which performs translation concurrently with the source speech, is widely useful in many scenarios such as international conferences, negotiations, press releases, legal proceedings, and medicine. This problem has long been considered one of the hardest problems in AI and one of its holy grail... | 2,020 | https://aclanthology.org/2020.emnlp-tutorials.6 | EMNLP | [{'id': 216638592, 'paperId': 'b7adef89d7a0e7b743ab098d583a90b1cbfc6de7', 'title': "Don't Until the Final Verb Wait: Reinforcement Learning for Simultaneous Machine Translation", 'authors': [{'authorId': '2778913', 'name': 'Alvin Grissom II'}, {'authorId': '144533687', 'name': 'He He'}, {'authorId': '1389036863', 'name... |
2020.emnlp-tutorials.7 | The Amazing World of Neural Language Generation | Neural Language Generation (NLG) – using neural network models to generate coherent text – is among the most promising methods for automated text creation. Recent years have seen a paradigm shift in neural text generation, caused by the advances in deep contextual language modeling (e.g., LSTMs, GPT, GPT2) and transfer... | 2,020 | https://aclanthology.org/2020.emnlp-tutorials.7 | EMNLP | [{'id': 16946362, 'paperId': 'd13bb317e87f3f6da10da11059ebf4350b754814', 'title': 'Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation', 'authors': [{'authorId': '1700894', 'name': 'Albert Gatt'}, {'authorId': '2297195264', 'name': 'E. Krahmer'}], 'venue': 'Journal of ... |
2021.acl-tutorials.1 | Advances in Debating Technologies: Building AI That Can Debate Humans | The tutorial focuses on Debating Technologies, a sub-field of computational argumentation defined as “computational technologies developed directly to enhance, support, and engage with human debating” (Gurevych et al., 2016). A recent milestone in this field is Project Debater, which was revealed in 2019 as the first A... | 2,021 | https://aclanthology.org/2021.acl-tutorials.1 | ACL, IJCNLP | [{'id': 203912051, 'paperId': 'a2ae7155d94686fe83f26f6d6ca2dfacd16c5e5c', 'title': 'Argument Mining: A Survey', 'authors': [{'authorId': '2055083035', 'name': 'J. Lawrence'}, {'authorId': '145989424', 'name': 'C. Reed'}], 'venue': 'Computational Linguistics', 'abstract': 'Argument mining is the automatic identification... |
2021.acl-tutorials.2 | Event-Centric Natural Language Processing | This tutorial targets researchers and practitioners who are interested in AI technologies that help machines understand natural language text, particularly real-world events described in the text. These include methods to extract the internal structures of an event regarding its protagonist(s), participant(s) and prope... | 2,021 | https://aclanthology.org/2021.acl-tutorials.2 | ACL, IJCNLP | [{'id': 60498119, 'paperId': 'e8eb363f3d87aaec3bb7d1f74917e21724123b93', 'title': 'The algebra of events', 'authors': [{'authorId': '144282503', 'name': 'Emmon Bach'}], 'venue': 'The Language of Time - A Reader', 'abstract': 'A number of writers have commented on the close parallels between the mass-count distinction i... |
2021.acl-tutorials.3 | Meta Learning and Its Applications to Natural Language Processing | Deep learning based natural language processing (NLP) has become the mainstream of research in recent years and significantly outperforms conventional methods. However, deep learning models are notorious for being data and computation hungry. These downsides limit the application of such models from deployment to diffe... | 2,021 | https://aclanthology.org/2021.acl-tutorials.3 | ACL, IJCNLP | [{'id': 6719686, 'paperId': 'c889d6f98e6d79b89c3a6adf8a921f88fa6ba518', 'title': 'Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks', 'authors': [{'authorId': '46881670', 'name': 'Chelsea Finn'}, {'authorId': '1689992', 'name': 'P. Abbeel'}, {'authorId': '1736651', 'name': 'S. Levine'}], 'venue': 'Inter... |
2021.acl-tutorials.4 | Pre-training Methods for Neural Machine Translation | This tutorial provides a comprehensive guide to make the most of pre-training for neural machine translation. Firstly, we will briefly introduce the background of NMT, pre-training methodology, and point out the main challenges when applying pre-training for NMT. Then we will focus on analysing the role of pre-training... | 2,021 | https://aclanthology.org/2021.acl-tutorials.4 | ACL, IJCNLP | [{'id': 13756489, 'paperId': '204e3073870fae3d05bcbc2f6a8e263d9b72e776', 'title': 'Attention is All you Need', 'authors': [{'authorId': '40348417', 'name': 'Ashish Vaswani'}, {'authorId': '1846258', 'name': 'Noam M. Shazeer'}, {'authorId': '3877127', 'name': 'Niki Parmar'}, {'authorId': '39328010', 'name': 'Jakob Uszko... |
2021.acl-tutorials.5 | Prosody: Models, Methods, and Applications | Prosody is essential in human interaction, enabling people to show interest, establish rapport, efficiently convey nuances of attitude or intent, and so on. Some applications that exploit prosodic knowledge have recently shown superhuman performance, and in many respects our ability to effectively model prosody is rapi... | 2,021 | https://aclanthology.org/2021.acl-tutorials.5 | ACL, IJCNLP | [{'id': 33433997, 'paperId': '38be2a1a9aa093a3d1de074f04fab47147d01418', 'title': 'An Introduction to English Phonetics', 'authors': [{'authorId': '144059102', 'name': 'Richard Ogden'}], 'venue': 'Phonetica: International Journal of Phonetic Science', 'abstract': '1. Introduction 2. Overview of the human speech mechani... |
2021.acl-tutorials.6 | Recognizing Multimodal Entailment | How information is created, shared and consumed has changed rapidly in recent decades, in part thanks to new social platforms and technologies on the web. With ever-larger amounts of unstructured and limited labels, organizing and reconciling information from different sources and modalities is a central challenge in m... | 2,021 | https://aclanthology.org/2021.acl-tutorials.6 | ACL, IJCNLP | [{'id': 13401254, 'paperId': '93ed6511a0ae5b13ccf445081ab829d415ca47df', 'title': 'Neural Graph Machines: Learning Neural Networks Using Graphs', 'authors': [{'authorId': '23519191', 'name': 'T. Bui'}, {'authorId': '35014893', 'name': 'Sujith Ravi'}, {'authorId': '2525389', 'name': 'Vivek Ramavajjala'}], 'venue': 'arXi... |
2021.eacl-tutorials.1 | Unsupervised Natural Language Parsing (Introductory Tutorial) | Unsupervised parsing learns a syntactic parser from training sentences without parse tree annotations. Recently, there has been a resurgence of interest in unsupervised parsing, which can be attributed to the combination of two trends in the NLP community: a general trend towards unsupervised training or pre-training, ... | 2,021 | https://aclanthology.org/2021.eacl-tutorials.1 | EACL | [{'id': 1364249, 'paperId': 'ca2858b2040724ae9f29ba601df12aae2e539596', 'title': 'Corpus-Based Induction of Syntactic Structure: Models of Dependency and Constituency', 'authors': [{'authorId': '38666915', 'name': 'D. Klein'}, {'authorId': '144783904', 'name': 'Christopher D. Manning'}], 'venue': 'Annual Meeting of the... |
