The First International Workshop on Designing Meaning Representations (DMR)

Workshop overview:

While deep learning methods have led to many breakthroughs in practical natural language applications, most notably in Machine Translation, Machine Reading/Question Answering, etc., there is still a sense among many NLP researchers that we have a long way to go before we can develop systems that can actually “understand” human language. “Understanding” natural language entails many different human-like capabilities, and they include but are not limited to the ability to track entities in a text, understand the relations between these entities, track events and their participants described in a text, understand how events unfold in time, and distinguish events that have actually happened from events that are planned or intended, are uncertain, or did not happen at all. We believe a critical step in achieving natural language understanding is to design meaning representations for text that have the necessary meaning “ingredients” that help us achieve these capabilities. Such meaning representations also need to be easy to produce in order to generate sufficient amounts of data to allow the training of accurate meaning representation parsers.

There has been a growing body of research devoted to the design, annotation, and parsing of meaning representations in recent years. The meaning representations that have been used for meaning representation parsing research are developed with different linguistic perspectives and practical goals in mind and have different formal properties [17]. Earlier meaning representation frameworks such as Minimal Recursion Semantics (MRS) [7, 11] and Discourse Representation Theory [15] (as exemplified in the Grongingen Meaning Bank [32] and the Parallel Meaning Bank [33]) are developed with the goal of supporting logical inference in reasoning based AI systems and are therefore easily translatable into first-order logic, requiring proper representation of semantic components such as quantification, negation, tense, and modality. More recent meaning representation frameworks such as Abstract Meaning Representation [4], Tectogrammatical Representation (TR) in Prague Dependency Treebanks [14], and the Universal Conceptual Cognitive Annotation (UCCA) [1], put more emphasis on the representation of lexical semantic information such as semantic roles and word sense, or entities and relations. The automatic parsing of natural language text into these meaning representations [34, 10, 30, 31, 2, 5, 12, 29, 3, 6, 16, 23, 21, 20] and to a lesser degree the generation of natural language text from these meaning representations [9, 16, 27, 26, 28] are also very active areas of research, and a wide range of technical approaches and learning methods have been applied to these problems. In addition, there have also been early attempts to use these meaning representations in natural language applications [22, 18, 13, 25, 8, 24, 19].

This workshop intends to bring together researchers who are producers and consumers of meaning representations and through their interaction gain a deeper understanding of the key elements of meaning representations that are the most valuable to the NLP community. The workshop will also provide an opportunity for meaning representation researchers to critically examine existing meaning representations with the goal of using their findings to inform the design of next-generation meaning representations. A third goal of the workshop is to explore opportunities and identify challenges in the design and use of meaning representations in multilingual settings. A final goal of the workshop is to understand the relationship between distributed meaning representations trained on large data sets using neural network models and the symbolic meaning representations that are carefully designed and annotated by CL researchers and gain a deeper understanding of areas where each type of meaning representation is the most effective. The workshop will solicit papers that address any one or combination of the following topics:

The design and annotation of meaning representations
Cross-framework comparison of meaning representations
Challenges in automatic parsing of meaning representations
Issues in applying meaning representations to multilingual settings
The relationship between symbolic meaning representations and distributed semantic representations
Any other topics that address the design, processing, and use of meaning representations

here

The workshop program is now available for Download

Invited speakers:

Omri Abend, Hebrew University of Jerusalem
Title: "The Case for Typologically-informed Meaning Representations"
Talk abstract and speaker's bio

Alexander Koller, Saarland University
Title: "Compositional semantic parsing across graphbanks"
Talk abstract and speaker's bio

Fei Liu, University of Central Floria
Title: "Toward Deep Abstractive Summarization Using Meaning Representations"
Talk abstract and speaker's bio

A survey on a Uniform Meaning Representation. Click here to start the survey

Co-Organizers:

Nianwen Xue, Brandeis University

William Croft, University of New Mexico

Jan Hajič, Charles University

Chu-Ren Huang, The Hong Kong Polytechnic University

Stephan Oepen, University of Oslo

Martha Palmer, University of Colorado

James Pustejovsky, Brandeis University

Program committee:

Abend, Omri, The Hebrew University of Jerusalem

Aroonmanakun, Wirote, Chulalongkorn University

Artzi, Yoav, Cornel University

Bender, Emily, University of Washington

Blache, Philippe, LPL/CNRS and Aix-Marseille University

Bos, Johan, University of Groningen

Butler, Alastair, Hirosaki University

Chersoni, Emmanuele, The Hong Kong Polytechnic University

Dalrymple, Mary, Oxford University

dePaiva, Valeira, Nuance Communications

Flanigan, Jeffrey. University of Mass, Amherst

Flickinger, Dan, Stanford University

Griffit, Kira, UPenn/LDC

Haug, Dag, University of Oslo

Hershcovich, Daniel The Hebrew University of Jerusalem

Kanzaki, Kyoko, Toyohashi University of Technology

Kennedy, Chris, The University of Chicago

Kim, Hansaem, Yonsei University

Lascarides, Alex, University of Edinburgh

Levin, Lori, CMU

Li, Bin, Nanjing Normal University,China

Lopez, Adam, University of Edinburgh

May,Jonathan, USC/ISI

Moeller, Sara, University of Colorado at Boulder

Nguyen, Thi Minh Huyen, VNU University of Science, Hanoi

Nivre, Joakim, Uppsala universitet

O’Gorman, Tim, University of Colorado

Øvrelid, Lilja, University of Oslo

Peng, Xiaochang, Facebook

Qu, Weiguang, Nanjin Normal University

Rutherford, Attapol, Chulalongkorn University

Schneider, Nathan, Georgetown University

Sun, Weiwei, Peking University

Tokunaga, Takeobu, Tokyo Institute of Technology

Uresova, Zdenka, Charles University

Voss, Clare, Army Research Lab

Wang, Chuan, Google

Webber, Bonnie, University of Edinburgh

Xu, Hongzhi, University of Pennsylvania

Zhang, Yuchen, Brandeis University

References

[1] Omri Abend and Ari Rappoport. Universal conceptual cognitive annotation (ucca). In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), volume 1, pages 228-238, 2013.

[2] Yoav Artzi, Kenton Lee, and Luke Zettlemoyer. Broad-coverage CCG semantic parsing with AMR. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1699-1710, 2015.

[3] Miguel Ballesteros and Yaser Al-Onaizan. Amr parsing using stack-lstms. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1280-1286. Association for Computational Linguistics, 2017.

[4] Laura Banarescu, Claire Bonial, Shu Cai, Madalina Georgescu, Kira Griffitt, Ulf Hermjakob, Kevin< Knight, Philipp Koehn, Martha Palmer, and Nathan Schneider. Abstract meaning representation for sembanking. In Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse, pages 178-186. Association for Computational Linguistics, 2013.

[5] Jan Buys and Phil Blunsom. Robust incremental neural semantic graph parsing. arXiv preprint arXiv:1704.07092, 2017.

[6] Jan Buys and Phil Blunsom. Robust incremental neural semantic graph parsing. In Proceedings of the 55th Meeting of the Association for Computational Linguistics, Vancouver, Canada, 2017.

[7] Ann Copestake, Dan Flickinger, Carl Pollard, and Ivan A. Sag. Minimal Recursion Semantics. An introduction. Research on Language and Computation, 3(4):281 - 332, 2005.

[8] Shibhansh Dohare, Harish Karnick, and Vivek Gupta. Text summarization using abstract meaning representation. arXiv preprint arXiv:1706.01678, 2017.

[9] Jeffrey Flanigan, Chris Dyer, Noah A Smith, and Jaime Carbonell. Generation from abstract meaning representation using tree transducers. In Proceedings of NAACL-HLT, pages 731-739, 2016.

[10] Jeffrey Flanigan, Sam Thomson, Jaime Carbonell, Chris Dyer, and Noah A. Smith. A discriminative graph-based parser for Abstract Meaning Representation. In Proceedings of the 52nd Annual Meeting of the Association of Computational Linguistics (ACL), 2014.

[11] Dan Flickinger, Emily M. Bender, and Stephan Oepen. Towards an encyclopedia of compositional semantics. Documenting the interface of the English Resource Grammar. In Proceedings of the 9th International Conference on Language Resources and Evaluation, page 875 - 881, Reykjavik, Iceland, 2014.

[12] William Foland and James Martin. Abstract meaning representation parsing using lstm recurrent neural networks. In Proceedings of the 55th Annual Meeting of the Association of the Computational Linguistics, Vancouver, Canada, 2017. Association for Computational Linguistics.

[13] Sahil Garg, Aram Galstyan, Ulf Hermjakob, and Daniel Marcu. Extracting biomolecular interactions using semantic parsing of biomedical text. arXiv preprint arXiv:1512.01587, 2015.

