If a piece of prior work appeared in multiple venues, the version that appeared in a refereed, archival venue should be referenced. If multiple versions of a piece of prior work exist, the one used by the authors should be referenced. Authors should not rely on automated citation indices to provide accurate references for prior and related work. Appendices: Appendices, if any, directly follow the text and the references (but see above). Letter them in sequence and provide an informative title: Appendix A. Title of Appendix.

Cooperative Learning of Disjoint Syntax and Semantics	There has been considerable attention devoted to models that learn to jointly infer an expression's syntactic structure and its semantics. Yet, Nangia and Bowman (2018) has recently shown that the current best systems fail to learn the correct parsing strategy on mathematical expressions generated from a simple context-free grammar. In this work, we present a recursive model inspired by Choi et al. (2018) that reaches near perfect accuracy on this task. Our model is composed of two separated modules for syntax and semantics. They are cooperatively trained with standard continuous and discrete optimisation schemes. Our model does not require any linguistic structure for supervision, and its recursive nature allows for out-of-domain generalisation. Additionally, our approach performs competitively on several natural language tasks, such as Natural Language Inference and Sentiment Analysis.

Appropriate records should be found for most materials in the current ACL Anthology at http://aclanthology.info/. As examples, we cite  to show you how papers with a DOI will appear in the bibliography. We cite  to show how papers without a DOI but with an ACL Anthology Identifier will appear in the bibliography. As reviewing will be double-blind, the submitted version of the papers should not include the authors' names and affiliations. Furthermore, self-references that reveal the author's identity, e.g., “We previously showed  ...” should be avoided. Instead, use citations such as “  Gusfield:97 previously showed ... ” Any preliminary non-archival versions of submitted papers should be listed in the submission form but not in the review version of the paper. NAACL-HLT 2019 reviewers are generally aware that authors may present preliminary versions of their work in other venues, but will not be provided the list of previous presentations from the submission form. Please do not use anonymous citations and do not include when submitting your papers. Papers that do not conform to these requirements may be rejected without review.

References: Gather the full set of references together under the heading References; place the section before any Appendices. Arrange the references alphabetically by first author, rather than by order of occurrence in the text. By using a .bib file, as in this template, this will be automatically handled for you. See the \bibliography commands near the end for more. Provide as complete a citation as possible, using a consistent format, such as the one for Computational Linguistics or the one in the Publication Manual of the American Psychological Association  . Use of full names for authors rather than initials is preferred. A list of abbreviations for common computer science journals can be found in the ACM Computing Reviews  . The and Bib style files provided roughly fit the American Psychological Association format, allowing regular citations, short citations and multiple citations as described above. Example citing an arxiv paper:  . Example article in journal citation:  . Example article in proceedings, with location:  . Example article in proceedings, without location:  . See corresponding .bib file for further details. Submissions should accurately reference prior and related work, including code and data.

Supplemental Material Submissions may include non-readable supplementary material used in the work and described in the paper. Any accompanying software and/or data should include licenses and documentation of research review as appropriate. Supplementary material may report preprocessing decisions, model parameters, and other details necessary for the replication of the experiments reported in the paper. Seemingly small preprocessing decisions can sometimes make a large difference in performance, so it is crucial to record such decisions to precisely characterize state-of-the-art methods. Nonetheless, supplementary material should be supplementary (rather than central) to the paper. Submissions that misuse the supplementary material may be rejected without review. Supplementary material may include explanations or details of proofs or derivations that do not fit into the paper, lists of features or feature templates, sample inputs and outputs for a system, pseudo-code or source code, and data. (Source code and data should be separate uploads, rather than part of the paper). The paper should not rely on the supplementary material: while the paper may refer to and cite the supplementary material and the supplementary material will be available to the reviewers, they will not be asked to review the supplementary material.

Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, and Diana Inkpen. 2017. Enhanced LSTM for natural language inference. In Proceedings of ACL 2017, Volume 1: Long Papers, pages 1657–1668. Jihun Choi, Kang Min Yoo, and Sang-goo Lee. 2018. Learning to compose task-specific tree structures. In Proceedings of AAAI 2018. Caio Corro and Ivan Titov. 2018. Differentiable perturb-and-parse: Semi-supervised parsing with a structured variational autoencoder. CoRR, abs/1807.09875. Sreerupa Das, C Lee Giles, and Guo-Zheng Sun. 1992. Learning context-free grammars: Capabilities and limitations of a recurrent neural network with an external stack memory. In Proceedings of CogSci 1992, page 14. Andrew Drozdov and Samuel Bowman. 2017. The coadaptation problem when learning how and what to compose. Proceedings of the 2nd Workshop on Representation Learning for NLP.

Length of Submission	The NAACL-HLT 2019 main conference accepts submissions of long papers and short papers. Long papers may consist of up to eight (8) pages of content plus unlimited pages for references. Upon acceptance, final versions of long papers will be given one additional page – up to nine (9) pages of content plus unlimited pages for references – so that reviewers' comments can be taken into account. Short papers may consist of up to four (4) pages of content, plus unlimited pages for references. Upon acceptance, short papers will be given five (5) pages in the proceedings and unlimited pages for references. For both long and short papers, all illustrations and tables that are part of the main text must be accommodated within these page limits, observing the formatting instructions given in the present document. Papers that do not conform to the specified length and formatting requirements are subject to be rejected without review. NAACL-HLT 2019 does encourage the submission of additional material that is relevant to the reviewers but not an integral part of the paper. There are two such types of material: appendices, which can be read, and non-readable supplementary materials, often data or code.

We suggest that instead of “  showed that ...” you use “Gusfield Gusfield:97 showed that ...” If you are using the provided and Bib style files, you can use the command \citet (cite in text) to get “author (year)” citations. If the Bib file contains DOI fields, the paper title in the references section will appear as a hyperlink to the DOI, using the hyperref package. To disable the hyperref package, load the style file with the nohyperref option: \usepackage[nohyperref]{naaclhlt2019} Digital Object Identifiers: As part of our work to make ACL materials more widely used and cited outside of our discipline, ACL has registered as a CrossRef member, as a registrant of Digital Object Identifiers (DOIs), the standard for registering permanent URNs for referencing scholarly materials. As of 2017, we are requiring all camera-ready references to contain the appropriate DOIs (or as a second resort, the hyperlinked ACL Anthology Identifier) to all cited works. Thus, please ensure that you use Bib records that contain DOI or URLs for any of the ACL materials that you reference.

Format of Electronic Manuscript	For the production of the electronic manuscript you must use Adobe's Portable Document Format (PDF). PDF files are usually produced from using the pdflatex command. If your version of produces Postscript files, you can convert these into PDF using ps2pdf or dvipdf. On Windows, you can also use Adobe Distiller to generate PDF. Please make sure that your PDF file includes all the necessary fonts (especially tree diagrams, symbols, and fonts with Asian characters). When you print or create the PDF file, there is usually an option in your printer setup to include none, all or just non-standard fonts. Please make sure that you select the option of including ALL the fonts. Before sending it, test your PDF by printing it from a computer different from the one where it was created. Moreover, some word processors may generate very large PDF files, where each page is rendered as an image. Such images may reproduce poorly. In this case, try alternative ways to obtain the PDF. One way on some systems is to install a driver for a postscript printer, send your document to the printer specifying “Output to a file”, then convert the file to PDF.

However, you should maintain space for names and addresses so that they will fit in the final (accepted) version. The NAACL-HLT 2019 style will create a titlebox space of 2.5in for you when \aclfinalcopy is commented out. The author list for submissions should include all (and only) individuals who made substantial contributions to the work presented. Each author listed on a submission to NAACL-HLT 2019 will be notified of submissions, revisions and the final decision. No authors may be added to or removed from submissions to NAACL-HLT 2019 after the submission deadline.

It is of utmost importance to specify the A4 format (21 cm x 29.7 cm) when formatting the paper. When working with dvips, for instance, one should specify -t a4. Or using the command \special{papersize=210mm,297mm} in the latex preamble (directly below the \usepackage commands). Then using dvipdf and/or pdflatex which would make it easier for some. Print-outs of the PDF file on A4 paper should be identical to the hardcopy version. If you cannot meet the above requirements about the production of your electronic submission, please contact the publication chairs as soon as possible.

