SIG Discourse【談話研究会 / 2015】

Special Interest Group on Discourse (SIG Discourse) discusses interesting findings of past/state-of-the-art researches. We also report and discuss research progress of our member. Typically, we read one paper (30 minutes) and discuss research progress of one person (40 minutes) at a time. The schedule is listed below.

Date

前期: Wednesday 16:30-18:00 後期: Monday 14:40-16:10

Member

乾，岡﨑，井之上，杉浦，山本，Paul，大野，小林，横井，高橋，佐藤

Related Keywords

人工知能/Artificial Intelligence，物語理解/Story Understanding，プラン/Plan，修辞構造/Rhetorical Structure，照応/Anaphora，省略/Ellipsis，ゼロ照応/Zero Anaphora

Schedule

• 3/15 (Tue): 14:40-
  • Progress Report (高橋): 内部資料
  • Paper Reading (山本)
    • A Bayesian Model of Diachronic Meaning Change, Lea Frermann and Mirella Lapata, TACL 2016
    • 論文, 内部資料

• 2/23 (Tue): 14:40-
  • Paper Reading (井之上)
    • Kelvin Guu, John Miller, and Percy Liang. Traversing Knowledge Graphs in Vector Space. EMNLP2015.

pdf, 著者 slides

• 2/16 (Tue): 14:40-
  • Progress Report (Paul)
    • Claim Classification and Persuasiveness 内部資料
  • Progress Report (高橋)
    • 「交通オントロジーと説明生成に基づく交通危険予測」進捗報告 内部資料

• 2/9 (Tue): 14:40-
  • Paper Reading (横井)
    • Luke Vilnis, Andrew McCallum. Word Representations via Gaussian Embeddings (ICLR 2015)
    • paper, 著者によるslide, 内部資料
  • Paper Reading (佐藤)
    • Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking. Tsung-Hsien Wen, SIGDIAL 2015
    • paper, 内部資料

• 2/2 (Tue): 14:40-
  • Progress Report (井之上): 内部資料
  • Paper Reading (大野)
    • Lexical Event Ordering with an Edge-Factored Model. Omri Abend, Shay Cohen and Mark Steedman. NAACL 2015
    • paper, 内部資料

• 1/26 (Tue): 14:40-
  • Progress Report (杉浦) 内部資料
  • Paper Reading (小林)
    • Learning statistical scripts with LSTM recurrent neural networks. Karl Pichotta and Raymond J. Mooney. AAAI 2016, to appear.
    • paper, 内部資料

• 1/12 (Tue): 14:40-
  • Paper Reading (Paul) paper,内部資料

• 1/5 (Tue): 14:40-
  • Paper Reading (高橋)
    • R. Regele, “Using Ontology-Based Traffic Models for More Efficient Decision Making of Autonomous Vehicles,” in Fourth International Conference on Autonomic and Autonomous Systems (ICAS’08), 2008, pp. 94–99.
    • paper，内部資料

• 12/22 (Tue): 14:40-
  • Progress report (小林)
    • How do neural networks read articles? 内部資料
    • 参考: Teaching Machines to Read and Comprehend, Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, Phil Blunsom, NIPS 2015
    • 参考: The Goldilocks Principle: Reading Children’s Books with Explicit Memory Representations, Felix Hill, Antoine Bordes, Sumit Chopra, Jason Weston, under review of ICLR 2016

• 12/8 (Tue): 14:40-
  • Paper reading (井之上)
    • Omid Bakhshandeh; James Allen. Semantic Framework for Comparison Structures in Natural Language. EMNLP2015. paper 内部資料
  • Progress report (佐藤): 内部資料

• 12/1 (Tue): 14:40-
  • Paper reading (横井)
    • Regina Barzilay and Mirella Lapata. “Modeling Local Coherence: An Entity-based Approach.” ACL 2005, Computational Linguistics 2008
    • paper (ACL 2005), paper (CompLing 2008)
    • resume, annotated-slides (the original version is made by the second author)
  • 緊急特番: 課題共有 (井之上)
    • Web から直接獲得するのは難しいであろう抽象的な知識/善悪に関する知識の整備 (内部資料)

• 11/24 (Tue): 14:40-
  • Progress report (Paul) 内部資料

• 11/17 (Tue): 14:40-
  • Progress report (大野)
    • 内部資料
  • Paper reading (小林)
    • Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, Phil Blunsom. Teaching Machines to Read and Comprehend, arXiv, Jun, 2015
    • paper, slide