2021.eacl-tutorials.2 | Aggregating and Learning from Multiple Annotators | The success of NLP research is founded on high-quality annotated datasets, which are usually obtained from multiple expert annotators or crowd workers. The standard practice to training machine learning models is to first adjudicate the disagreements and then perform the training. To this end, there has been a lot of w... | 2,021 | https://aclanthology.org/2021.eacl-tutorials.2 | EACL | [{'id': 219302730, 'paperId': '919aa58480ac34f6b7ea433d5fa6368745aa572b', 'title': 'The Benefits of a Model of Annotation', 'authors': [{'authorId': '1703046', 'name': 'R. Passonneau'}, {'authorId': '2579894', 'name': 'Bob Carpenter'}], 'venue': 'Transactions of the Association for Computational Linguistics', 'abstract... |
2021.eacl-tutorials.3 | Tutorial: End-to-End Speech Translation | Speech translation is the translation of speech in one language typically to text in another, traditionally accomplished through a combination of automatic speech recognition and machine translation. Speech translation has attracted interest for many years, but the recent successful applications of deep learning to bot... | 2,021 | https://aclanthology.org/2021.eacl-tutorials.3 | EACL | [{'id': 215754220, 'paperId': 'b57a537ae33092b7acf83dbd0470c6c03752fc79', 'title': 'Speech Translation and the End-to-End Promise: Taking Stock of Where We Are', 'authors': [{'authorId': '3011998', 'name': 'Matthias Sperber'}, {'authorId': '1775245', 'name': 'M. Paulik'}], 'venue': 'Annual Meeting of the Association fo... |
2021.eacl-tutorials.4 | Reviewing Natural Language Processing Research | The reviewing procedure has been identified as one of the major issues in the current situation of the NLP field. While it is implicitly assumed that junior researcher learn reviewing during their PhD project, this might not always be the case. Additionally, with the growing NLP community and the efforts in the context... | 2,021 | https://aclanthology.org/2021.eacl-tutorials.4 | EACL | [{'id': 8460592, 'paperId': '9ca5552008fe2c24e0541f6af47fd5110d4015b3', 'title': 'Last Words: Reviewing the Reviewers', 'authors': [{'authorId': '2272727361', 'name': 'K. Church'}], 'venue': 'International Conference on Computational Logic', 'abstract': '', 'year': 2005, 'in_acl': True, 'citationCount': 48, 'section': ... |
2021.eacl-tutorials.5 | Advances and Challenges in Unsupervised Neural Machine Translation | Unsupervised cross-lingual language representation initialization methods, together with mechanisms such as denoising and back-translation, have advanced unsupervised neural machine translation (UNMT), which has achieved impressive results. Meanwhile, there are still several challenges for UNMT. This tutorial first int... | 2,021 | https://aclanthology.org/2021.eacl-tutorials.5 | EACL | [{'id': 11212020, 'paperId': 'fa72afa9b2cbc8f0d7b05d52548906610ffbb9c5', 'title': 'Neural Machine Translation by Jointly Learning to Align and Translate', 'authors': [{'authorId': '3335364', 'name': 'Dzmitry Bahdanau'}, {'authorId': '1979489', 'name': 'Kyunghyun Cho'}, {'authorId': '1751762', 'name': 'Yoshua Bengio'}],... |
2021.emnlp-tutorials.1 | Crowdsourcing Beyond Annotation: Case Studies in Benchmark Data Collection | Crowdsourcing from non-experts is one of the most common approaches to collecting data and annotations in NLP. Even though it is such a fundamental tool in NLP, crowdsourcing use is largely guided by common practices and the personal experience of researchers. Developing a theory of crowdsourcing use for practical lang... | 2,021 | https://aclanthology.org/2021.emnlp-tutorials.1 | EMNLP | [{'id': 3432876, 'paperId': '5ded2b8c64491b4a67f6d39ce473d4b9347a672e', 'title': 'A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference', 'authors': [{'authorId': '81840293', 'name': 'Adina Williams'}, {'authorId': '10666396', 'name': 'Nikita Nangia'}, {'authorId': '3644767', 'name': 'Samuel R.... |
2021.emnlp-tutorials.2 | Financial Opinion Mining | In this tutorial, we will show where we are and where we will be to those researchers interested in this topic. We divide this tutorial into three parts, including coarse-grained financial opinion mining, fine-grained financial opinion mining, and possible research directions. This tutorial starts by introducing the co... | 2,021 | https://aclanthology.org/2021.emnlp-tutorials.2 | EMNLP | [{'id': 55268987, 'paperId': '349b576d6919ebf68617c81378bab9a90c7389b5', 'title': 'When is a Liability not a Liability? Textual Analysis, Dictionaries, and 10-Ks', 'authors': [{'authorId': '46173917', 'name': 'Tim Loughran'}, {'authorId': '35005086', 'name': 'B. Mcdonald'}], 'venue': '', 'abstract': 'Previous research ... |
2021.emnlp-tutorials.3 | Knowledge-Enriched Natural Language Generation | Knowledge-enriched text generation poses unique challenges in modeling and learning, driving active research in several core directions, ranging from integrated modeling of neural representations and symbolic information in the sequential/hierarchical/graphical structures, learning without direct supervisions due to th... | 2,021 | https://aclanthology.org/2021.emnlp-tutorials.3 | EMNLP | [{'id': 222272210, 'paperId': 'c845494445f3bfa01d8245a4759b144e27aa3788', 'title': 'A Survey of Knowledge-enhanced Text Generation', 'authors': [{'authorId': '38767143', 'name': 'W. Yu'}, {'authorId': '70461341', 'name': 'Wenhao Yu'}, {'authorId': '8652308', 'name': 'Chenguang Zhu'}, {'authorId': '1993150474', 'name': ... |
2021.emnlp-tutorials.6 | Syntax in End-to-End Natural Language Processing | This tutorial surveys the latest technical progress of syntactic parsing and the role of syntax in end-to-end natural language processing (NLP) tasks, in which semantic role labeling (SRL) and machine translation (MT) are the representative NLP tasks that have always been beneficial from informative syntactic clues sin... | 2,021 | https://aclanthology.org/2021.emnlp-tutorials.6 | EMNLP | [{'id': 3074096, 'paperId': '2913c2bf3f92b5ae369400a42b2d27cc5bc05ecb', 'title': 'Deep Learning', 'authors': [{'authorId': '1688882', 'name': 'Yann LeCun'}, {'authorId': '1751762', 'name': 'Yoshua Bengio'}, {'authorId': '1695689', 'name': 'Geoffrey E. Hinton'}], 'venue': '', 'abstract': 'Machine-learning technology pow... |