Introduction	The following instructions are directed to authors of papers submitted to NAACL-HLT 2019 or accepted for publication in its proceedings. All authors are required to adhere to these specifications. Authors are required to provide a Portable Document Format (PDF) version of their papers. The proceedings are designed for printing on A4 paper.

General Instructions	Manuscripts must be in two-column format. Exceptions to the two-column format include the title, authors' names and complete addresses, which must be centered at the top of the first page, and any full-width figures or tables (see the guidelines in Subsection "The First Page" ). Type single-spaced. Start all pages directly under the top margin. See the guidelines later regarding formatting the first page. The manuscript should be printed single-sided and its length should not exceed the maximum page limit described in Section "Length of Submission" . Pages are numbered for initial submission. However, do not number the pages in the camera-ready version. By uncommenting \aclfinalcopy at the top of this document, it will compile to produce an example of the camera-ready formatting; by leaving it commented out, the document will be anonymized for initial submission. When you first create your submission on softconf, please fill in your submitted paper ID where *** appears in the \def\aclpaperid{***} definition at the top. The review process is double-blind, so do not include any author information (names, addresses) when submitting a paper for review.

George Tucker, Andriy Mnih, Chris J. Maddison, John Lawson, and Jascha Sohl-Dickstein. 2017. REBAR: low-variance, unbiased gradient estimates for discrete latent variable models. In Proceedings of NIPS 2017, pages 2624–2633. Claire Cardie Vlad Niculae, André F. T. Martins. 2018. Towards dynamic computation graphs via sparse latent structure. CoRR, abs/1809.00653. Adina Williams, Andrew Drozdov, and Samuel R. Bowman. 2018a. Do latent tree learning models identify meaningful structure in sentences? TACL, 6:253–267. Adina Williams, Nikita Nangia, and Samuel Bowman. 2018b. A broad-coverage challenge corpus for sentence understanding through inference. In Proceedings of NAACL-HLT 2018, Volume 1 (Long Papers), pages 1112–1122. Association for Computational Linguistics. Ronald J. Williams. 1992. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8:229–256.

The Ruler	The NAACL-HLT 2019 style defines a printed ruler which should be presented in the version submitted for review. The ruler is provided in order that reviewers may comment on particular lines in the paper without circumlocution. If you are preparing a document without the provided style files, please arrange for an equivalent ruler to appear on the final output pages. The presence or absence of the ruler should not change the appearance of any other content on the page. The camera ready copy should not contain a ruler. ( users may uncomment the \aclfinalcopy command in the document preamble.) Reviewers: note that the ruler measurements do not align well with lines in the paper – this turns out to be very difficult to do well when the paper contains many figures and equations, and, when done, looks ugly. In most cases one would expect that the approximate location will be adequate, although you can also use fractional references (e.g., the first paragraph on this page ends at mark $108.5$ ).

Sections	Headings: Type and label section and subsection headings in the style shown on the present document. Use numbered sections (Arabic numerals) in order to facilitate cross references. Number subsections with the section number and the subsection number separated by a dot, in Arabic numerals. Do not number subsubsections. Citations: Citations within the text appear in parentheses as  or, if the author's name appears in the text itself, as Gusfield Gusfield:97. Using the provided style, the former is accomplished using \cite and the latter with \shortcite or \newcite. Collapse multiple citations as in  ,  ; this is accomplished with the provided style using commas within the \cite command, e.g., \cite{Gusfield:97,Aho:72}. Append lowercase letters to the year in cases of ambiguities. Treat double authors as in  , but write as in  when more than two authors are involved. Collapse multiple citations as in  ,  . Also refrain from using full citations as sentence constituents.

Electronically-available resources	NAACL-HLT provides this description in 2e (naaclhlt2019.tex) and PDF format (naaclhlt2019.pdf), along with the 2e style file used to format it (naaclhlt2019.sty) and an ACL bibliography style (acl_natbib.bst) and example bibliography (naaclhlt2019.bib). These files are all available at http://naacl2019.org/downloads/ naaclhlt2019-latex.zip. We strongly recommend the use of these style files, which have been appropriately tailored for the NAACL-HLT 2019 proceedings.

