Recent developments in computational semantics and discourse processing

References

A very useful introductory textbook: M. Stede. Discourse Processing. Morgan & Claypool, 2011. Available in the Coli library!
More background in: Chapter 21, D. Jurafsky and J. Martin. Speech and Language Processing. Pearson, 2009.

Below, you can find a list of topics/session. For each topic, you should present the paper(s) listed for this topic. If there are multiple papers, you are expected to present one (sometimes two) of them in detail (title(s) in red), and give an overview of what's happening in the other(s). In some cases additional references are provided for background information. Ask us if you have any questions!

1. Introduction to Computational Discourse
   • Bonnie Webber, Markus Egg, Valia Kordoni, 2012. Discourse structure and language technology. Natural Language Engineering, 18.
2. Semantics: PPDB
   • Pavlick et al., 2015. PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification
   • Wieting et al. 2015. From Paraphrase Database to Compositional Paraphrase Model and Back
   • Wieting et al. 2016. Towards universal sentence embeddings.
3. Semantics: Modelling composition meaning of phrases and sentences
   • William Blacoe and Mirella Lapata. 2012. A Comparison of Vector-based Representations for Semantic Composition EMNLP 2012.
   • Dmitrijs Milajevs, Dimitri Kartsaklis, Mehrnoosh Sardzadeh, Matthew Purver. 2014. Evaluating NeuralWord Representations in Tensor-Based Compositional Settings EMNLP 2014
4. Semantics: State-of-the-art systems in paraphrase detection
   • Yangfeng Ji and Jacob Eisenstein. 2013. Discriminative Improvements to Distributional Sentence Similarity
   • Jianpeng Cheng and Dimitiri Kartsaklis. 2015. Syntax-aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning.
5. Semantics/Discourse: Coreference Resolution and Discourse Entity Processing
   • de Marneffe, M.-C., Recasens, M., Potts, C. (2015), Modeling the Lifespan of Discourse Entities with Application to Coreference Resolution. Journal of Artificial Intelligence Research, vol. 52
   • Recasens, M. and Hovy, E. (2009).
   • Raghunathan et al: A multi-pass sieve for coreference resolution. EMNLP 2010.
   • Jie Cai and Michael Strube: End-to-End coreference resolution via hypergraph partitioning. Proceedings of the 23rd International Conference on Computational Linguistics. Association for Computational Linguistics, 2010.
6. Discourse: Document Compression
   • James Clarke and Mirella Lapata, 2010: Discourse Constraints for Document Compression. Computational Linguistics 36(3).
   • (background on Centering Theory) M. Poesio, H. Cheng, R. Henschel, J. Hitzeman, R. Kibble, R. Stevenson, 2000. Specifying the parameters of centering theory: a corpus-based evaluation using text from application-oriented domainsProceedings of ACL 2000.
   • (more on Centering Theory) Nikiforos Karamanis, Chris Mellish, Massimo Poesio, Jon Oberlander, 2009: Evaluating centering for information ordering using corpora. Computational Linguistics 35(1).
7. Discourse: Penn Discourse Treebank & Parser (PDTB)
   • Lin, Ziheng, Hwee Tou Ng, and Min-Yen Kan. A PDTB-styled end-to-end discourse parser. Natural Language Engineering (2012): 1-34. (FOCUS ON THIS PAPER)
   • Ji, Y., & Eisenstein, J. (2015). One Vector is Not Enough: Entity-Augmented Distributed Semantics for Discourse Relations. Transactions of the Association for Computational Linguistics, 3, 329-344.
   • The PDTB Research Group. The Penn Discourse TreeBank 1.0. Annotation Manual. IRCS Technical Report IRCS-06-01, Institute for Research in Cognitive Science, University of Pennsylvania. March 2006. (no need to read all of this, but some sections may be useful.)
   • Eleni Miltsakaki, Rashmi Prasad, Aravind Joshi and Bonnie Webber.The Penn Discourse TreeBank. In Proceedings of the Language Resources and Evaluation Conference. Lisbon, Portugal. 2004.
8. Discourse: Rhetorical Structure Theory & Parser (RST)
   • Mann, W.C., & Thompson, S.A. 1988. Text, 8 (3). 243-281. A useful overview is here.
   • Soricut, Radu, and Daniel Marcu. Sentence Level Discourse Parsing using Syntactic and Lexical Information Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology-Volume 1. Association for Computational Linguistics, 2003.
9. Discourse: Topic Modeling (Please make sure to explain some background about topic models)
   