2021.naacl-tutorials.2 | Fine-grained Interpretation and Causation Analysis in Deep NLP Models | Deep neural networks have constantly pushed the state-of-the-art performance in natural language processing and are considered as the de-facto modeling approach in solving complex NLP tasks such as machine translation, summarization and question-answering. Despite the proven efficacy of deep neural networks at-large, t... | 2,021 | https://aclanthology.org/2021.naacl-tutorials.2 | NAACL | [{'id': 56657817, 'paperId': '668f42a4d4094f0a66d402a16087e14269b31a1f', 'title': 'Analysis Methods in Neural Language Processing: A Survey', 'authors': [{'authorId': '2083259', 'name': 'Yonatan Belinkov'}, {'authorId': '145898106', 'name': 'James R. Glass'}], 'venue': 'Transactions of the Association for Computational... |
2021.naacl-tutorials.3 | Deep Learning on Graphs for Natural Language Processing | Due to its great power in modeling non-Euclidean data like graphs or manifolds, deep learning on graph techniques (i.e., Graph Neural Networks (GNNs)) have opened a new door to solving challenging graph-related NLP problems. There has seen a surge of interests in applying deep learning on graph techniques to NLP, and h... | 2,021 | https://aclanthology.org/2021.naacl-tutorials.3 | NAACL | [{'id': 3144218, 'paperId': '36eff562f65125511b5dfab68ce7f7a943c27478', 'title': 'Semi-Supervised Classification with Graph Convolutional Networks', 'authors': [{'authorId': '41016725', 'name': 'Thomas Kipf'}, {'authorId': '1678311', 'name': 'M. Welling'}], 'venue': 'International Conference on Learning Representations... |
2021.naacl-tutorials.5 | Beyond Paragraphs: NLP for Long Sequences | In this tutorial, we aim at bringing interested NLP researchers up to speed about the recent and ongoing techniques for document-level representation learning. Additionally, our goal is to reveal new research opportunities to the audience, which will hopefully bring us closer to address existing challenges in this doma... | 2,021 | https://aclanthology.org/2021.naacl-tutorials.5 | NAACL | [{'id': 6857205, 'paperId': '455afd748e8834ef521e4b67c7c056d3c33429e2', 'title': 'Hierarchical Attention Networks for Document Classification', 'authors': [{'authorId': '8387085', 'name': 'Zichao Yang'}, {'authorId': '2022168', 'name': 'Diyi Yang'}, {'authorId': '1745899', 'name': 'Chris Dyer'}, {'authorId': '144137069... |
2021.naacl-tutorials.6 | Crowdsourcing Natural Language Data at Scale: A Hands-On Tutorial | In this tutorial, we present a portion of unique industry experience in efficient natural language data annotation via crowdsourcing shared by both leading researchers and engineers from Yandex. We will make an introduction to data labeling via public crowdsourcing marketplaces and will present the key components of ef... | 2,021 | https://aclanthology.org/2021.naacl-tutorials.6 | NAACL | [{'id': 45813168, 'paperId': 'c80c7ab615b2fad5148a7848dbdd26a2dc50dd3d', 'title': 'Maximum Likelihood Estimation of Observer Error‐Rates Using the EM Algorithm', 'authors': [{'authorId': '144845491', 'name': 'A. Dawid'}, {'authorId': '1909853', 'name': 'A. Skene'}], 'venue': '', 'abstract': 'In compiling a patient reco... |
2022.aacl-tutorials.1 | Efficient and Robust Knowledge Graph Construction | Knowledge graph construction which aims to extract knowledge from the text corpus, has appealed to the NLP community researchers. Previous decades have witnessed the remarkable progress of knowledge graph construction on the basis of neural models; however, those models often cost massive computation or labeled data re... | 2,022 | https://aclanthology.org/2022.aacl-tutorials.1 | AACL, IJCNLP | [{'id': 4557963, 'paperId': 'cf5ea582bccc7cb21a2ebeb7a0987f79652bde8d', 'title': 'Knowledge vault: a web-scale approach to probabilistic knowledge fusion', 'authors': [{'authorId': '145867172', 'name': 'X. Dong'}, {'authorId': '1718798', 'name': 'E. Gabrilovich'}, {'authorId': '1728179', 'name': 'Geremy Heitz'}, {'auth... |
2022.aacl-tutorials.2 | Recent Advances in Pre-trained Language Models: Why Do They Work and How Do They Work | Pre-trained language models (PLMs) are language models that are pre-trained on large-scaled corpora in a self-supervised fashion. These PLMs have fundamentally changed the natural language processing community in the past few years. In this tutorial, we aim to provide a broad and comprehensive introduction from two per... | 2,022 | https://aclanthology.org/2022.aacl-tutorials.2 | AACL, IJCNLP | [{'id': 13756489, 'paperId': '204e3073870fae3d05bcbc2f6a8e263d9b72e776', 'title': 'Attention is All you Need', 'authors': [{'authorId': '40348417', 'name': 'Ashish Vaswani'}, {'authorId': '1846258', 'name': 'Noam M. Shazeer'}, {'authorId': '3877127', 'name': 'Niki Parmar'}, {'authorId': '39328010', 'name': 'Jakob Uszko... |
2022.aacl-tutorials.3 | When Cantonese NLP Meets Pre-training: Progress and Challenges | Cantonese is an influential Chinese variant with a large population of speakers worldwide. However, it is under-resourced in terms of the data scale and diversity, excluding Cantonese Natural Language Processing (NLP) from the stateof-the-art (SOTA) “pre-training and fine-tuning” paradigm. This tutorial will start with... | 2,022 | https://aclanthology.org/2022.aacl-tutorials.3 | AACL, IJCNLP | [{'id': 160543569, 'paperId': 'ef8d3369f0d5d78f1c0989e2dd59d5ca8f045441', 'title': 'Cantonese as Written Language: The Growth of a Written Chinese Vernacular', 'authors': [{'authorId': '66196923', 'name': 'Don Snow'}], 'venue': '', 'abstract': 'List of Illustrations and TablesPreface 1. Why Study the Development of Wri... |
2022.aacl-tutorials.4 | Grounding Meaning Representation for Situated Reasoning | As natural language technology becomes ever-present in everyday life, people will expect artificial agents to understand language use as humans do. Nevertheless, most advanced neural AI systems fail at some types of interactions that are trivial for humans (e.g., ask a smart system “What am I pointing at?”). One critic... | 2,022 | https://aclanthology.org/2022.aacl-tutorials.4 | AACL, IJCNLP | [{'id': 7771402, 'paperId': 'e72e5ee5de14fd463ab58ce830474157258e3578', 'title': 'Abstract Meaning Representation for Sembanking', 'authors': [{'authorId': '3460261', 'name': 'L. Banarescu'}, {'authorId': '3202888', 'name': 'C. Bonial'}, {'authorId': '2112618394', 'name': 'Shu Cai'}, {'authorId': '2065872210', 'name': ... |
2022.aacl-tutorials.5 | The Battlefront of Combating Misinformation and Coping with Media Bias | Misinformation is a pressing issue in modern society. It arouses a mixture of anger, distrust, confusion, and anxiety that cause damage on our daily life judgments and public policy decisions. While recent studies have explored various fake news detection and media bias detection techniques in attempts to tackle the pr... | 2,022 | https://aclanthology.org/2022.aacl-tutorials.5 | AACL, IJCNLP | [{'id': 168169824, 'paperId': 'ad7129af0644dbcafa9aa2f111cb76526ea444a1', 'title': 'Defending Against Neural Fake News', 'authors': [{'authorId': '2545335', 'name': 'Rowan Zellers'}, {'authorId': '14487640', 'name': 'Ari Holtzman'}, {'authorId': '2516777', 'name': 'Hannah Rashkin'}, {'authorId': '3312309', 'name': 'Yon... |