• 11/10 (Tue): 14:40-
  • Paper reading (高橋)
    • Armand, Alexandre, David Filliat, and Javier Ibañez-Guzman. “Ontology-based context awareness for driving assistance systems.” In Intelligent Vehicles Symposium Proceedings, 2014 IEEE, pp. 227-233. IEEE, 2014.
    • paper，内部資料
  • Progress report (井之上)
    • 内部資料

• 10/27 (Tue): 14:40-
  • (Progress report (Paul))
  • Practice talk (小林)
    • 再帰型ニューラルネットワークを用いた対話破綻検出と言語モデルのマルチタスク学習
    • slide, paper1, paper2

• 10/20 (Tue): 14:40-
  • Paper reading (横井)
    • Tim Rocktäschel, Sameer Singh, and Sebastian Riedel. Injecting Logical Background Knowledge into Embeddings for Relation Extraction. NAACL-HLT 2015.
    • paper, slides
  • Paper reading (小林)
    • Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiský, Phil Blunsom, Reasoning about Entailment with Neural Attention, arXiv, Sep, 2015
    • paper, slide

• 10/13 (Tue): 14:40-
  • 大野
    • 内部資料

• 9/2 (Wed): 16:30-
  • Tutorial (井之上): Logic Programming ~ Answer Set Programming
    • 内部資料
    • 補助資料1
    • 補助資料2

• 8/5 (Wed): 夏休み特別会 / 14:30-
  • Progress report (Martin)
    • 内部資料

• 7/22 (Wed)
  • Progress report (井之上)
    • 内部資料

• 7/15 (Wed)
  • Paper reading (Paul) Slides Paper

• 7/8 (Wed)
  • Progress report (小林) 内部資料
  • Paper reading (周)
    • slides
    • paper
• 7/1 (Wed)
  • Progress report (Paul) 内部資料
  • Paper reading (山本)
    • paper, slides

• 6/24 (Wed)
  • Paper reading (井之上)
    • Mathias Niepert and Pedro Domingos. Tractable Probabilistic Knowledge Bases: Wikipedia and Beyond. STAR-AI14.
    • paper, slides
  • Paper reading (横井)
    • Zhen Wang et al. Knowledge Graph Embedding by Translating on Hyperplanes. AAAI 2014.
    • paper, slides

• 6/17 (Wed)
  • Progress report (周) 内部資料

• 6/10 (Wed)
  • Progress report (山本): 評価関数の改良と共参照事例からの知識獲得 (内部資料)
  • Paper reading (小林)
    • Do Supervised Distributional Methods Really Learn Lexical Inference Relations? Omer Levy, Steffen Remus, Chris Biemann, and Ido Dagan. NAACL 2015
    • paper, slide

• 6/3 (Wed)
  • 休会: 関根さんの talk

• 5/27 (Wed)
  • Progress ReportPaper Reading (井之上)
    • Haoruo Peng, Daniel Khashab and Dan Roth. Solving Hard Coreference Problems. NAACL2015.
    • slides paper (annotated)
  • Paper Reading (折田)

• 5/20 (Wed)
  • Progress Report (杉浦)
    • 内部資料
  • Paper Reading (大野)
    • Li Dong er al., Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification, ACL 2014
    • paper, slide

• 5/13 (Wed)
  • Paper Reading (Paul)
    • Slides Paper
  • Progress Report (井之上, 1分)
    • 内部資料
  • Practice Talk (小林)
    • 内部資料

• 5/6 (Wed)
  • 振替休日

• 4/29 (Wed)
  • 昭和の日

• 4/22 (Wed)
  • Paper Reading (周)
    • Ander Barrena et al. “One Entity per Discourse ” and “One Entity per Collocation” Improve Named-Entity Disambiguation. COLING 2014.
    • slides paper
  • Paper Reading (小林)
    • Subhro Roy, Tim Vieira, Dan Roth. Reasoning about Quantitiesin Natural Language, TACL 2015
    • slide, paper, 補助資料

• 4/15 (Wed)
  • Paper Reading (山本)
    • Rolf Schwitter, Controlled Natural Language Processing as Answer Set Programming: an Experiment, 2014
    • スライド, 論文
  • Progress Report (大野)
    • 内部資料

• 4/8 (Wed)
  • Kick-off Meeting (杉浦)
    • 内部資料
  • タスク紹介 (杉浦，山本，小林，周，Paul，稲田，大野)
    • 杉浦 内部資料
    • 山本 内部資料
    • 小林 内部資料
    • 周 内部資料
    • Paul 内部資料