   • Barzilay, R., & Lee, L. (2004).Catching the drift: Probabilistic content models, with applications to generation and summarization. Proceedings of HLT-NAACL.
   • Chen, H., Branavan, S. R. K., Barzilay, R., & Karger, D. R. (2009). Global Models of Document Structure Using Latent Permutations. HLT-NAACL 2009 (pp. 371379).
   • Lapata, Mirella. Probabilistic text structuring: Experiments with sentence ordering. Proceedings of the 41st Annual Meeting on Association for Computational Linguistics-Volume 1. Association for Computational Linguistics, 2003.
10. Discourse: Temporal Relation Processing
    • TempEval-2014 overview: UzZaman et al. (2013): SemEval-2013 Task 1: TempEval-3
    • Steven Bethard and James H Martin. 2006. Identification of event mentions and their semantic class. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics.
    • Franco Costa and Antonio Branco. 2014. Aspectual Type and Temporal Relation Classification. Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 266–275, Avignon, France, April 23 - 27 2012.
    • Steven Bethard. 2013. ClearTK-TimeML: A minimalist approach to TempEval 2013. In Second Joint Conference on Lexical and Computational Semantics (* SEM), volume 2, pages 10–14.
11. Discourse: Machine Translation
    • Thomas Meyer; Bonnie Webber: Implicitation of Discourse Connectives in (Machine) Translation. In Proceedings of the 1st DiscoMT Workshop at ACL 2013 (51st Annual Meeting of the Association for Computational Linguistics).
    • Thomas Meyer, Lucia Polakova: Machine Translation with Many Manually Labeled Discourse Connectives, In Proceedings of the 1st DiscoMT Workshop at ACL 2013 (51st Annual Meeting of the Association for Computational Linguistics).
    • Pick a recent paper from DiscoMT 2015, interesting choices would be van der Wees et al, Wetzel et al.
12. Discourse: Sentiment analysis
    • Liu, Bing (2010) Sentiment analysis and subjectivity. In N. Indurkhya and D. F. J. (Eds.), Handbook of Natural Language Processing. Boca Raton, Fl: CRC Press.
    • Heerschop, Bas, Frank Goossen, Alexander Hogenboom, Flavius Frasincar, Uzay Kaymak, Franciska de Jong (2011) Polarity Analysis of Texts using Discourse Structure. CIKM 2011.
    • Taboada, Maite, Julian Brooke, Milan Tofiloski, Kimberly Voll and Manfred Stede (2011) Lexicon-based methods for sentiment analysis. Computational Linguistics, 37 (2): 267-307.
    • Bhatia, P., Ji, Y., & Eisenstein, J. (2015, September). Better document-level sentiment analysis from rst discourse parsing. In Proceedings of the Conference on Empirical Methods in Natural Language Processing,(EMNLP).
13. Discourse: Summarization
    • Janara Christensen, Mausam, Stephen Soderland, Oren Etzioni, 2013. Towards Coherent Multi-Document Summarization, Proceedings of NAACL-HLT 2013.
    • Ani Nenkova and Kathleen McKeown, 2011. Automatic Summarization. Foundations and Trends in Information Retrieval 5(2-3).
14. Semantics/Discourse: Semantic Role Labeling in Discourse Context
    • Roth, M. and Lapata, M. (2015), Context-aware Frame-Semantic Role Labeling. Transactions of the Association for Computational Linguistics, vol. 3
    • T&aauml;ckström, O., Ganchev, K., Das, D. (2015), Efficient Inference and Structured Learning for Semantic Role Labeling. Transactions of the Association for Computational Linguistics, vol. 3
    • Björkelund, A., Bohnet, B., Hafdell, L. and Nugues, P. (2010), A high-performance syntactic and semantic dependency parser.Proceedings of COLING'10
15. Scripts and Events 2: Temporal Ordering of Events
    • Abend, O., Cohen, S.B. and Steedman (2015), Lexical Event Ordering with an Edge-Factored Model. Proceedings of NAACL 2015 (PRIMARY PAPER of this session)
    • BACKGROUND: Chambers, N and Jurafsky, D. (2008), Unsupervised Learning of Narrative Event Chains. Proceedings of ACL-08: HLT.
    • BACKGROUND: Modi, A. and Titov, I. (2014), Inducing Neural Models of Script Knowledge. Proceedings of CoNLL-2014
16. Discourse-based Distributional Compositional Semantics
    • Polajnar, T., Rimell, L. and Clark, S. (2015), An Exploration of Discourse-Based Sentence Spaces for Compositional Distributional Semantics. Proceedings of the EMNLP 2015 Workshop on Linking Models of Lexical, Sentential and Discourse-Level Semantics