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SemEval-2019 Task 10: Math Question Answering
Mark Hopkins, Ronan Le Bras, Cristian Petrescu-Prahova, Gabriel Stanovsky, Hannaneh Hajishirzi, Rik Koncel-KedziorskiSemEval • 2019 We report on the SemEval 2019 task on math question answering. We provided a question set derived from Math SAT practice exams, including 2778 training questions and 1082 test questions. For a significant subset of these questions, we also provided SMT-LIB…Variational Pretraining for Semi-supervised Text Classification
Suchin Gururangan, Tam Dang, Dallas Card, Noah A. SmithACL • 2019 We introduce VAMPIRE, a lightweight pretraining framework for effective text classification when data and computing resources are limited. We pretrain a unigram document model as a variational autoencoder on in-domain, unlabeled data and use its internal…Be Consistent! Improving Procedural Text Comprehension using Label Consistency
Xinya Du, Bhavana Dalvi Mishra, Niket Tandon, Antoine Bosselut, Wen-tau Yih, Peter Clark, Claire CardieNAACL-HLT • 2019 Our goal is procedural text comprehension, namely tracking how the properties of entities (e.g., their location) change with time given a procedural text (e.g., a paragraph about photosynthesis, a recipe). This task is challenging as the world is changing…A General Framework for Information Extraction Using Dynamic Span Graphs
Yi Luan, Dave Wadden, Luheng He, Mari Ostendorf, Hannaneh HajishirziNAACL • 2019 We introduce a general framework for several information extraction tasks that share span representations using dynamically constructed span graphs. The graphs are dynamically constructed by selecting the most confident entity spans and linking these nodes…Aligning Vector-spaces with Noisy Supervised Lexicons
Noa Yehezkel, Jacob Goldberger, Yoav GoldbergNAACL • 2019 The problem of learning to translate between two vector spaces given a set of aligned points arises in several application areas of NLP. Current solutions assume that the lexicon which defines the alignment pairs is noise-free. We consider the case where the…Benchmarking Hierarchical Script Knowledge
Yonatan Bisk, Jan Buys, Karl Pichotta, Yejin ChoiNAACL • 2019 Understanding procedural language requires reasoning about both hierarchical and temporal relations between events. For example, “boiling pasta” is a sub-event of “making a pasta dish”, typically happens before “draining pasta,” and requires the use of…Combining Distant and Direct Supervision for Neural Relation Extraction
Iz Beltagy, Kyle Lo, Waleed AmmarNAACL • 2019 In relation extraction with distant supervision, noisy labels make it difficult to train quality models. Previous neural models addressed this problem using an attention mechanism that attends to sentences that are likely to express the relations. We improve…CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, Jonathan BerantNAACL • 2019 When answering a question, people often draw upon their rich world knowledge in addition to the particular context. Recent work has focused primarily on answering questions given some relevant document or context, and required very little general background…DiscoFuse: A Large-Scale Dataset for Discourse-based Sentence Fusion
Mor Geva, Eric Malmi, Idan Szpektor, Jonathan BerantNAACL • 2019 Sentence fusion is the task of joining several independent sentences into a single coherent text. Current datasets for sentence fusion are small and insufficient for training modern neural models. In this paper, we propose a method for automatically…DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, Matt GardnerNAACL-HLT • 2019 Reading comprehension has recently seen rapid progress, with systems matching humans on the most popular datasets for the task. However, a large body of work has highlighted the brittleness of these systems, showing that there is much work left to be done. We…