Layout	Format manuscripts two columns to a page, in the manner these instructions are formatted. The exact dimensions for a page on A4 paper are: Left and right margins: 2.5 cm Top margin: 2.5 cm Bottom margin: 2.5 cm Column width: 7.7 cm Column height: 24.7 cm Gap between columns: 0.6 cm Papers should not be submitted on any other paper size. If you cannot meet the above requirements about the production of your electronic submission, please contact the publication chairs above as soon as possible.

Fonts	For reasons of uniformity, Adobe's Times Roman font should be used. In 2e this is accomplished by putting \usepackage{times} \usepackage{latexsym} in the preamble. If Times Roman is unavailable, use Computer Modern Roman (2e's default). Note that the latter is about 10% less dense than Adobe's Times Roman font.

The First Page	Center the title, author's name(s) and affiliation(s) across both columns. Do not use footnotes for affiliations. Do not include the paper ID number assigned during the submission process. Use the two-column format only when you begin the abstract. Title: Place the title centered at the top of the first page, in a 15-point bold font. (For a complete guide to font sizes and styles, see Table 1 ) Long titles should be typed on two lines without a blank line intervening. Approximately, put the title at 2.5 cm from the top of the page, followed by a blank line, then the author's names(s), and the affiliation on the following line. Do not use only initials for given names (middle initials are allowed). Do not format surnames in all capitals (e.g., use “Mitchell” not “MITCHELL”). Do not format title and section headings in all capitals as well except for proper names (such as “BLEU”) that are conventionally in all capitals. The affiliation should contain the author's complete address, and if possible, an electronic mail address.

Start the body of the first page 7.5 cm from the top of the page. The title, author names and addresses should be completely identical to those entered to the electronical paper submission website in order to maintain the consistency of author information among all publications of the conference. If they are different, the publication chairs may resolve the difference without consulting with you; so it is in your own interest to double-check that the information is consistent. Abstract: Type the abstract at the beginning of the first column. The width of the abstract text should be smaller than the width of the columns for the text in the body of the paper by about 0.6 cm on each side. Center the word Abstract in a 12 point bold font above the body of the abstract. The abstract should be a concise summary of the general thesis and conclusions of the paper. It should be no longer than 200 words. The abstract text should be in 10 point font. Text: Begin typing the main body of the text immediately after the abstract, observing the two-column format as shown in the present document. Do not include page numbers. Indent: Indent when starting a new paragraph, about 0.4 cm.

Use 11 points for text and subsection headings, 12 points for section headings and 15 points for the title.

Mathematical methods in linguistics, volume 30. Springer Science & Business Media. Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. Glove: Global vectors for word representation. In Proceedings of EMNLP 2014, pages 1532–1543. Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. arXiv preprint arXiv:1802.05365. Alec Radford, Rafal Jozefowicz, and Ilya Sutskever. 2017. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444. Steven J. Rennie, Etienne Marcheret, Youssef Mroueh, Jarret Ross, and Vaibhava Goel. 2017. Self-critical sequence training for image captioning. In Proceedings of CVPR 2017, pages 1179–1195. Sheldon M. Ross.

Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. 2016. Asynchronous methods for deep reinforcement learning. In Proceedings of ICML 2016, pages 1928–1937. Michael Mozer and Sreerupa Das. 1992. A connectionist symbol manipulator that discovers the structure of context-free languages. In Proceedings of NIPS 1992, pages 863–870. Tsendsuren Munkhdalai and Hong Yu. 2017. Neural tree indexers for text understanding. In Proceedings of EACL 2017, volume 1, page 11. NIH Public Access. Nikita Nangia and Samuel R. Bowman. 2018. Listops: A diagnostic dataset for latent tree learning. In Proceedings of NAACL-HLT 2018, Student Research Workshop, pages 92–99. Barbara BH Partee, Alice G ter Meulen, and Robert Wall. 1990.

Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Ng, and Christopher Potts. 2013a. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of EMNLP 2013, pages 1631–1642. Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, and Christopher Potts. 2013b. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of EMNLP 2013, pages 1631–1642. G Sun. 1990. Connectionist pushdownautomata that learn context-free gram-mars. In Proc. IJCNN'90, volume 1, pages 577–580. Kai Sheng Tai, Richard Socher, and Christopher D. Manning. 2015. Improved semantic representations from tree-structured long short-term memory networks. In Proceedings of ACL 2015, Volume 1: Long Papers, pages 1556–1566.

