--- title: "Papers of 2016 — page 2" description: "Papers of 2016: 331 papers of the Elamur Research library — what each work is about in our own words, its ideas, references and who cites it." canonical: "https://research.elamur.ai/free/en/papers/2016/page/2/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/2016/page/2/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / 2016 # Papers of 2016 — page 2 331 papers · [all years](https://md.research.elamur.ai/free/en/papers/) [← prev](https://md.research.elamur.ai/free/en/papers/2016/) [1](https://md.research.elamur.ai/free/en/papers/2016/) **2** [3](https://md.research.elamur.ai/free/en/papers/2016/page/3/) [4](https://md.research.elamur.ai/free/en/papers/2016/page/4/) [next →](https://md.research.elamur.ai/free/en/papers/2016/page/3/) - [Different Flavors of GUM: Evaluating Genre and Sentence Type Effects on Multilayer Corpus Annotation Quality](https://md.research.elamur.ai/free/en/papers/aclbulk-2016-zeldes-different-flavors-gum-evaluating-genre/) — Amir Zeldes, Dan Simonson · English - [Discourse Parsing with Attention-based Hierarchical Neural Networks](https://md.research.elamur.ai/free/en/papers/li-2016-discourse-parsing-attention-based-hierarchical/) — Qi Li, Tianshi Li, Baobao Chang · Proceedings of the 2016 Conference on Empirical… - [Discourse Relation Sense Classification Using Cross-argument Semantic Similarity Based on Word Embeddings](https://md.research.elamur.ai/free/en/papers/mihaylov-2016-discourse-relation-sense-classification-cross/) — Todor Mihaylov, Anette Frank · Proceedings of the CoNLL-16 shared task - [Discourse Structure and Dialogue Acts in Multiparty Dialogue: the STAC Corpus](https://md.research.elamur.ai/free/en/papers/asher-2016-discourse-structure-dialogue-acts-multiparty/) — Nicholas Asher, Julie Hunter, Mathieu Morey, et al. · English - [Discriminating between Similar Languages and Arabic Dialect Identification: A Report on the Third DSL Shared Task](https://md.research.elamur.ai/free/en/papers/malmasi-2016-discriminating-between-similar-languages/) — Shervin Malmasi, Marcos Zampieri, Nikola Ljubešić, et al. · VarDial 2016 - [Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models](https://md.research.elamur.ai/free/en/papers/vijayakumar-2016-diverse-beam-search-decoding-diverse/) — Ashwin K Vijayakumar, Michael Cogswell, Ramprasath R. Selvaraju, et al. · arXiv:1610.02424 - [Does String-Based Neural MT Learn Source Syntax?](https://md.research.elamur.ai/free/en/papers/shi-2016-does-string-based-neural-mt/) — Xing Shi, Inkit Padhi, Kevin Knight · Proceedings of the 2016 Conference on Empirical… - [DTED: Evaluation of Machine Translation Structure Using Dependency Parsing and Tree Edit Distance](https://md.research.elamur.ai/free/en/papers/mccaffery-2016-dted-evaluation-machine-translation-structure/) — Martin McCaffery, Mark-Jan Nederhof · Machine translation - [Dynamic Coattention Networks For Question Answering](https://md.research.elamur.ai/free/en/papers/xiong-2016-dynamic-coattention-networks-question-answering/) — Caiming Xiong, Victor Zhong, Richard Socher · arXiv:1611.01604 - [Edinburgh Neural Machine Translation Systems for WMT 16](https://md.research.elamur.ai/free/en/papers/sennrich-2016-edinburgh-neural-machine-translation-systems/) — Rico Sennrich, Barry Haddow, Alexandra Birch · Proceedings of the First Conference on Machine… - [Effective LSTMs for Target-Dependent Sentiment Classification](https://md.research.elamur.ai/free/en/papers/tang-2016-effective-lstms-target-dependent-sentiment/) — Duyu