2022.aacl-tutorials.6 | A Tour of Explicit Multilingual Semantics: Word Sense Disambiguation, Semantic Role Labeling and Semantic Parsing | The recent advent of modern pretrained language models has sparked a revolution in Natural Language Processing (NLP), especially in multilingual and cross-lingual applications. Today, such language models have become the de facto standard for providing rich input representations to neural systems, achieving unprecedent... | 2,022 | https://aclanthology.org/2022.aacl-tutorials.6 | AACL, IJCNLP | [{'id': 237100274, 'paperId': 'b0fe3bf02e16bbea17df617fed6d367a0cc5e739', 'title': 'Recent Trends in Word Sense Disambiguation: A Survey', 'authors': [{'authorId': '143802044', 'name': 'Michele Bevilacqua'}, {'authorId': '40438851', 'name': 'Tommaso Pasini'}, {'authorId': '3106437', 'name': 'Alessandro Raganato'}, {'au... |
2022.acl-tutorials.2 | Towards Reproducible Machine Learning Research in Natural Language Processing | While recent progress in the field of ML has been significant, the reproducibility of these cutting-edge results is often lacking, with many submissions lacking the necessary information in order to ensure subsequent reproducibility. Despite proposals such as the Reproducibility Checklist and reproducibility criteria a... | 2,022 | https://aclanthology.org/2022.acl-tutorials.2 | ACL | [{'id': 4460617, 'paperId': '57b101db87fb0b67fbe8b57f90b83f8e9efe81a6', 'title': '1,500 scientists lift the lid on reproducibility', 'authors': [{'authorId': '2225440', 'name': 'M. Baker'}], 'venue': 'Nature', 'abstract': 'Survey sheds light on the ‘crisis’ rocking research.', 'year': 2016, 'in_acl': False, 'citationCo... |
2022.acl-tutorials.4 | Non-Autoregressive Sequence Generation | Non-autoregressive sequence generation (NAR) attempts to generate the entire or partial output sequences in parallel to speed up the generation process and avoid potential issues (e.g., label bias, exposure bias) in autoregressive generation. While it has received much research attention and has been applied in many se... | 2,022 | https://aclanthology.org/2022.acl-tutorials.4 | ACL | [{'id': 13756489, 'paperId': '204e3073870fae3d05bcbc2f6a8e263d9b72e776', 'title': 'Attention is All you Need', 'authors': [{'authorId': '40348417', 'name': 'Ashish Vaswani'}, {'authorId': '1846258', 'name': 'Noam M. Shazeer'}, {'authorId': '3877127', 'name': 'Niki Parmar'}, {'authorId': '39328010', 'name': 'Jakob Uszko... |
2022.acl-tutorials.5 | Learning with Limited Text Data | Natural Language Processing (NLP) has achieved great progress in the past decade on the basis of neural models, which often make use of large amounts of labeled data to achieve state-of-the-art performance. The dependence on labeled data prevents NLP models from being applied to low-resource settings and languages beca... | 2,022 | https://aclanthology.org/2022.acl-tutorials.5 | ACL | [{'id': 235422524, 'paperId': '013eb12ce5468f79d58bf859653f4929c5a2bd14', 'title': 'An Empirical Survey of Data Augmentation for Limited Data Learning in NLP', 'authors': [{'authorId': '47739850', 'name': 'Jiaao Chen'}, {'authorId': '1390031652', 'name': 'Derek Tam'}, {'authorId': '2402716', 'name': 'Colin Raffel'}, {'... |
2022.acl-tutorials.6 | Zero- and Few-Shot NLP with Pretrained Language Models | The ability to efficiently learn from little-to-no data is critical to applying NLP to tasks where data collection is costly or otherwise difficult. This is a challenging setting both academically and practically—particularly because training neutral models typically require large amount of labeled data. More recently,... | 2,022 | https://aclanthology.org/2022.acl-tutorials.6 | ACL | [{'id': 218971783, 'paperId': '90abbc2cf38462b954ae1b772fac9532e2ccd8b0', 'title': 'Language Models are Few-Shot Learners', 'authors': [{'authorId': '31035595', 'name': 'Tom B. Brown'}, {'authorId': '2056658938', 'name': 'Benjamin Mann'}, {'authorId': '39849748', 'name': 'Nick Ryder'}, {'authorId': '2065894334', 'name'... |
2022.acl-tutorials.8 | Natural Language Processing for Multilingual Task-Oriented Dialogue | Recent advances in deep learning have also enabled fast progress in the research of task-oriented dialogue (ToD) systems. However, the majority of ToD systems are developed for English and merely a handful of other widely spoken languages, e.g., Chinese and German. This hugely limits the global reach and, consequently,... | 2,022 | https://aclanthology.org/2022.acl-tutorials.8 | ACL | [{'id': 10565222, 'paperId': '0a22389bd99b7efe3627ec6fc77ddaf3ff5e2faa', 'title': 'A Network-based End-to-End Trainable Task-oriented Dialogue System', 'authors': [{'authorId': '1388702112', 'name': 'L. Rojas-Barahona'}, {'authorId': '51175233', 'name': 'M. Gašić'}, {'authorId': '3334541', 'name': 'N. Mrksic'}, {'autho... |
2022.emnlp-tutorials.1 | Meaning Representations for Natural Languages: Design, Models and Applications | This tutorial reviews the design of common meaning representations, SoTA models for predicting meaning representations, and the applications of meaning representations in a wide range of downstream NLP tasks and real-world applications. Reporting by a diverse team of NLP researchers from academia and industry with exte... | 2,022 | https://aclanthology.org/2022.emnlp-tutorials.1 | EMNLP | [{'id': 2486369, 'paperId': '99d2dcdcf4cf05facaa101a48c7e31d140b4736d', 'title': 'The Proposition Bank: An Annotated Corpus of Semantic Roles', 'authors': [{'authorId': '145755155', 'name': 'Martha Palmer'}, {'authorId': '2489901', 'name': 'Paul R. Kingsbury'}, {'authorId': '1793218', 'name': 'D. Gildea'}], 'venue': 'I... |
2022.emnlp-tutorials.4 | CausalNLP Tutorial: An Introduction to Causality for Natural Language Processing | Causal inference is becoming an increasingly important topic in deep learning, with the potential to help with critical deep learning problems such as model robustness, interpretability, and fairness. In addition, causality is naturally widely used in various disciplines of science, to discover causal relationships amo... | 2,022 | https://aclanthology.org/2022.emnlp-tutorials.4 | EMNLP | [{'id': 259253713, 'paperId': '1e3ee4a75451ef74febb720a7bdda561f16b964a', 'title': 'A Survey of Learning Causality with Data: Problems and Methods', 'authors': [{'authorId': '2773849', 'name': 'Ruocheng Guo'}, {'authorId': '2140175677', 'name': 'Lu Cheng'}, {'authorId': '2040455', 'name': 'Jundong Li'}, {'authorId': '1... |