1997. Simulation (2. ed.). Statistical modeling and decision science. Academic Press. Himanshu Sahni, Saurabh Kumar, Farhan Tejani, and Charles L. Isbell. 2017. Learning to compose skills. CoRR, abs/1711.11289. John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal policy optimization algorithms. CoRR, abs/1707.06347. Haoyue Shi, Hao Zhou, Jiaze Chen, and Lei Li. 2018. On tree-based neural sentence modeling. CoRR, abs/1808.09644. Satinder P. Singh. 1992. Transfer of learning by composing solutions of elemental sequential tasks. Machine Learning, 8:323–339. Richard Socher, Cliff Chiung-Yu Lin, Andrew Y. Ng, and Christopher D. Manning. 2011. Parsing natural scenes and natural language with recursive neural networks. In Proceedings of ICML 2011, pages 129–136.

Dani Yogatama, Phil Blunsom, Chris Dyer, Edward Grefenstette, and Wang Ling. 2016. Learning to compose words into sentences with reinforcement learning. CoRR, abs/1611.09100. Matthew D. Zeiler. 2012. ADADELTA: an adaptive learning rate method. CoRR, abs/1212.5701. Xiao-Dan Zhu, Parinaz Sobhani, and Hongyu Guo. 2015. Long short-term memory over recursive structures. In Proceedings of ICML 2015, pages 1604–1612. | where naaclhlt2019 corresponds to a naaclhlt2019.bib file. Appendices Appendices are material that can be read, and include lemmas, formulas, proofs, and tables that are not critical to the reading and understanding of the paper. Appendices should be uploaded as supplementary material when submitting the paper for review. Upon acceptance, the appendices come after the references, as shown here. Use \appendix before any appendix section to switch the section numbering over to letters.

Footnotes	Footnotes: Put footnotes at the bottom of the page and use 9 point font. They may be numbered or referred to by asterisks or other symbols. Footnotes should be separated from the text by a line.

Graphics	Illustrations: Place figures, tables, and photographs in the paper near where they are first discussed, rather than at the end, if possible. Wide illustrations may run across both columns. Color illustrations are discouraged, unless you have verified that they will be understandable when printed in black ink. Captions: Provide a caption for every illustration; number each one sequentially in the form: “Figure 1. Caption of the Figure.” “Table 1. Caption of the Table.” Type the captions of the figures and tables below the body, using 10 point text. Captions should be placed below illustrations. Captions that are one line are centered (see Table 1 ). Captions longer than one line are left-aligned (see Table 2 ). Do not overwrite the default caption sizes. The naaclhlt2019.sty file is compatible with the caption and subcaption packages; do not add optional arguments.

Accessibility	In an effort to accommodate people who are color-blind (as well as those printing to paper), grayscale readability for all accepted papers will be encouraged. Color is not forbidden, but authors should ensure that tables and figures do not rely solely on color to convey critical distinctions. A simple criterion: All curves and points in your figures should be clearly distinguishable without color.

Translation of non-English Terms	It is also advised to supplement non-English characters and terms with appropriate transliterations and/or translations since not all readers understand all such characters and terms. Inline transliteration or translation can be represented in the order of: original-form transliteration “translation”.

Do not include this additional material in the same document as your main paper. Additional material must be submitted as one or more separate files, and must adhere to the same anonymity guidelines as the main paper. The paper must be self-contained: it is optional for reviewers to look at the supplementary material. Papers should not refer, for further detail, to documents, code or data resources that are not available to the reviewers. Refer to Appendix "Appendices" and Appendix "Supplemental Material" for further information. Workshop chairs may have different rules for allowed length and whether supplemental material is welcome. As always, the respective call for papers is the authoritative source.