Tang, Bing Qin, Xiaocheng Feng, et al. · Proceedings of COLING 2016, the 26th… - [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://md.research.elamur.ai/free/en/papers/malkov-2016-efficient-robust-approximate-nearest-neighbor/) — Yu. A. Malkov, D. A. Yashunin · arXiv:1603.09320 - [Emergent: a novel data-set for stance classification](https://md.research.elamur.ai/free/en/papers/ferreira-2016-emergent-novel-data-set-stance/) — William Ferreira, Andreas Vlachos · Proceedings of the 2016 Conference of the North… - [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://md.research.elamur.ai/free/en/papers/miwa-2016-end-end-relation-extraction-lstms/) — Makoto Miwa, Mohit Bansal · Proceedings of the 54th Annual Meeting of the… - [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://md.research.elamur.ai/free/en/papers/ma-2016-end-end-sequence-labeling-bi/) — Xuezhe Ma, Eduard Hovy · arXiv:1603.01354 - [English Resource Semantics](https://md.research.elamur.ai/free/en/papers/aclbulk-2016-flickinger-english-resource-semantics/) — Dan Flickinger, Emily M. Bender, Woodley Packard · English - [Enhanced English Universal Dependencies: An Improved Representation for Natural Language Understanding Tasks](https://md.research.elamur.ai/free/en/papers/ud-2016-schuster-enhanced-english-universal-dependencies-improved/) — Sebastian Schuster, Christopher D. Manning · English - [Enhancing PTB Universal Dependencies for Grammar-Based Surface Realization](https://md.research.elamur.ai/free/en/papers/king-2016-enhancing-ptb-universal-dependencies-grammar/) — David L. King, Michael White · Machine translation - [Equality of Opportunity in Supervised Learning](https://md.research.elamur.ai/free/en/papers/hardt-2016-equality-opportunity-supervised-learning/) — Moritz Hardt, Eric Price, Nathan Srebro · arXiv:1610.02413 - [Evaluating Inter-Annotator Agreement on Historical Spelling Normalization](https://md.research.elamur.ai/free/en/papers/aclbulk-2016-bollmann-evaluating-inter-annotator-agreement-historical/) — Marcel Bollmann, Stefanie Dipper, Florian Petran · English - [Event Detection and Co-reference with Minimal Supervision](https://md.research.elamur.ai/free/en/papers/peng-2016-event-detection-co-reference-minimal/) — Haoruo Peng, Yangqiu Song, Dan Roth · Proceedings of the 2016 Conference on Empirical… - [Experiments on bridging across languages and genres](https://md.research.elamur.ai/free/en/papers/grishina-2016-experiments-bridging-across-languages-genres/) — Yulia Grishina · Proceedings of the Workshop on Coreference… - [Exploiting Arabic Diacritization for High Quality Automatic Annotation](https://md.research.elamur.ai/free/en/papers/lingbulk-2016-habash-exploiting-arabic-diacritization-high-quality/) — Nizar Habash, Anas Shahrour, Muhamed Al-Khalil · Semitic languages - [Exploiting Source-side Monolingual Data in Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/zhang-2016-exploiting-source-side-monolingual-data/) — Jiajun Zhang, Chengqing Zong · Proceedings of the 2016 Conference on Empirical… - [Exploring the Realization of Irony in Twitter Data](https://md.research.elamur.ai/free/en/papers/hee-2016-exploring-realization-irony-twitter-data/) — Cynthia Van Hee, Els Lefever, Véronique Hoste · Proceedings of the Tenth International… - [Exploring the steps of Verb Phrase Ellipsis](https://md.research.elamur.ai/free/en/papers/aclbulk-2016-liu-exploring-steps-verb-phrase-ellipsis/) — Zhengzhong Liu, Edgar Gonzàlez Pellicer, Daniel Gillick · English - [Farasa: A Fast and Furious Segmenter for