2022.emnlp-tutorials.6 | Non-Autoregressive Models for Fast Sequence Generation | Autoregressive (AR) models have achieved great success in various sequence generation tasks. However, AR models can only generate target sequence word-by-word due to the AR mechanism and hence suffer from slow inference. Recently, non-autoregressive (NAR) models, which generate all the tokens in parallel by removing th... | 2,022 | https://aclanthology.org/2022.emnlp-tutorials.6 | EMNLP | [{'id': 13756489, 'paperId': '204e3073870fae3d05bcbc2f6a8e263d9b72e776', 'title': 'Attention is All you Need', 'authors': [{'authorId': '40348417', 'name': 'Ashish Vaswani'}, {'authorId': '1846258', 'name': 'Noam M. Shazeer'}, {'authorId': '3877127', 'name': 'Niki Parmar'}, {'authorId': '39328010', 'name': 'Jakob Uszko... |
2022.naacl-tutorials.1 | Text Generation with Text-Editing Models | Text-editing models have recently become a prominent alternative to seq2seq models for monolingual text-generation tasks such as grammatical error correction, text simplification, and style transfer. These tasks share a common trait – they exhibit a large amount of textual overlap between the source and target texts. T... | 2,022 | https://aclanthology.org/2022.naacl-tutorials.1 | NAACL | [{'id': 13756489, 'paperId': '204e3073870fae3d05bcbc2f6a8e263d9b72e776', 'title': 'Attention is All you Need', 'authors': [{'authorId': '40348417', 'name': 'Ashish Vaswani'}, {'authorId': '1846258', 'name': 'Noam M. Shazeer'}, {'authorId': '3877127', 'name': 'Niki Parmar'}, {'authorId': '39328010', 'name': 'Jakob Uszko... |
2022.naacl-tutorials.2 | Self-supervised Representation Learning for Speech Processing | There is a trend in the machine learning community to adopt self-supervised approaches to pre-train deep networks. Self-supervised representation learning (SSL) utilizes proxy supervised learning tasks, for example, distinguishing parts of the input signal from distractors, or generating masked input segments condition... | 2,022 | https://aclanthology.org/2022.naacl-tutorials.2 | NAACL | [{'id': 239017006, 'paperId': 'aa62d5e43cb151cd574e4df058b4c6a509d62644', 'title': 'Self-Supervised Representation Learning: Introduction, advances, and challenges', 'authors': [{'authorId': '37151799', 'name': 'Linus Ericsson'}, {'authorId': '2319565', 'name': 'H. Gouk'}, {'authorId': '1717179', 'name': 'Chen Change L... |
2022.naacl-tutorials.3 | New Frontiers of Information Extraction | This tutorial targets researchers and practitioners who are interested in AI and ML technologies for structural information extraction (IE) from unstructured textual sources. Particularly, this tutorial will provide audience with a systematic introduction to recent advances of IE, by answering several important researc... | 2,022 | https://aclanthology.org/2022.naacl-tutorials.3 | NAACL | [{'id': 233297055, 'paperId': 'dbfc17833434243e07c4629e58f3d8ed7112dbfe', 'title': 'Learning from Noisy Labels for Entity-Centric Information Extraction', 'authors': [{'authorId': '2203076', 'name': 'Wenxuan Zhou'}, {'authorId': '1998918', 'name': 'Muhao Chen'}], 'venue': 'Conference on Empirical Methods in Natural Lan... |
2022.naacl-tutorials.5 | Tutorial on Multimodal Machine Learning | Multimodal machine learning involves integrating and modeling information from multiple heterogeneous sources of data. It is a challenging yet crucial area with numerous real-world applications in multimedia, affective computing, robotics, finance, HCI, and healthcare. This tutorial, building upon a new edition of a su... | 2,022 | https://aclanthology.org/2022.naacl-tutorials.5 | NAACL | [{'id': 252118396, 'paperId': '63f93a6d9c38d656933706acfc720684470bc108', 'title': 'Foundations and Recent Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions', 'authors': [{'authorId': '28130078', 'name': 'P. Liang'}, {'authorId': '144802290', 'name': 'Amir Zadeh'}, {'authorId': '49933077... |
2022.naacl-tutorials.6 | Contrastive Data and Learning for Natural Language Processing | Current NLP models heavily rely on effective representation learning algorithms. Contrastive learning is one such technique to learn an embedding space such that similar data sample pairs have close representations while dissimilar samples stay far apart from each other. It can be used in supervised or unsupervised set... | 2,022 | https://aclanthology.org/2022.naacl-tutorials.6 | NAACL | [{'id': 211096730, 'paperId': '7af72a461ed7cda180e7eab878efd5f35d79bbf4', 'title': 'A Simple Framework for Contrastive Learning of Visual Representations', 'authors': [{'authorId': '145358498', 'name': 'Ting Chen'}, {'authorId': '40464924', 'name': 'Simon Kornblith'}, {'authorId': '144739074', 'name': 'Mohammad Norouzi... |
2023.acl-tutorials.1 | Goal Awareness for Conversational AI: Proactivity, Non-collaborativity, and Beyond | Conversational systems are envisioned to provide social support or functional service to human users via natural language interactions. Conventional conversation researches mainly focus on the responseability of the system, such as dialogue context understanding and response generation, but overlooks the design of an e... | 2,023 | https://aclanthology.org/2023.acl-tutorials.1 | ACL | [{'id': 12633363, 'paperId': 'a5a8dfe5dcfa998124c8f115f5f743da2c40d714', 'title': 'Deep Learning for Dialogue Systems', 'authors': [{'authorId': '1725643', 'name': 'Yun-Nung (Vivian) Chen'}, {'authorId': '1709797', 'name': 'Asli Celikyilmaz'}, {'authorId': '1395813836', 'name': 'Dilek Z. Hakkani-Tür'}], 'venue': 'Inter... |
2023.acl-tutorials.2 | Complex Reasoning in Natural Language | Teaching machines to reason over texts has been a long-standing goal of natural language processing (NLP). To this end, researchers have designed a diverse set of complex reasoning tasks that involve compositional reasoning, knowledge retrieval, grounding, commonsense reasoning, etc. A standard choice for building syst... | 2,023 | https://aclanthology.org/2023.acl-tutorials.2 | ACL | [{'id': 236447339, 'paperId': '0e6e8274d0dcbc1c3c1ccdbd87f3e5d53fdf62b4', 'title': 'QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension', 'authors': [{'authorId': '145046059', 'name': 'Anna Rogers'}, {'authorId': '40642935', 'name': 'Matt Gardner'}, {'authorId': '1736067',... |
2023.acl-tutorials.5 | Indirectly Supervised Natural Language Processing | This tutorial targets researchers and practitioners who are interested in ML technologies for NLP from indirect supervision. In particular, we will present a diverse thread of indirect supervision studies that try to answer the following questions: (i) when and how can we provide supervision for a target task T, if all... | 2,023 | https://aclanthology.org/2023.acl-tutorials.5 | ACL | [{'id': 202540839, 'paperId': '093d9253a2fe765ca6577b091d3f99bab3155a7d', 'title': 'Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach', 'authors': [{'authorId': '40483594', 'name': 'Wenpeng Yin'}, {'authorId': '153035400', 'name': 'Jamaal Hay'}, {'authorId': '144590225', 'name': '... |