Acknowledgments	The acknowledgments should go immediately before the references. Do not number the acknowledgments section. Do not include this section when submitting your paper for review. Preparing References: Include your own bib file like this: \bibliographystyle{acl_natbib} \begin{thebibliography}{50}  Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015. A large annotated corpus for learning natural language inference. In Proceedings EMNLP 2015, pages 632–642. Samuel R. Bowman, Jon Gauthier, Abhinav Rastogi, Raghav Gupta, Christopher D. Manning, and Christopher Potts. 2016. A fast unified model for parsing and sentence understanding. In Proceedings of the ACL 2016, Volume 1: Long Papers. Michael B. Chang, Abhishek Gupta, Sergey Levine, and Thomas L. Griffiths. 2018. Automatically composing representation transformations as a means for generalization. CoRR, abs/1807.04640.

C. Lee Giles, Guo-Zheng Sun, Hsing-Hen Chen, Yee-Chun Lee, and Dong Chen. 1989. Higher order recurrent networks and grammatical inference. In Proceedings of NIPS 1989, pages 380–387. Christoph Goller and Andreas Kuchler. 1996. Learning task-dependent distributed representations by backpropagation through structure. Neural Networks, 1:347–352. Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, and David K. Duvenaud. 2017. Backpropagation through the void: Optimizing control variates for black-box gradient estimation. CoRR, abs/1711.00123. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8):1735–1780. Eric Jang, Shixiang Gu, and Ben Poole. 2016. Categorical reparameterization with gumbel-softmax. CoRR, abs/1611.01144.

Armand Joulin and Tomas Mikolov. 2015. Inferring algorithmic patterns with stack-augmented recurrent nets. In Proceedings of NIPS 2015, pages 190–198. Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. CoRR, abs/1412.6980. Diederik P. Kingma and Max Welling. 2013. Auto-encoding variational bayes. CoRR, abs/1312.6114. Phong Le and Willem H. Zuidema. 2015. The forest convolutional network: Compositional distributional semantics with a neural chart and without binarization. In Proceedings of EMNLP 2015, pages 1155–1164. Moshe Looks, Marcello Herreshoff, DeLesley Hutchins, and Peter Norvig. 2017. Deep learning with dynamic computation graphs. arXiv preprint arXiv:1702.02181. Chris J. Maddison, Andriy Mnih, and Yee Whye Teh. 2016.

The concrete distribution: A continuous relaxation of discrete random variables. CoRR, abs/1611.00712. Jean Maillard and Stephen Clark. 2018. Latent tree learning with differentiable parsers: Shift-reduce parsing and chart parsing. CoRR, abs/1806.00840. Jean Maillard, Stephen Clark, and Dani Yogatama. 2017. Jointly learning sentence embeddings and syntax with unsupervised tree-lstms. CoRR, abs/1705.09189. Bryan McCann, James Bradbury, Caiming Xiong, and Richard Socher. 2017. Learned in translation: Contextualized word vectors. In Proceedings of NIPS 2017, pages 6294–6305. Andriy Mnih and Karol Gregor. 2014. Neural variational inference and learning in belief networks. arXiv preprint arXiv:1402.0030.

Credits	This document has been adapted from the instructions for earlier ACL and NAACL proceedings, including those for ACL 2018 by Shay Cohen, Kevin Gimpel, and Wei Lu, NAACL 2018 by Margaret Michell and Stephanie Lukin, 2017/2018 (NA)ACL bibtex suggestions from Jason Eisner, ACL 2017 by Dan Gildea and Min-Yen Kan, NAACL 2017 by Margaret Mitchell, ACL 2012 by Maggie Li and Michael White, those from ACL 2010 by Jing-Shing Chang and Philipp Koehn, those for ACL 2008 by JohannaD. Moore, Simone Teufel, James Allan, and Sadaoki Furui, those for ACL 2005 by Hwee Tou Ng and Kemal Oflazer, those for ACL 2002 by Eugene Charniak and Dekang Lin, and earlier ACL and EACL formats. Those versions were written by several people, including John Chen, Henry S. Thompson and Donald Walker. Additional elements were taken from the formatting instructions of the International Joint Conference on Artificial Intelligence and the Conference on Computer Vision and Pattern Recognition.

Question: How much does this system outperform prior work?
Answer: The system outperforms by 27.7% the LSTM model, 38.5% the RL-SPINN model and 41.6% the Gumbel Tree-LSTM