Arabic](https://md.research.elamur.ai/free/en/papers/aclbulk-2016-abdelali-farasa-fast-furious-segmenter-arabic/) — Ahmed Abdelali, Kareem Darwish, Nadir Durrani, et al. · Semitic languages - [FastText.zip: Compressing text classification models](https://md.research.elamur.ai/free/en/papers/joulin-2016-fasttext-zip-compressing-text-classification/) — Armand Joulin, Edouard Grave, Piotr Bojanowski, et al. · arXiv:1612.03651 - [Findings of the 2016 Conference on Machine Translation](https://md.research.elamur.ai/free/en/papers/bojar-2016-findings-2016-conference-machine-translation/) — Ondřej Bojar, Rajen Chatterjee, Christian Federmann, et al. · Proceedings of the First Conference on Machine… - [Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks](https://md.research.elamur.ai/free/en/papers/adi-2016-fine-grained-analysis-sentence-embeddings/) — Yossi Adi, Einat Kermany, Yonatan Belinkov, et al. · arXiv:1608.04207 - [Fracking Sarcasm using Neural Network](https://md.research.elamur.ai/free/en/papers/ghosh-2016-fracking-sarcasm-neural-network/) — Aniruddha Ghosh, Tony Veale · Proceedings of the 7th Workshop on… - [From distributions to labels: A lexical proficiency analysis using learner corpora](https://md.research.elamur.ai/free/en/papers/alfter-2016-distributions-labels-lexical-proficiency-analysis/) — David Alfter, Yuri Bizzoni, Anders Agebjörn, et al. · Proceedings of the joint workshop on NLP for… - [Generating English from Abstract Meaning Representations](https://md.research.elamur.ai/free/en/papers/pourdamghani-2016-generating-english-abstract-meaning-representations/) — Nima Pourdamghani, Kevin Knight, Ulf Hermjakob · Machine translation - [Generating Natural Questions About an Image](https://md.research.elamur.ai/free/en/papers/mostafazadeh-2016-generating-natural-questions-about-image/) — Nasrin Mostafazadeh, Ishan Misra, Jacob Devlin, et al. · Proceedings of the 54th Annual Meeting of the… - [Generating Sentences from a Continuous Space](https://md.research.elamur.ai/free/en/papers/bowman-2016-generating-sentences-continuous-space/) — Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, et al. · Proceedings of the 20th SIGNLL Conference on… - [Generation from Abstract Meaning Representation using Tree Transducers](https://md.research.elamur.ai/free/en/papers/flanigan-2016-generation-abstract-meaning-representation-tree/) — Jeffrey Flanigan, Chris Dyer, Noah A. Smith, et al. · Machine translation - [Globally Coherent Text Generation with Neural Checklist Models](https://md.research.elamur.ai/free/en/papers/kiddon-2016-globally-coherent-text-generation-neural/) — Chloé Kiddon, Luke Zettlemoyer, Yejin Choi · Proceedings of the 2016 Conference on Empirical… - [Globally Normalized Transition-Based Neural Networks](https://md.research.elamur.ai/free/en/papers/andor-2016-globally-normalized-transition-based-neural/) — Daniel Andor, Chris Alberti, David Weiss, et al. · Proceedings of the 54th Annual Meeting of the… - [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://md.research.elamur.ai/free/en/papers/selvaraju-2016-grad-cam-visual-explanations-deep/) — Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, et al. · arXiv:1610.02391 - [Habakkuk 2:5a: Denouncing ‘wine’ or ‘wealth’? Contextual readings of the Masoretic text and 1QpHab](https://md.research.elamur.ai/free/en/papers/prinsloo-2016-habakkuk-2-5a-denouncing-wine/) — Gert Prinsloo · HTS Teologiese Studies/Theological Studies 72 - [Harnessing Cognitive Features for Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/mishra-2016-harnessing-cognitive-features-sarcasm-detection/) — Abhijit