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      • Coreference Resolution
      • Crowdsourcing and Data Annotation
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      • Executable Semantic Parsing
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      • Olg Blog Posts
        • Semantic Parsing with Semi-Supervised Sequential Autoencoders (Kocisky et al., EMNLP 2016)
        • A causal framework for explaining the predictions of black-box sequence-to-sequence models (Alvarez-Melis et al., 2017)
        • A Factored Neural Network Model for Characterizing Online Discussions in Vector Space (Cheng et al., EMNLP 2017)
        • A Large-Scale Corpus for Conversation Disentanglement (Kummerfeld et al., 2019)
        • A Local Detection Approach for Named Entity Recognition and Mention Detection (Xu et al., 2017)
        • A Novel Workflow for Accurately and Efficiently Crowdsourcing Predicate Senses and Argument Labels (Jiang, et al., Findings of EMNLP 2020)
        • A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings (Yang et al., 2017)
        • A Transition-Based Directed Acyclic Graph Parser for UCCA (Hershcovich et al., 2017)
        • A Two-Stage Parsing Method for Text-Level Discourse Analysis (Wang et al., 2017)
        • Abstractive Document Summarization with a Graph-Based Attentional Neural Model (Tan et al., 2017)
        • Addressing the Data Sparsity Issue in Neural AMR Parsing (Peng et al., EACL 2017)
        • An Analysis of Neural Language Modeling at Multiple Scales (Merity et al., 2018)
        • Approaching Conferences
        • Arc-Standard Spinal Parsing with Stack-LSTMs (Ballesteros et al., 2017)
        • Attention Is All You Need (Vaswani et al., ArXiv 2017)
        • Attention Strategies for Multi-Source Sequence-to-Sequence Learning (Libovicky et al., 2017)
        • Beyond Accuracy: Behavioral Testing of NLP Models with CheckList (Ribeiro, et al., ACL 2020 Best Paper)
        • ChartDialogs: Plotting from Natural Language Instructions (Shao and Nakashole, ACL 2020)
        • Compositional Demographic Word Embeddings (Welch et al., EMNLP 2020)
        • Controlled Crowdsourcing for High-Quality QA-SRL Annotation (Roit, et al., ACL 2020)
        • DeftNN: Addressing Bottlenecks for DNN Execution on GPUs via Synapse Vector Elimination and Near-compute Data Fission (Hill et al., MICRO 2017)
        • Detecting annotation noise in automatically labelled data (Rehbein and Ruppenhofer, ACL 2017)
        • Dynamic Evaluation of Neural Sequence Models (Krause et al., 2017)
        • Dynamic Programming Algorithms for Transition-Based Dependency Parsers (Kuhlmann et al., ACL 2011)
        • Error-repair Dependency Parsing for Ungrammatical Texts (Sakaguchi et al., 2017)
        • Evaluating the Utility of Hand-crafted Features in Sequence Labelling (Minghao Wu et al., 2018)
        • Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time (Huang et al., 2018)
        • Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples (Vidur Joshi et al., 2018)
        • Filling the Blanks (hint: plural noun) for Mad Libs Humor (Hossain et al., EMNLP 2017)
        • Frames: a corpus for adding memory to goal-oriented dialogue systems (El Asri et al., 2017)
        • Getting the Most out of AMR Parsing (Wang and Xue, EMNLP 2017)
        • Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation (Johnson et al., TACL 2017)
        • High-risk learning: acquiring new word vectors from tiny data (Herbelot et al., 2017)
        • Improving Human-Labeled Data through Dynamic Automatic Conflict Resolution (Sun, et al., CoLing 2020)
        • Improving Low Compute Language Modeling with In-Domain Embedding Initialisation (Welch, Mihalcea, and Kummerfeld, EMNLP 2020)
        • In-Order Transition-based Constituent Parsing (Liu et al., 2017)
        • Iterative Feature Mining for Constraint-Based Data Collection to Increase Data Diversity and Model Robustness (Larson, et al., EMNLP 2020)
        • Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme (Zheng et al., 2017)
        • Joint Modeling of Content and Discourse Relations in Dialogues (Qin et al., 2017)
        • Learning Distributed Representations of Texts and Entities from Knowledge Base (Yamada et al., 2017)
        • Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings (He et al., 2017)
        • Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning (Tsvetkov et al., 2016)
        • Learning Whom to Trust with MACE (Hovy et al., NAACL 2013)
        • Leveraging Knowledge Bases in LSTMs for Improving Machine Reading (Yang et al., 2017)
        • Mastering the game of Go without human knowledge (Silver et al., Nature 2017)
        • Mr. Bennet, his coachman, and the Archbishop walk into a bar but only one of them gets recognized: On The Difficulty of Detecting Characters in Literary Texts (Vala et al., 2015)
        • Multimodal Word Distributions (Athiwaratkun and Wilson, 2017)
        • Named Entity Disambiguation for Noisy Text (Eshel et al., CoNLL 2017)
        • Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog (Kottur et al., 2017)
        • Neural Semantic Parsing over Multiple Knowledge-bases (Herzig et al., 2017)
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