[14] J. Hajič, E. Hajičová, J. Panevová, P. Sgall, O. Bojar, S. Cinková, E. Fuč´ıková, M. Mikulová, P. Pajas,J. Popelka, J. Semecký, J. Šindlerová, J. Štěpánek, J. Toman, Z. Urešová, and Z. Žabokrtský. Announcing Prague Czech-English Dependency Treebank 2.0. In Proceedings of the eighth international conference on Language Resources and Evaluation, pages 3153-3160, Istanbul, 2012.

[15] Hans Kamp and Uwe Reyle. From discourse to logic: Introduction to model theoretic semantics of natural language, formal logic and discourse representation theory. Kluwer, Dordrecht, 1993.

[16] Ioannis Konstas, Srinivasan Iyer, Mark Yatskar, Yejin Choi, and Luke Zettlemoyer. Neural amr: sequence-to-sequence models for parsing and generation. In Proceedings of the 55th annual meeting of the Association for Computational Linguistics, Vancouver, Canada, 2017.

[17] Marco Kuhlmann and Stephan Oepen. Towards a catalogue of linguistic graph banks. Computational Linguistics, 42(4):819 - 827, 2016.

[18] Fei Liu, Jeffrey Flanigan, Sam Thomson, Norman Sadeh, and A. Noah Smith. Toward abstractive summarization using semantic representations. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1077-1086. Association for Computational Linguistics, 2015.

[19] Fei Liu, Jeffrey Flanigan, Sam Thomson, Norman Sadeh, and Noah A Smith. Toward abstractive summarization using semantic representations. arXiv preprint arXiv:1805.10399, 2018.

[20] Jiangming Liu, Shay B Cohen, and Mirella Lapata. Discourse representation structure parsing. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), volume 1, pages 429-439, 2018.

[21] Chunchuan Lyu and Ivan Titov. Amr parsing as graph prediction with latent alignment. arXiv preprint arXiv:1805.05286, 2018.

[22] Xiaoman Pan, Taylor Cassidy, Ulf Hermjakob, Heng Ji, and Kevin Knight. Unsupervised entity linking with abstract meaning representation. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1130-1139, 2015.

[23] Xiaochang Peng, Chuan Wang, Daniel Gildea, and Nianwen Xue. Addressing the data sparsity issue in neural amr parsing. In Proceedings of European Chapter of the Association for Computational Linguistics, Valencia, Spain, 2017.

[24] Sudha Rao, Daniel Marcu, Kevin Knight, and Hal Daumé III. Biomedical event extraction using abstract meaning representation. In BioNLP workshop 2017, pages 126-135, Vancouver, Canada, 2017.

[25] Mrinmaya Sachan and Eric Xing. Machine comprehension using rich semantic representations. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 486-492. Association for Computational Linguistics, 2016.

[26] Linfeng Song, Xiaochang Peng, Yue Zhang, Zhiguo Wang, and Daniel Gildea. Amr-to-text generation with synchronous node replacement grammar. arXiv preprint arXiv:1702.00500, 2017.

[27] Linfeng Song, Yue Zhang, Xiaochang Peng, Zhiguo Wang, and Daniel Gildea. Amr-to-text generation as a traveling salesman problem. arXiv preprint arXiv:1609.07451, 2016.

[28] Sho Takase, Jun Suzuki, Naoaki Okazaki, Tsutomu Hirao, and Masaaki Nagata. Neural headline generation on abstract meaning representation. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1054-1059.

[29] Chuan Wang and Nianwen Xue. Getting the most out of amr parsing. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1268-1279, 2017.

[30] Chuan Wang, Nianwen Xue, Sameer Pradhan, and Sameer Pradhan. A transition-based algorithm for amr parsing. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 366-375, 2015.

[31] Keenon Werling, Gabor Angeli, and D. Christopher Manning. Robust subgraph generation improves abstract meaning representation parsing. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 982-991. Association for Computational Linguistics, 2015.

[32] Johan Bos, Valerio Basille, Kilian Evang, Noortje J. Venhuizen, and Johannes Bjerva. 2017. The Groningen Meaning Bank. In Hand of Linguistic Annotation, pages 463-496. Springer, Dordrecht.

[33] Lasha Abzianidze, Johannes Bjerva, Kilian Evang, Hessel Haagsma, Rik Van Noord, Pierre Ludmann, Duc-Duy Nguyen, and Johan Bos. The Parallel Meaning Bank: Towards a multilingual corpus of translations annotated with compositional meaning representations. arXiv preprint arXiv:1702.03964 (2017).

[34] Johan Bos. 2008. Wide-coverage semantic analysis with Boxer. In Proceedings of the 2008 Conference on Semantics in Text Processing, pages 277-286, in conjunction with ACL-2008.