2023.acl-tutorials.6 | Retrieval-based Language Models and Applications | Retrieval-based language models (LMs) have shown impressive performance on diverse NLP tasks. In this tutorial, we will provide a comprehensive and coherent overview of recent advances in retrieval-based LMs. We will start by providing preliminaries covering the foundation of LMs (e.g., masked LMs, autoregressive LMs) ... | 2,023 | https://aclanthology.org/2023.acl-tutorials.6 | ACL | [{'id': 249097975, 'paperId': '4f4a409f701f7552d45c46a5b0fea69dca6f8e84', 'title': 'Unsupervised Dense Information Retrieval with Contrastive Learning', 'authors': [{'authorId': '1410231361', 'name': 'Gautier Izacard'}, {'authorId': '2062862676', 'name': 'Mathilde Caron'}, {'authorId': '26360550', 'name': 'Lucas Hossei... |
2023.eacl-tutorials.1 | Mining, Assessing, and Improving Arguments in NLP and the Social Sciences | Computational argumentation is an interdisciplinary research field, connecting Natural Language Processing (NLP) to other disciplines such as the social sciences. This tutorial will focus on a task that recently got into the center of attention in the community: argument quality assessment, that is, what makes an argum... | 2,023 | https://aclanthology.org/2023.eacl-tutorials.1 | EACL | [{'id': 51609977, 'paperId': '0957dc1a757f292ff8ba7a8e186ffc63a63d6b5a', 'title': 'Five Years of Argument Mining: a Data-driven Analysis', 'authors': [{'authorId': '1772891', 'name': 'Elena Cabrio'}, {'authorId': '1725656', 'name': 'S. Villata'}], 'venue': 'International Joint Conference on Artificial Intelligence', 'a... |
2023.eacl-tutorials.2 | Emotion Analysis from Texts | Emotion analysis in text is an area of research that encompasses a set of various natural language processing (NLP) tasks, including classification and regression settings, as well as structured prediction tasks like role labelling or stimulus detection. In this tutorial, we provide an overview of research from emotion... | 2,023 | https://aclanthology.org/2023.eacl-tutorials.2 | EACL | [{'id': 10784127, 'paperId': '435f22636f6fe13797951fd6cbe4532bc88f89ee', 'title': 'Emotion and Motivation: Toward Consensus Definitions and a Common Research Purpose', 'authors': [{'authorId': '143853826', 'name': 'P. Lang'}], 'venue': '', 'abstract': 'Historically, the hypothesis driving emotion research has been that... |
2023.eacl-tutorials.3 | Summarization of Dialogues and Conversations At Scale | Conversations are the natural communication format for people. This fact has motivated the large body of question answering and chatbot research as a seamless way for people to interact with machines. The conversations between people however, captured as video, audio or private or public written conversations, largely ... | 2,023 | https://aclanthology.org/2023.eacl-tutorials.3 | EACL | [{'id': 208010268, 'paperId': 'f9700e31a1d0ae34d4571ab056dfb268c1543349', 'title': 'SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization', 'authors': [{'authorId': '1782426', 'name': 'Bogdan Gliwa'}, {'authorId': '103241417', 'name': 'Iwona Mochol'}, {'authorId': '2065251929', 'name': 'M. Bie... |
2023.emnlp-tutorial.2 | Security Challenges in Natural Language Processing Models | Large-scale natural language processing models have been developed and integrated into numerous applications, given the advantage of their remarkable performance. Nonetheless, the security concerns associated with these models prevent the widespread adoption of these black-box machine learning models. In this tutorial,... | 2,023 | https://aclanthology.org/2023.emnlp-tutorial.2 | EMNLP | [{'id': 26783139, 'paperId': '573fd2ce97c70bb29097e8efb28a27af791225ca', 'title': 'BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain', 'authors': [{'authorId': '2367353', 'name': 'Tianyu Gu'}, {'authorId': '1398683279', 'name': 'Brendan Dolan-Gavitt'}, {'authorId': '1696125', 'name': 'S. G... |
2023.emnlp-tutorial.3 | Designing, Evaluating, and Learning from Humans Interacting with NLP Models | The rapid advancement of natural language processing (NLP) research has led to various applications spanning a wide range of domains that require models to interact with humans – e.g., chatbots responding to human inquiries, machine translation systems assisting human translators, designers prompting Large Language Mod... | 2,023 | https://aclanthology.org/2023.emnlp-tutorial.3 | EMNLP | [{'id': 232147529, 'paperId': 'c4788d6d19c9c6555264f274d01fd0c34c22c674', 'title': 'Putting Humans in the Natural Language Processing Loop: A Survey', 'authors': [{'authorId': '1390877819', 'name': 'Zijie J. Wang'}, {'authorId': '2026030439', 'name': 'Dongjin Choi'}, {'authorId': '51132439', 'name': 'Shenyu Xu'}, {'aut... |
2023.emnlp-tutorial.4 | LLM-driven Instruction Following: Progresses and Concerns | The progress of natural language processing (NLP) is primarily driven by machine learning that optimizes a system on a large-scale set of task-specific labeled examples. This learning paradigm limits the ability of machines to have the same capabilities as humans in handling new tasks since humans can often solve unsee... | 2,023 | https://aclanthology.org/2023.emnlp-tutorial.4 | EMNLP | [{'id': 211126925, 'paperId': '7b4358d7692353003eae7e39cececa2c2c44c43a', 'title': 'Learning from Explanations with Neural Execution Tree', 'authors': [{'authorId': '1390880371', 'name': 'Ziqi Wang'}, {'authorId': '50625437', 'name': 'Yujia Qin'}, {'authorId': '2203076', 'name': 'Wenxuan Zhou'}, {'authorId': '49781448'... |
2023.ijcnlp-tutorials.2 | Current Status of NLP in South East Asia with Insights from Multilingualism and Language Diversity | null | 2,023 | https://aclanthology.org/2023.ijcnlp-tutorials.2 | IJCNLP, AACL | [{'id': 247748611, 'paperId': 'a747e8f2659df479c0092301b9658fc582423df1', 'title': 'One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesia', 'authors': [{'authorId': '8129718', 'name': 'Alham Fikri Aji'}, {'authorId': '9162688', 'name': 'Genta Indra Winata'}, {'authorId': '... |