• Spring 2014-Winter 2015
• Spring 2013-Winter 2014
• Spring 2012-Winter 2013
• Spring 2011-Winter 2012
• Spring 2010-Winter 2011

Assignment

Links

Artificial Intelligence

• Levesque, Hector J. “On our best behaviour.” The 23rd International Joint Conference on Artificial Intelligence (IJCAI). August. 2013. pdf
• Rahman, Altaf, and Vincent Ng. “Resolving complex cases of definite pronouns: the winograd schema challenge.” Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Association for Computational Linguistics, 2012. pdf dataset dataset 2
• Roemmele, Melissa, Cosmin Adrian Bejan, and Andrew S. Gordon. “Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning.” AAAI Spring Symposium: Logical Formalizations of Commonsense Reasoning. 2011. pdf dataset
• Levesque, Hector J., Ernest Davis, and Leora Morgenstern. “The Winograd schema challenge.” AAAI Spring Symposium: Logical Formalizations of Commonsense Reasoning. 2011. pdf
• Jerry R. Hobbs, Mark Stickel, Douglas Appelt, and Paul Martin. Interpretation as Abduction, Artificial Intelligence, 1993. pdf

Logical Inference

• Mohammad Shahed Sorower, Thomas G. Dietterich, Janardhan Rao Doppa Walker Orr, Prasad Tadepalli, and Xiaoli Fern. Inverting Grice’s Maxims to Learn Rules from Natural Language Extractions. NIPS 2011. pdf
• Ekaterina Ovchinnikova, Niloofar Montazeri, Theodore Alexandrov, Jerry R. Hobbs, Michael C. McCord and Rutu Mulkar-Mehta. Abductive Reasoning with a Large Knowledge Base for Discourse Processing. IWCS 2011. pdf
• James Blythe, Jerry R. Hobbs, Pedro Domingos, Rohit J. Kate and Raymond J. Mooney. Implementing Weighted Abduction in Markov Logic. IWCS2011. pdf
• Dan Garrette, Katrin Erk, Raymond Mooney. Integrating Logical Representations with Probabilistic Information using Markov Logic. IWCS2011. pdf
• Sindhu V. Raghavan, Raymond J. Mooney. Bayesian Abductive Logic Programs. AAAI 2010. pdf
• RohitJ. Kate RaymondJ. Mooney. Probabilistic Abduction using Markov Logic Networks. IJCAI 2009 on PAIR 2009. pdf
• Jerry R. Hobbs, Mark Stickel, Douglas Appelt, and Paul Martin. Interpretation as Abduction, Artificial Intelligence, 1993. pdf
• J. Bos (2009): Applying automated deduction to natural language understanding. Journal of Applied Logic 7(1): 100–112. pdf
• A Unified Approach to Abductive Inference (ARO 2008 MURI Project@University of Washington)

Discourse Theory

• Rhetorical Structure Theory
• Discourse Representation Theory
• J. Bos, M. Nissim (2008): Combining Discourse Representation Theory with FrameNet. In: R. Rossini Favretti (ed): Frames, Corpora, and Knowledge Representation, pp 169–183, Bononia University Press. pdf
• Dan Cristea, Nancy Ide and Laurent Romary. Veins Theory: A Model of Global Discourse Cohesion and Coherence. ACL 1998. pdf
• Barbara J. Grosz, Aravind K. Joshi and Scott Weinstein. Centering: A Framework for Modeling the Local Coherence of Discourse. Computational Linguistics, 1995. pdf
• Barbara J. Grosz and Candace L. Sidner. ATTENTION, INTENTIONS, AND THE STRUCTURE OF DISCOURSE. Computational Linguistics, 1986. pdf

• Bonnie Webber. Accounting for Discourse Relations: Constituency and Dependency. Intelligent Linguistic Architectures, 2006. pdf
• Florian Wolf, Edward Gibson. Representing Discourse Coherence: A Corpus-Based Study. Computational Linguistics, 2005.
• Bonnie Webber, Matthew Stone, Aravind Joshi and Alistair Knott. Anaphora and Discourse Structure. Computational Linguistics, 2003. pdf
• Daniel Marcu. A Formal and Computational Synthesis of Grosz and Sidner’s and Mann and Thompson’s theories. 1999. pdf
• Erhard Hinrichs. Discourse Annotation of Corpora. pdf
• Johanna D. Moore and Martha E. Pollack. A Problem for RST: The Need for Multi-Level Discourse Analysis. Computational Linguistics, 1992. pdf

