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Viewing 26 papers from 2019 in AllenNLP
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    • EMNLP 2019
      Ben Bogin, Matt Gardner, Jonathan Berant
      State-of-the-art semantic parsers rely on auto-regressive decoding, emitting one symbol at a time. When tested against complex databases that are unobserved at training time (zero-shot), the parser often struggles to select the correct set of database constants in the new database, due to the local…  (More)
    • EMNLP 2019
      Jesse Dodge, Suchin Gururangan, Dallas Card, Roy Schwartz, Noah A. Smith
      Research in natural language processing proceeds, in part, by demonstrating that new models achieve superior performance (e.g., accuracy) on held-out test data, compared to previous results. In this paper, we demonstrate that test-set performance scores alone are insufficient for drawing accurate…  (More)
    • EMNLP 2019
      Jesse Dodge, Roy Schwartz, Hao Peng, Noah A. Smith
      Neural models for NLP typically use large numbers of parameters to reach state-of-the-art performance, which can lead to excessive memory usage and increased runtime. We present a structure learning method for learning sparse, parameter-efficient NLP models. Our method applies group lasso to…  (More)
    • EMNLP 2019
      Pradeep Dasigi, Nelson F. Liu, Ana Marasovi'c, Noah A. Smith, Matt Gardner
      Machine comprehension of texts longer than a single sentence often requires coreference resolution. However, most current reading comprehension benchmarks do not contain complex coreferential phenomena and hence fail to evaluate the ability of models to resolve coreference. We present a new…  (More)
    • EMNLP 2019
      Matthew E. Peters, Mark Neumann, Robert L. Logan, Roy Schwartz, Vidur Joshi, Sameer Singh, and Noah A. Smith
      Contextual word representations, typically trained on unstructured, unlabeled text, do not contain any explicit grounding to real world entities and are often unable to remember facts about those entities. We propose a general method to embed multiple knowledge bases (KBs) into large scale models…  (More)
    • EMNLP 2019
      Hao Peng, Roy Schwartz, Noah A. Smith
      We present PaLM, a hybrid parser and neural language model. Building on an RNN language model, PaLM adds an attention layer over text spans in the left context. An unsupervised constituency parser can be derived from its attention weights, using a greedy decoding algorithm. We evaluate PaLM on…  (More)
    • EMNLP 2019
      Xiujun Li, Chunyuan Li, Qiaolin Xia, Yonatan Bisk, Asli Celikyilmaz, Jianfeng Gao, Noah Smith, Yejin Choi
      Core to the vision-and-language navigation (VLN) challenge is building robust instruction representations and action decoding schemes, which can generalize well to previously unseen instructions and environments. In this paper, we report two simple but highly effective methods to address these…  (More)
    • EMNLP 2019
      Sachin Kumar, Shuly Wintner, Noah A. Smith, Yulia Tsvetkov
      Despite impressive performance on many text classification tasks, deep neural networks tend to learn frequent superficial patterns that are specific to the training data and do not always generalize well. In this work, we observe this limitation with respect to the task of native language…  (More)
    • ACL 2019
      Christine Betts, Joanna Power, Waleed Ammar
      We introduce GrapAL (Graph database of Academic Literature), a versatile tool for exploring and investigating a knowledge base of scientific literature, that was semi-automatically constructed using NLP methods. GrapAL satisfies a variety of use cases and information needs requested by researchers…  (More)
    • ACL 2019
      Maarten Sap, Dallas Card, Saadia Gabriel, Yejin Choi, Noah A. Smith
      We investigate how annotators’ insensitivity to differences in dialect can lead to racial bias in automatic hate speech detection models, potentially amplifying harm against minority populations. We first uncover unexpected correlations between surface markers of African American English (AAE) and…  (More)
    • ACL 2019
      Sewon Min, Eric Wallace, Sameer Singh, Matt Gardner, Hannaneh Hajishirzi, Luke Zettlemoyer
      Multi-hop reading comprehension (RC) questions are challenging because they require reading and reasoning over multiple paragraphs. We argue that it can be difficult to construct large multi-hop RC datasets. For example, even highly compositional questions can be answered with a single hop if they…  (More)
    • ACL • RepL4NLP 2019
      Matthew E. Peters, Sebastian Ruder, Noah A. Smith
      While most previous work has focused on different pretraining objectives and architectures for transfer learning, we ask how to best adapt the pretrained model to a given target task. We focus on the two most common forms of adaptation, feature extraction (where the pretrained weights are frozen…  (More)
    • ACL • BioNLP Workshop 2019
      Mark Neumann, Daniel King, Iz Beltagy, Waleed Ammar