Mishra, Diptesh Kanojia, Seema Nagar, et al. · Proceedings of the 54th Annual Meeting of the… - [Harnessing Deep Neural Networks with Logic Rules](https://md.research.elamur.ai/free/en/papers/hu-2016-harnessing-deep-neural-networks-logic/) — Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, et al. · Proceedings of the 54th Annual Meeting of the… - [Harnessing Sequence Labeling for Sarcasm Detection in Dialogue from TV Series ‘Friends'](https://md.research.elamur.ai/free/en/papers/joshi-2016-harnessing-sequence-labeling-sarcasm-detection/) — Aditya Joshi, Vaibhav Tripathi, Pushpak Bhattacharyya, et al. · Proceedings of the 20th SIGNLL Conference on… - [Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter](https://md.research.elamur.ai/free/en/papers/waseem-2016-hateful-symbols-or-hateful-people/) — Zeerak Waseem, Dirk Hovy · Proceedings of the NAACL Student Research… - [Hierarchical Attention Networks for Document Classification](https://md.research.elamur.ai/free/en/papers/yang-2016-hierarchical-attention-networks-document-classification/) — Zichao Yang, Diyi Yang, Chris Dyer, et al. · Proceedings of the 2016 Conference of the North… - [How Challenging is Sarcasm versus Irony Classification?: A Study With a Dataset from English Literature](https://md.research.elamur.ai/free/en/papers/joshi-2016-how-challenging-is-sarcasm-versus/) — Aditya Joshi, Vaibhav Tripathi, Pushpak Bhattacharyya, et al. · English - [How Do Cultural Differences Impact the Quality of Sarcasm Annotation?: A Case Study of Indian Annotators and American Text](https://md.research.elamur.ai/free/en/papers/joshi-2016-how-do-cultural-differences-impact/) — Aditya Joshi, Pushpak Bhattacharyya, Mark Carman, et al. · Proceedings of the 10th SIGHUM Workshop on… - [HUME: Human UCCA-Based Evaluation of Machine Translation](https://md.research.elamur.ai/free/en/papers/birch-2016-hume-human-ucca-based-evaluation/) — Alexandra Birch, Omri Abend, Ondřej Bojar, et al. · Machine translation - [Identification of Parallel Passages Across a Large Hebrew/Aramaic Corpus](https://md.research.elamur.ai/free/en/papers/hbo-2016-shmidman-identification-parallel-passages-across-large/) — Avi Shmidman, Moshe Koppel, Ely Porat · Ancient Hebrew - [Idiom Token Classification using Sentential Distributed Semantics](https://md.research.elamur.ai/free/en/papers/salton-2016-idiom-token-classification-sentential-distributed/) — Giancarlo Salton, Robert Ross, John Kelleher · Proceedings of the 54th Annual Meeting of the… - [Implicit Discourse Relation Detection via a Deep Architecture with Gated Relevance Network](https://md.research.elamur.ai/free/en/papers/chen-2016-implicit-discourse-relation-detection-deep/) — Jifan Chen, Qi Zhang, Pengfei Liu, et al. · Proceedings of the 54th Annual Meeting of the… - [Implicit Discourse Relation Recognition with Context-aware Character-enhanced Embeddings](https://md.research.elamur.ai/free/en/papers/qin-2016-implicit-discourse-relation-recognition-context/) — Lianhui Qin, Zhisong Zhang, Hai Zhao · Proceedings of COLING 2016, the 26th… - [Improved Techniques for Training GANs](https://md.research.elamur.ai/free/en/papers/salimans-2016-improved-techniques-training-gans/) — Tim Salimans, Ian Goodfellow, Wojciech Zaremba, et al. · arXiv:1606.03498 - [Improving Coreference Resolution by Learning Entity-Level Distributed Representations](https://md.research.elamur.ai/free/en/papers/clark-2016-improving-coreference-resolution-learning-entity/) — Kevin Clark, Christopher D. Manning · Proceedings of the 