2023.ijcnlp-tutorials.4 | Editing Large Language Models | Even with their impressive abilities, Large Language Models (LLMs) such as ChatGPT are not immune to issues of factual or logically consistent. Concretely, the key concern is how to seamlessly update those LLMs to correct mistakes without resorting to an exhaustive retraining or continuous training procedure, both of w... | 2,023 | https://aclanthology.org/2023.ijcnlp-tutorials.4 | IJCNLP, AACL | [{'id': 258833129, 'paperId': 'f5c73d9e6641b018b633690102121f5605d34fb0', 'title': 'Editing Large Language Models: Problems, Methods, and Opportunities', 'authors': [{'authorId': '4841460', 'name': 'Yunzhi Yao'}, {'authorId': '144282672', 'name': 'Peng Wang'}, {'authorId': '2064522174', 'name': 'Bo Tian'}, {'authorId':... |
2023.ijcnlp-tutorials.5 | Learning WHO Saying WHAT to WHOM in Multi-Party Conversations | Multi-party conversations (MPC) are a more practical and challenging scenario involving more than two interlocutors. This research topic has drawn significant attention from both academia and industry, and it is nowadays counted as one of the most promising research areas in the field of dialogue systems. In general, M... | 2,023 | https://aclanthology.org/2023.ijcnlp-tutorials.5 | IJCNLP, AACL | [{'id': 250637571, 'paperId': '7958647ee241185ab253cdaa63466033e37e78ca', 'title': 'Who Says What to Whom: A Survey of Multi-Party Conversations', 'authors': [{'authorId': '3028818', 'name': 'Jia-Chen Gu'}, {'authorId': '8801869', 'name': 'Chongyang Tao'}, {'authorId': '1749989', 'name': 'Zhenhua Ling'}], 'venue': 'Int... |
2024.eacl-tutorials.1 | Computational modeling of semantic change | Languages change constantly over time, influenced by social, technological, cultural and political factors that affect how people express themselves. In particular, words can undergo the process of semantic change, which can be subtle and significantly impact the interpretation of texts. For example, the word terrific ... | 2,024 | https://aclanthology.org/2024.eacl-tutorials.1 | EACL | [{'id': 140928479, 'paperId': '5a05cd1f253baaa1b67c55d22335403a6251094c', 'title': 'How anger rose: Hypothesis testing in diachronic semantics', 'authors': [{'authorId': '1796288', 'name': 'D. Geeraerts'}, {'authorId': '145914884', 'name': 'C. Gevaert'}, {'authorId': '1754574', 'name': 'D. Speelman'}], 'venue': '', 'ab... |
2024.eacl-tutorials.4 | Transformer-specific Interpretability | Transformers have emerged as dominant play- ers in various scientific fields, especially NLP. However, their inner workings, like many other neural networks, remain opaque. In spite of the widespread use of model-agnostic interpretability techniques, including gradient-based and occlusion-based, their shortcomings are ... | 2,024 | https://aclanthology.org/2024.eacl-tutorials.4 | EACL | [{'id': 56657817, 'paperId': '668f42a4d4094f0a66d402a16087e14269b31a1f', 'title': 'Analysis Methods in Neural Language Processing: A Survey', 'authors': [{'authorId': '2083259', 'name': 'Yonatan Belinkov'}, {'authorId': '145898106', 'name': 'James R. Glass'}], 'venue': 'Transactions of the Association for Computational... |
2024.eacl-tutorials.5 | LLMs for Low Resource Languages in Multilingual, Multimodal and Dialectal Settings | The recent breakthroughs in Artificial Intelligence (AI) can be attributed to the remarkable performance of Large Language Models (LLMs) across a spectrum of research areas (e.g., machine translation, question-answering, automatic speech recognition, text-to-speech generation) and application domains (e.g., business, l... | 2,024 | https://aclanthology.org/2024.eacl-tutorials.5 | EACL | [{'id': 257900969, 'paperId': '0b3904d0e229796aff0bda43bb386513353bc992', 'title': 'A Survey of Large Language Models', 'authors': [{'authorId': '2542603', 'name': 'Wayne Xin Zhao'}, {'authorId': '1423651904', 'name': 'Kun Zhou'}, {'authorId': '2018027', 'name': 'Junyi Li'}, {'authorId': '1997234792', 'name': 'Tianyi T... |
2024.lrec-tutorials.2 | Geo-Cultural Representation and Inclusion in Language Technologies | Training and evaluation of language models are increasingly relying on semi-structured data that is annotated by humans, along with techniques such as RLHF growing in usage across the board. As a result, both the data and the human perspectives involved in this process play a key role in what is taken as ground truth b... | 2,024 | https://aclanthology.org/2024.lrec-tutorials.2 | LREC | [{'id': 245005939, 'paperId': '6159a9048cf3efb9bcee231b175932d07be33e37', 'title': 'Whose Ground Truth? Accounting for Individual and Collective Identities Underlying Dataset Annotation', 'authors': [{'authorId': '40081727', 'name': 'Emily L. Denton'}, {'authorId': '2146515892', 'name': "M. D'iaz"}, {'authorId': '24643... |
2024.lrec-tutorials.3 | Meaning Representations for Natural Languages: Design, Models and Applications | This tutorial reviews the design of common meaning representations, SoTA models for predicting meaning representations, and the applications of meaning representations in a wide range of downstream NLP tasks and real-world applications. Reporting by a diverse team of NLP researchers from academia and industry with exte... | 2,024 | https://aclanthology.org/2024.lrec-tutorials.3 | LREC | [{'id': 7771402, 'paperId': 'e72e5ee5de14fd463ab58ce830474157258e3578', 'title': 'Abstract Meaning Representation for Sembanking', 'authors': [{'authorId': '3460261', 'name': 'L. Banarescu'}, {'authorId': '3202888', 'name': 'C. Bonial'}, {'authorId': '2112618394', 'name': 'Shu Cai'}, {'authorId': '2065872210', 'name': ... |
2024.lrec-tutorials.4 | Navigating the Modern Evaluation Landscape: Considerations in Benchmarks and Frameworks for Large Language Models (LLMs) | General-Purpose Language Models have changed the world of Natural Language Processing, if not the world itself. The evaluation of such versatile models, while supposedly similar to evaluation of generation models before them, in fact presents a host of new evaluation challenges and opportunities. In this Tutorial, we w... | 2,024 | https://aclanthology.org/2024.lrec-tutorials.4 | LREC | [{'id': 5000956, 'paperId': '33a9d1a702eb75da709d26c44aaeb7c2015c870b', 'title': 'A Discriminative Graph-Based Parser for the Abstract Meaning Representation', 'authors': [{'authorId': '144683841', 'name': 'Jeffrey Flanigan'}, {'authorId': '38094552', 'name': 'Sam Thomson'}, {'authorId': '143712374', 'name': 'J. Carbon... |
2024.lrec-tutorials.7 | The DBpedia Databus Tutorial: Increase the Visibility and Usability of Your Data | This tutorial introduces DBpedia Databus (https://databus.dbpedia.org), a FAIR data publishing platform, to address challenges faced by data producers and consumers. It covers data organization, publishing, and consumption on the DBpedia Databus, with an exclusive focus on Linguistic Knowledge Graphs. The tutorial offe... | 2,024 | https://aclanthology.org/2024.lrec-tutorials.7 | LREC | [{'id': 219602200, 'paperId': '20aa85c0b0b07c3bef15f435b9d5292781b7c751', 'title': 'The New DBpedia Release Cycle: Increasing Agility and Efficiency in Knowledge Extraction Workflows', 'authors': [{'authorId': '24163430', 'name': 'M. Hofer'}, {'authorId': '2024066', 'name': 'Sebastian Hellmann'}, {'authorId': '1819564'... |