      Despite recent advances in natural language processing, many statistical models for processing text perform extremely poorly under domain shift. Processing biomedical and clinical text is a critically important application area of natural language processing, for which there are few robust…  (More)
    • ACL 2019
      Ben Bogin, Jonathan Berant, Matt Gardner
      Research on parsing language to SQL has largely ignored the structure of the database (DB) schema, either because the DB was very simple, or because it was observed at both training and test time. In SPIDER, a recently-released text-to-SQL dataset, new and complex DBs are given at test time, and so…  (More)
    • ACL 2019
      Gabriel Stanovsky, Noah A. Smith, Luke Zettlemoyer
      We present the first challenge set and evaluation protocol for the analysis of gender bias in machine translation (MT). Our approach uses two recent coreference resolution datasets composed of English sentences which cast participants into non-stereotypical gender roles (e.g., "The doctor asked the…  (More)
    • ACL 2019
      Robert L. Logan IV, Nelson F. Liu, Matthew E. Peters, Matt Gardner, Sameer Singh
      Modeling human language requires the ability to not only generate fluent text but also encode factual knowledge. However, traditional language models are only capable of remembering facts seen at training time, and often have difficulty recalling them. To address this, we introduce the knowledge…  (More)
    • ACL 2019
      Elizabeth Clark, Asli Çelikyilmaz, Noah A. Smith
      For evaluating machine-generated texts, automatic methods hold the promise of avoiding collection of human judgments, which can be expensive and time-consuming. The most common automatic metrics, like BLEU and ROUGE, depend on exact word matching, an inflexible approach for measuring semantic…  (More)
    • ACL 2019
      Sofia Serrano, Noah A. Smith
      Attention mechanisms have recently boosted performance on a range of NLP tasks. Because attention layers explicitly weight input components' representations, it is also often assumed that attention can be used to identify information that models found important (e.g., specific contextualized word…  (More)
    • ACL 2019
      Suchin Gururangan, Tam Dang, Dallas Card, Noah A. Smith
      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 states as features in a downstream…  (More)
    • NAACL-HLT 2019
      Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, Matt Gardner
      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 introduce a new English reading…  (More)
    • NAACL 2019
      Pradeep Dasigi, Matt Gardner, Shikhar Murty, Luke Zettlemoyer, Ed Hovy
      Training semantic parsers from question-answer pairs typically involves searching over an exponentially large space of logical forms, and an unguided search can easily be misled by spurious logical forms that coincidentally evaluate to the correct answer. We propose a novel iterative training…  (More)
    • NAACL 2019
      Nelson F. Liu, Roy Schwartz, Noah Smith
      Several datasets have recently been constructed to expose brittleness in models trained on existing benchmarks. While model performance on these challenge datasets is significantly lower compared to the original benchmark, it is unclear what particular weaknesses they reveal. For example, a…  (More)
    • NAACL 2019
      Phoebe Mulcaire, Jungo Kasai, Noah A. Smith
      We introduce a method to produce multilingual contextual word representations by training a single language model on text from multiple languages. Our method combines the advantages of contextual word representations with those of multilingual representation learning. We produce language models…  (More)
    • NAACL 2019
      Nelson F. Liu, Matt Gardner, Yonatan Belinkov, Matthew Peters, Noah A. Smith
      Contextual word representations derived from large-scale neural language models are successful across a diverse set of NLP tasks, suggesting that they encode useful and transferable features of language. To shed light on the linguistic knowledge they capture, we study the representations produced…  (More)
    • AAAI 2019
      Maarten Sap, Ronan LeBras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith, Yejin Choi
      We present ATOMIC, an atlas of everyday commonsense reasoning, organized through 877k textual descriptions of inferential knowledge. Compared to existing resources that center around taxonomic knowledge, ATOMIC focuses on inferential knowledge organized as typed if-then relations with variables (e…  (More)
    • AAAI 2019
      Oyvind Tafjord, Peter Clark, Matt Gardner, Wen-tau Yih, Ashish Sabharwal
      Many natural language questions require recognizing and reasoning with qualitative relationships (e.g., in science, economics, and medicine), but are challenging to answer with corpus-based methods. Qualitative modeling provides tools that support such reasoning, but the semantic parsing task of…  (More)