54th Annual Meeting of the… - [Improving event prediction by representing script participants](https://md.research.elamur.ai/free/en/papers/ahrendt-2016-improving-event-prediction-representing-script/) — Simon Ahrendt, Vera Demberg · Proceedings of the 2016 Conference of the North… - [Improving historical spelling normalization with bi-directional LSTMs and multi-task learning](https://md.research.elamur.ai/free/en/papers/aclbulk-2016-bollmann-improving-historical-spelling-normalization-bi/) — Marcel Bollmann, Anders Søgaard · English - [Improving Hypernymy Detection with an Integrated Path-based and Distributional Method](https://md.research.elamur.ai/free/en/papers/shwartz-2016-improving-hypernymy-detection-integrated-path/) — Vered Shwartz, Yoav Goldberg, Ido Dagan · Proceedings of the 54th Annual Meeting of the… - [IMS HotCoref DE: A Data-driven Co-reference Resolver for German](https://md.research.elamur.ai/free/en/papers/roesiger-2016-ims-hotcoref-de-data-driven/) — Ina Roesiger, Jonas Kuhn · Proceedings of the Tenth International… - [Incorporating Copying Mechanism in Sequence-to-Sequence Learning](https://md.research.elamur.ai/free/en/papers/gu-2016-incorporating-copying-mechanism-sequence-sequence/) — Jiatao Gu, Zhengdong Lu, Hang Li, et al. · arXiv:1603.06393 - [Incorporating Discrete Translation Lexicons into Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/arthur-2016-incorporating-discrete-translation-lexicons-into/) — Philip Arthur, Graham Neubig, Satoshi Nakamura · Proceedings of the 2016 Conference on Empirical… - [Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora](https://md.research.elamur.ai/free/en/papers/hamilton-2016-inducing-domain-specific-sentiment-lexicons/) — William L. Hamilton, Kevin Clark, Jure Leskovec, et al. · Proceedings of the 2016 Conference on Empirical… - [Integer Linear Programming for Discourse Parsing](https://md.research.elamur.ai/free/en/papers/perret-2016-integer-linear-programming-discourse-parsing/) — Jérémy Perret, Stergos Afantenos, Nicholas Asher, et al. · Proceedings of the 2016 Conference of the North… - [Interactive Tools and Tasks for the Hebrew Bible: From Language Learning to Textual Criticism](https://md.research.elamur.ai/free/en/papers/hbo-2016-winther-nielsen-interactive-tools-tasks-hebrew-bible/) — Nicolai Winther-Nielsen · Ancient Hebrew - [International translation in the Grammatical Framework (GF)](https://md.research.elamur.ai/free/en/papers/ranta-2016-international-translation-grammatical-framework-gf/) — Aarne Ranta, Kasimir Angelov, Thomas Hallgren, et al. · Machine translation - [Interpreting Neural Networks to Improve Politeness Comprehension](https://md.research.elamur.ai/free/en/papers/aubakirova-2016-interpreting-neural-networks-improve-politeness/) — Malika Aubakirova, Mohit Bansal · Proceedings of the 2016 Conference on Empirical… - [Intra-Sentential Subject Zero Anaphora Resolution using Multi-Column Convolutional Neural Network](https://md.research.elamur.ai/free/en/papers/iida-2016-intra-sentential-subject-zero-anaphora/) — Ryu Iida, Kentaro Torisawa, Jong-Hoon Oh, et al. · Proceedings of the 2016 Conference on Empirical… - [Is Neural Machine Translation Ready for Deployment? A Case Study on 30 Translation Directions](https://md.research.elamur.ai/free/en/papers/dowmunt-2016-is-neural-machine-translation-ready/) — Marcin Junczys-Dowmunt, Tomasz Dwojak, Hieu Hoang · Proceedings of the 13th International… - [Is the adjective distinct from the noun as a grammatical category in biblical