Discourse Parsing

• Alexis Palmer, Afra Alishahi and Caroline Sporleder. Robust Semantic Analysis for Unseen Data in FrameNet. RANLP2011. pdf
• Michaela Regneri, Alexander Koller, Josef Ruppenhofer and Manfred Pinkal. Learning Script Participants from Unlabeled Data. RANLP2011. pdf
• Manfred Klenner and Don Tuggener. An Incremental Entity-Mention Model for Coreference Resolution with Restrictive Antecedent Accessibility. RANLP2011. pdf
• Ziheng Lin, Hwee Tou Ng and Min-Yen Kan. Automatically Evaluating Text Coherence Using Discourse Relation. ACL 2011. pdf

• Ziheng Lin, Hwee Tou Ng, and Min-Yen Kan. A PDTB-Styled End-to-End Discourse Parser. 2010. pdf

• Annie Louis, Rashmi Prasad, Aravind Joshi and Ani Nenkova. Using Entity Features to Classify Implicit Discourse Relations. SIGDIAL 2010. pdf
• Aria Haghighi and Dan Klein. Coreference Resolution in a Modular, Entity-Centered Model. NAACL-HLT 2010. pdf
• Emily Pitler, Annie Louis and Ani Nenkova. Automatic sense prediction for implicit discourse relations in text. ACL-IJCNLP 2009. pdf
• Rajen Subba and Barbara Di Eugenio. An effective Discourse Parser that uses Rich Linguistic Information. NAACL-HLT 2009. pdf
• Ravikiran Vadlapudi, Poornima Malepati and Suman Yelati. Hierarchical Discourse Parsing Based on Similarity Metrics. RANLP 2009. pdf
• Manfred Klenner, Étienne Ailloud. Optimization in Coreference Resolution is not Needed: A Nearly-Optimal Algorithm with Intensional Constraints. EACL 2009. pdf
• Jason Baldridge and Alex Lascarides. Probabilistic Head-Driven Parsing for Discourse Structure. CoNLL 2005. pdf
• Daniel Marcu and Abdessamad Echihabi. An Unsupervised Approach to Recognizing Discourse Relations. ACL 2002. pdf

Plan Recognition

• Parag Singla and Raymond J. Mooney. Abductive Markov Logic for Plan Recognition. AAAI2011. pp 1069-1075. pdf
• Nate Blaylock and James Allen. Hierarchical Instantiated Goal Recognition. MOO2006. pdf
• Nate Blaylock and James Allen. Fast Hierarchical Goal Schema Recognition. AAAI2006. pdf
• Douglas E. Appelt and Martha E. Pollack. Weighted Abduction for Plan Ascription. Technical Note 491, SRI International, 1992. pdf
• Sandra Carberry. Techniques for Plan Recognition. User Modeling and User-Adapted Interaction, 11(1-2), pp. 31-48, 2001. pdf

Corpus

• Penn Discourse Treebank
• RST Discourse Treebank
• Discourse Graphbank: paper LDC
• SDRT annotations of dialogues: DISCOR project

Knowledge Acquisition

• Doo Soon Kim and Bruce Poter. Integrating declarative knowledge : Issues, Algorithms and Future Work. AAAI2008. pdf
• Jonathan Berant, Tel Aviv and Jacob Goldberger. Global Learning of Typed Entailment Rules. ACL2011. (to appear) pdf
• Stefan Schoenmackers, Jesse Davis, Oren Etzioni and Daniel Weld. Learning First-Order Horn Clauses from Web Text. EMNLP2010. pdf
• Nathanael Chambers and Dan Jurafsky. Unsupervised Learning of Narrative Schemas and their Participants. ACL2010. pdf

Lectures

• University of Southern California: Introduction to NLP, Empirical Methods in Natural Language Processing
• MIT: Computational Models of Discourse
• Tohoku University: http://www.is.tohoku.ac.jp/_eng/introduction/laboratories/sis/iis_cs.html
• Story Understanding Resources
• David Poole and Alan Mackworth. Artificial Intelligence: Foundations of Computational Agents

Tools

• WOFMC
• Stanford CoreNLP
• Mini-TACITUS
• PRISM
• enju
• C&C tools

Misc.

• University of Texas at Austin Machine Learning Group: Publications
• Linguistic Problems and Complexities