2024.lrec-tutorials.8 | NLP for Chemistry – Introduction and Recent Advances | In this half-day tutorial we will be giving an introductory overview to a number of recent applications of natural language processing to a relatively underrepresented application domain: chemistry. Specifically, we will see how neural language models (transformers) can be applied (oftentimes with near-human performanc... | 2,024 | https://aclanthology.org/2024.lrec-tutorials.8 | LREC | [{'id': 1427846, 'paperId': '5dd0ae971c88a817bb46160d1afc8af3c09fa69d', 'title': 'Identifying, Indexing, and Ranking Chemical Formulae and Chemical Names in Digital Documents', 'authors': [{'authorId': '47935371', 'name': 'Bingjun Sun'}, {'authorId': '143930195', 'name': 'P. Mitra'}, {'authorId': '145157784', 'name': '... |
2024.lrec-tutorials.9 | Formal Semantic Controls over Language Models | Text embeddings provide a concise representation of the semantics of sentences and larger spans of text, rather than individual words, capturing a wide range of linguistic features. They have found increasing application to a variety of NLP tasks, including machine translation and natural language inference. While most... | 2,024 | https://aclanthology.org/2024.lrec-tutorials.9 | LREC | [{'id': 393948, 'paperId': '184ac0766262312ba76bbdece4e7ffad0aa8180b', 'title': 'Representation Learning: A Review and New Perspectives', 'authors': [{'authorId': '1751762', 'name': 'Yoshua Bengio'}, {'authorId': '1760871', 'name': 'Aaron C. Courville'}, {'authorId': '145467703', 'name': 'Pascal Vincent'}], 'venue': 'I... |
2024.lrec-tutorials.10 | Towards a Human-Computer Collaborative Scientific Paper Lifecycle: A Pilot Study and Hands-On Tutorial | Due to the rapid growth of publications varying in quality, there exists a pressing need to help scientists digest and evaluate relevant papers, thereby facilitating scientific discovery. This creates a number of urgent questions; however, computer-human collaboration in the scientific paper lifecycle is still in the e... | 2,024 | https://aclanthology.org/2024.lrec-tutorials.10 | LREC | [{'id': 221191589, 'paperId': '22c39a725b020a57e4c152333ea702a342eee46c', 'title': 'Scientific Text Mining and Knowledge Graphs', 'authors': [{'authorId': '144812586', 'name': 'Meng Jiang'}, {'authorId': '2884976', 'name': 'Jingbo Shang'}], 'venue': 'Knowledge Discovery and Data Mining', 'abstract': 'Unstructured scien... |
2024.lrec-tutorials.11 | Tutorial Proposal: Hallucination in Large Language Models | In the fast-paced domain of Large Language Models (LLMs), the issue of hallucination is a prominent challenge. Despite continuous endeavors to address this concern, it remains a highly active area of research within the LLM landscape. Grasping the intricacies of this problem can be daunting, especially for those new to... | 2,024 | https://aclanthology.org/2024.lrec-tutorials.11 | LREC | [{'id': 261530162, 'paperId': 'd00735241af700d21762d2f3ca00d920241a15a4', 'title': "Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models", 'authors': [{'authorId': '1895977079', 'name': 'Yue Zhang'}, {'authorId': '2110450452', 'name': 'Yafu Li'}, {'authorId': '152496687', 'name': 'Leyang Cui... |
2024.lrec-tutorials.13 | Knowledge-enhanced Response Generation in Dialogue Systems: Current Advancements and Emerging Horizons | This tutorial provides an in-depth exploration of Knowledge-enhanced Dialogue Systems (KEDS), diving into their foundational aspects, methodologies, advantages, and practical applications. Topics include the distinction between internal and external knowledge integration, diverse methodologies employed in grounding dia... | 2,024 | https://aclanthology.org/2024.lrec-tutorials.13 | LREC | [{'id': 5523008, 'paperId': 'a6401e102c03a441992b3e45f7b63eec09d4b89d', 'title': 'A Survey on Dialogue Systems: Recent Advances and New Frontiers', 'authors': [{'authorId': '2957953', 'name': 'Hongshen Chen'}, {'authorId': '1390612725', 'name': 'Xiaorui Liu'}, {'authorId': '50559722', 'name': 'Dawei Yin'}, {'authorId':... |
2024.naacl-tutorials.2 | Combating Security and Privacy Issues in the Era of Large Language Models | This tutorial seeks to provide a systematic summary of risks and vulnerabilities in security, privacy and copyright aspects of large language models (LLMs), and most recent solutions to address those issues. We will discuss a broad thread of studies that try to answer the following questions: (i) How do we unravel the ... | 2,024 | https://aclanthology.org/2024.naacl-tutorials.2 | NAACL | [{'id': 227118606, 'paperId': '3a1f8829e641b46f661775f64a7f27b933a46103', 'title': 'ONION: A Simple and Effective Defense Against Textual Backdoor Attacks', 'authors': [{'authorId': '51466208', 'name': 'Fanchao Qi'}, {'authorId': '123331686', 'name': 'Yangyi Chen'}, {'authorId': '2027599235', 'name': 'Mukai Li'}, {'aut... |
2024.naacl-tutorials.4 | From Text to Context: Contextualizing Language with Humans, Groups, and Communities for Socially Aware NLP | Aimed at the NLP researchers or practitioners who would like to integrate human - individual, group, or societal level factors into their analyses, this tutorial will cover recent techniques and libraries for doing so at each level of analysis. Starting with human-centered techniques that provide benefit to traditional... | 2,024 | https://aclanthology.org/2024.naacl-tutorials.4 | NAACL | [{'id': 433382, 'paperId': 'bdb73be49c4fdcbd0c79ca62e5703155915fa4c4', 'title': 'Learning Multiview Embeddings of Twitter Users', 'authors': [{'authorId': '145583569', 'name': 'Adrian Benton'}, {'authorId': '144365054', 'name': 'R. Arora'}, {'authorId': '1782853', 'name': 'Mark Dredze'}], 'venue': 'Annual Meeting of th... |
2024.naacl-tutorials.5 | Human-AI Interaction in the Age of LLMs | Recently, the development of Large Language Models (LLMs) has revolutionized the capabilities of AI systems. These models possess the ability to comprehend and generate human-like text, enabling them to engage in sophisticated conversations, generate content, and even perform tasks that once seemed beyond the reach of ... | 2,024 | https://aclanthology.org/2024.naacl-tutorials.5 | NAACL | [{'id': 218483124, 'paperId': '529025645c70a935221bd434484faee695ad0f25', 'title': 'Re-examining Whether, Why, and How Human-AI Interaction Is Uniquely Difficult to Design', 'authors': [{'authorId': '2117860470', 'name': 'Qian Yang'}, {'authorId': '1792714', 'name': 'Aaron Steinfeld'}, {'authorId': '35959897', 'name': ... |
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