Hebrew?](https://md.research.elamur.ai/free/en/papers/hbo-2016-miller-naude-adjective-distinct-noun-grammatical-category/) — Cynthia L. Miller-Naudé, Jacobus A. Naudé · Ancient Hebrew - [Joint Event Extraction via Recurrent Neural Networks](https://md.research.elamur.ai/free/en/papers/nguyen-2016-joint-event-extraction-recurrent-neural/) — Thien Huu Nguyen, Kyunghyun Cho, Ralph Grishman · Proceedings of the 2016 Conference of the North… - [Joint Extraction of Events and Entities within a Document Context](https://md.research.elamur.ai/free/en/papers/yang-2016-joint-extraction-events-entities-within/) — Bishan Yang, Tom M. Mitchell · Proceedings of the 2016 Conference of the North… - [Joint Inference for Event Coreference Resolution](https://md.research.elamur.ai/free/en/papers/lu-2016-joint-inference-event-coreference-resolution/) — Jing Lu, Deepak Venugopal, Vibhav Gogate, et al. · Proceedings of COLING 2016, the 26th… - [Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation](https://md.research.elamur.ai/free/en/papers/yamada-2016-joint-learning-embedding-words-entities/) — Ikuya Yamada, Hiroyuki Shindo, Hideaki Takeda, et al. · Proceedings of the 20th SIGNLL Conference on… - [Joint Transition-based Dependency Parsing and Disfluency Detection for Automatic Speech Recognition Texts](https://md.research.elamur.ai/free/en/papers/aclbulk-2016-yoshikawa-joint-transition-based-dependency-parsing/) — Masashi Yoshikawa, Hiroyuki Shindo, Yuji Matsumoto · English - [Korean FrameNet Expansion Based on Projection of Japanese FrameNet](https://md.research.elamur.ai/free/en/papers/kim-2016-korean-framenet-expansion-based-projection/) — Jeong-uk Kim, Younggyun Hahm, Key-Sun Choi · Proceedings of COLING 2016, the 26th… - [Language Modeling with Gated Convolutional Networks](https://md.research.elamur.ai/free/en/papers/dauphin-2016-language-modeling-gated-convolutional-networks/) — Yann N. Dauphin, Angela Fan, Michael Auli, et al. · arXiv:1612.08083 - [Large-Scale Machine Translation between Arabic and Hebrew: Available Corpora and Initial Results](https://md.research.elamur.ai/free/en/papers/langstream-2016-belinkov-large-scale-machine-translation-between/) — Yonatan Belinkov, James Glass · Modern Hebrew - [Layer Normalization](https://md.research.elamur.ai/free/en/papers/ba-2016-layer-normalization/) — Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton · arXiv:1607.06450 - [Learning a POS tagger for AAVE-like language](https://md.research.elamur.ai/free/en/papers/jorgensen-2016-learning-pos-tagger-aave-like/) — Anna Jørgensen, Dirk Hovy, Anders Søgaard · English - [Learning Connective-based Word Representations for Implicit Discourse Relation Identification](https://md.research.elamur.ai/free/en/papers/braud-2016-learning-connective-based-word-representations/) — Chloé Braud, Pascal Denis · Proceedings of the 2016 Conference on Empirical… - [Learning Distributed Representations of Sentences from Unlabelled Data](https://md.research.elamur.ai/free/en/papers/hill-2016-learning-distributed-representations-sentences-unlabelled/) — Felix Hill, Kyunghyun Cho, Anna Korhonen · Proceedings of the 2016 Conference of the North… - [Learning Global Features for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/wiseman-2016-learning-global-features-coreference-resolution/) — Sam Wiseman, Alexander M. Rush, Stuart M. Shieber · Proceedings of the 2016 Conference of the North… - [Learning Natural Language Inference with LSTM](https://md.research.elamur.ai/free/en/papers/wang-2016-learning-natural-language-inference-lstm/) — Shuohang Wang, Jing Jiang · Proceedings of the 2016 Conference of the North… - [Learning principled bilingual mappings of word embeddings while preserving monolingual invariance](https://md.research.elamur.ai/free/en/papers/artetxe-2016-learning-principled-bilingual-mappings-word/) — Mikel Artetxe, Gorka Labaka, Eneko Agirre · Proceedings of the 2016 Conference on Empirical… - [Learning to Compose Neural Networks for Question Answering](https://md.research.elamur.ai/free/en/papers/andreas-2016-learning-compose-neural-networks-question/) — Jacob Andreas, Marcus Rohrbach, Trevor Darrell, et al. · Proceedings of the 2016 Conference of the North… - [Learning to Understand Phrases by Embedding the Dictionary](https://md.research.elamur.ai/free/en/papers/hill-2016-learning-understand-phrases-embedding-dictionary/) — Felix Hill, Kyunghyun Cho, Anna Korhonen, et al. · Transactions of the Association for… - [Learning To Use Formulas To Solve Simple Arithmetic Problems](https://md.research.elamur.ai/free/en/papers/mitra-2016-learning-use-formulas-solve-simple/) — Arindam Mitra, Chitta Baral · Mova's core list - [Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints](https://md.research.elamur.ai/free/en/papers/durrett-2016-learning-based-single-document-summarization/) — Greg Durrett, Taylor Berg-Kirkpatrick, Dan Klein · Proceedings of the 54th Annual Meeting of the… - [Left Dislocation in North-Eastern Neo-Aramaic dialects](https://md.research.elamur.ai/free/en/papers/geoffrey-2016-left-dislocation-north-eastern-neo/) — Geoffrey Khan · Stellenbosch Papers in Linguistics Plus 50 - [Leveraging FrameNet to Improve Automatic Event Detection](https://md.research.elamur.ai/free/en/papers/liu-2016-leveraging-framenet-improve-automatic-event/) — Shulin Liu, Yubo Chen, Shizhu He, et al. · Proceedings of the 54th Annual Meeting of the… - [Linguistic Input Features Improve Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/sennrich-2016-linguistic-input-features-improve-neural/) — Rico Sennrich, Barry Haddow · Proceedings of the First Conference on Machine… - [Log-linear Combinations of Monolingual and Bilingual Neural Machine Translation Models for Automatic Post-Editing](https://md.research.elamur.ai/free/en/papers/dowmunt-2016-log-linear-combinations-monolingual-bilingual/) — Marcin Junczys-Dowmunt, Roman Grundkiewicz · Proceedings of the First Conference on Machine… - [Long Short-Term Memory-Networks for Machine Reading](https://md.research.elamur.ai/free/en/papers/cheng-2016-long-short-term-memory-networks/) — Jianpeng Cheng, Li Dong, Mirella Lapata · Proceedings of the 2016 Conference on Empirical… - [LORELEI Language Packs: Data, Tools, and Resources for Technology Development in Low Resource Languages](https://md.research.elamur.ai/free/en/papers/strassel-2016-lorelei-language-packs-data-tools/) — Stephanie Strassel, Jennifer Tracey · Proceedings of the Tenth International… - [LSTM CCG Parsing](https://md.research.elamur.ai/free/en/papers/aclbulk-2016-lewis-lstm-ccg-parsing/) — Mike Lewis, Kenton Lee, Luke Zettlemoyer · English - [Machine Comprehension Using Match-LSTM and Answer Pointer](https://md.research.elamur.ai/free/en/papers/wang-2016-machine-comprehension-using-match-lstm/) — Shuohang Wang, Jing Jiang · arXiv:1608.07905 - [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://md.research.elamur.ai/free/en/papers/goyal-2016-making-v-vqa-matter-elevating/) — Yash Goyal, Tejas Khot, Douglas Summers-Stay, et al. · arXiv:1612.00837 - [Man is to Computer Programmer as Woman is to Homemaker? 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